Microsoft Copilot vs ChatGPT vs Claude vs Gemini vs DeepSeek: Full Guide, Report & Comparison of Core Features, Use-Case Strengths, Multimodality, Context Limits, Pricing Tie, and more

The most popular AI tools today—Microsoft Copilot, ChatGPT, Claude, Gemini and DeepSeek—are lined up side-by-side in this guide.

Copilot lives inside Microsoft apps to finish code and office docs; ChatGPT is the all-round helper with plug-ins; Claude reads very long files while keeping a safe tone; Gemini adds pictures and voice and checks Google live; DeepSeek gives GPT-4-level answers for a fraction of the price.

Key differences: Claude remembers the most text, only Gemini accepts images + audio, and only DeepSeek lets you download the model. Tests show GPT-4 and Gemini Ultra most accurate, Claude best at math, and DeepSeek quickest while nearly as good.

Prices run from free tiers to $20 pro plans, with Microsoft 365 Copilot at $30 per user.

INDEX

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1. Introduction

This in-depth report walks you through every topic listed below, giving you a clear, detailed comparison of today’s five most popular generative-AI chatbots.

• What they’re good at.– Coding: Copilot slips suggestions straight into VS Code; GPT-4 in ChatGPT handles full-function generation; Claude’s 100 k-token window digests whole repos; Gemini can reason over screenshots; DeepSeek Coder delivers GPT-4-level quality for free.– Writing: ChatGPT is the all-round creative; Copilot drafts inside Word or Outlook and cites your docs; Claude churns out long, well-reasoned reports; Gemini writes in Docs and can start from an image; DeepSeek offers solid, multilingual drafts at zero cost.– Search & live facts: Bing-branded Copilot and Gemini pull real-time web results with footnotes; Claude Pro and ChatGPT Plus can browse on demand; DeepSeek relies on its 2024 training data.– Data work: ChatGPT Plus runs Python to plot your CSV; Excel and Power BI Copilot build charts automatically; Claude now executes JavaScript on uploaded files; Gemini in Sheets fills formulas from plain language.

• Core specs in a sentence.  GPT-4(o) and Gemini Ultra are dense trillion-class models; DeepSeek is a 671 B-parameter mixture-of-experts (only 37 B “awake” per token); Claude trades raw size for a record 100 k context; all but DeepSeek accept images, and only Gemini is text-image-audio by design.

• Price snapshot.  ChatGPT Plus and Claude Pro are $20/month; GitHub Copilot Pro is $10, while Microsoft 365 Copilot costs $30 per user; Gemini Advanced rides in Google One AI Premium at $19.99; DeepSeek’s chat is free and its API costs about $0.001 per 1 000 tokens.

• Performance shorthand.  On public tests: Gemini ~85 % and GPT-4 ~80 % on HumanEval coding; GPT-4 leads general knowledge at ~86 % MMLU (DeepSeek claims 88 %); Claude tops math with 88 % GSM8k; DeepSeek streams answers three-times faster than GPT-4.

• Reliability cues.  Copilot and Gemini ground answers in live search, lowering hallucinations; Claude’s “constitution” makes it extra polite and cautious; DeepSeek is open and less filtered—great for power users, riskier for unsupervised deployment.

Platform	One-line strength	One caution
Copilot	Deep, seamless Office & IDE integration	Premium features tied to $30 Microsoft 365 license
ChatGPT	Most versatile and largest plugin ecosystem	Free tier lacks live data; GPT-4(o) responses can be slow
Claude	Handles book-length context safely	Tends to be verbose; fewer third-party add-ons
Gemini	True multimodal answers plus Google Search grounding	Ultra model still rolling out worldwide
DeepSeek	Open-weights GPT-4-calibre power at tiny cost	DIY integrations and lighter guardrails

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2. Use Cases and Applications

One way to differentiate these AI tools is by the use cases they are best suited for.

Common applications include coding assistance, writing and content creation, information retrieval (search), data analysis, and acting as conversational agents for customer support.

Here we evaluate how each of the five systems performs in these major tasks.

✦ Coding Assistance

Microsoft Copilot (GitHub Copilot) was initially designed as an AI pair-programmer. It excels at in-line code suggestions and autocompletions within development environments. Integrated into editors like VS Code, Visual Studio, and others, Copilot continuously suggests code as you type, helping automate routine code writing and even generating whole functions on prompt. Studies have shown it can significantly boost developer productivity—helping developers code up to 55% faster in controlled studies. Copilot can also function in a chat mode (Copilot Chat) within IDEs to answer coding questions, explain code, or find bugs. It supports many programming languages (Python, JavaScript, TypeScript, Go, Java, C#, C++, etc.) and frameworks. Copilot’s training on vast GitHub code repositories gives it a strong grasp of typical coding patterns. However, it is focused exclusively on coding (and related dev tasks like writing tests or regex) rather than general tasks. Its tight integration with IDEs makes it seamless for coding, but it is not a general chatbot.

ChatGPT (OpenAI) has emerged as a versatile coding helper in addition to its general conversational abilities. Especially with OpenAI’s GPT-4 model available in ChatGPT, it can generate code from scratch based on natural language prompts, debug code, and explain code concepts. ChatGPT can handle complex algorithmic problems and has excelled in coding benchmarks (GPT-4 scored 80+% on the HumanEval Python coding test, a state-of-the-art result). Unlike GitHub Copilot’s inline suggestions, using ChatGPT for coding is more of a question-and-answer or code generation on-demand experience—you describe what you need, and it outputs code. This works well for writing functions, scripts, or even analyzing code you provide. ChatGPT Plus users can leverage Advanced Data Analysis (formerly “Code Interpreter”) to actually run code and get results, which is very powerful for testing and data tasks. ChatGPT supports a wide range of programming languages and can even translate between them. It’s excellent for explaining algorithms, generating code comments, and providing multiple approaches to a problem. However, in an IDE context, it’s less integrated—you’ll likely copy code from ChatGPT into your editor. In terms of accuracy, GPT-4’s coding capabilities are among the best, though it may occasionally produce syntactically correct but functionally incorrect code, so oversight is needed.

Anthropic Claude is also capable in coding tasks, and many users laud its ability especially in handling large codebases. Claude’s context window (up to 100K tokens) allows it to ingest very large files or multiple files at once, meaning you can paste in entire source files or logs and ask Claude to analyze them. This makes Claude extremely useful for tasks like debugging a large codebase or summarizing/reviewing code. In coding Q&A, Claude is very detailed and thoughtful—it will often explain its reasoning step by step. Some developers find Claude’s coding suggestions to be as good as or even better than ChatGPT’s in certain scenarios. Notably, one independent evaluation found Claude’s coding abilities to be top-notch, describing Claude as “unmatched” for coding among LLMs tested. (Claude 2’s performance on coding benchmarks like HumanEval jumped to 71.2%, from 56% in the previous version.) Claude can certainly write code in popular languages and help with debugging and explaining code. However, it lacks the tight IDE integration of GitHub Copilot—using Claude for coding is done via its chat interface or API. Anthropic has introduced a feature called Claude Instant (lightweight model) and a Claude Pro “Analysis” tool that lets Claude execute code (JavaScript) in a sandbox, similar to ChatGPT’s code interpreter. This enables Claude to not only suggest code but also run it for data analysis or verification, enhancing its utility for data-heavy coding tasks.

Google Gemini is Google’s next-generation foundation model, and it brings strong coding capabilities as well. According to Google, Gemini (especially the higher-end “Pro” or upcoming “Ultra” models) was trained with a focus on coding and even surpassed GPT-4 on coding benchmarks like HumanEval. It can generate, understand, and explain code in languages such as Python, Java, C++, Go, and more. In fact, Google used a specialized version of Gemini to create AlphaCode 2, which excels at solving complex competitive programming problems. In practical use, Gemini’s capabilities are surfaced through products: for example, in Google’s Colab notebooks and Android development tools. Google has integrated its code-focused model (previously called Codey) into Colab and other developer services; with Gemini, these coding assistants are even more powerful. Android Studio’s “Studio Bot” and other Duet AI for Developers features now leverage Gemini for code completion, code generation, and documentation within Google’s ecosystem. While not a standalone IDE plugin like Copilot, Gemini is accessible to developers via the Vertex AI API and is integrated in Google Cloud’s AI tools. It is strong at coding and comes with the benefit of Google’s extensive training data (including open-source GitHub code, StackOverflow Q&As, etc.). One advantage is that Gemini can be prompted with images or diagrams (thanks to its multimodal nature)—for instance, a screenshot of an error or a hand-drawn flowchart—and still assist with coding, something text-only models cannot do easily. Overall, Gemini is positioned to be one of the leading models for coding, and Google reports it “excels in several coding benchmarks,” though direct IDE integration might require third-party tools or Google’s cloud services.

DeepSeek offers a slightly different take on coding assistance. As an open-source AI platform, DeepSeek has a specialized Coder model (DeepSeek Coder V2), and the main DeepSeek-V3 model itself has demonstrated strong coding performance. In benchmarks, DeepSeek V3 outperformed OpenAI’s GPT-4 (GPT-4o) on coding tests—for example, scoring 82.6 on HumanEval vs GPT-4’s 80.5, and dramatically outperforming GPT-4 on a Codeforces competitive programming benchmark (51.6 vs 23.6). This suggests DeepSeek’s training places an emphasis on code and technical problem-solving. Users can interact with DeepSeek via its chat UI or API to get code generation and debugging help. It supports major programming languages, though being a relatively newer entrant, its integration is more DIY—e.g., via API into your own development tools. That said, the free DeepSeek Chat interface allows coding queries with no upfront cost, which is attractive for developers on a budget. Early adopters note that DeepSeek’s style and capability on coding queries feel very similar to GPT-4 (indeed, it’s speculated that DeepSeek may have been trained on outputs from GPT-4). The trade-off is that since it’s not natively built into IDEs, using DeepSeek for coding involves copying prompts and code between your editor and the chat (similar to ChatGPT usage). Still, DeepSeek stands out by offering high-end coding help for free or low cost, whereas others require subscriptions for their best models.

✦ Writing and Content Creation

All five AI systems are capable of natural language generation, making them useful for writing assistance—from drafting emails and documents to creating articles, marketing copy, or even fiction.

ChatGPT is widely used for writing tasks. With the GPT-3.5 and GPT-4 models, ChatGPT can produce well-structured paragraphs, translate ideas into fluent text, and adopt a desired style or tone. It excels at tasks like drafting blog posts, composing emails, brainstorming creative stories, summarizing long text, and so on. Users often praise ChatGPT’s ability to maintain context over a conversation—for example, you can ask it to refine a draft iteratively. GPT-4 in particular is known for its coherence, creativity, and strong command of language. It can emulate different writing styles (formal, casual, academic, etc.) and even mimic the tone of famous authors if asked. Many writers use ChatGPT as a “writing partner” to overcome writer’s block or generate first drafts. The model supports a wide variety of languages, so it’s not limited to English—you can ask ChatGPT to write in Spanish, French, Chinese, or any major language with good quality. One limitation in writing help is that ChatGPT has a finite knowledge cutoff (without browsing enabled), so if writing about very recent events or using up-to-date facts, it may not have that information. However, for general topics and creative writing, it shines. Also, by default ChatGPT does not cite sources, so if you need factual content with references, you must provide the references or use the browsing feature to have it include citations. Overall, ChatGPT’s strength is versatility and quality—it can produce anything from a formal business proposal to song lyrics in the style of Taylor Swift. Users do need to review and fact-check its outputs (it can confidently write incorrect information at times), but as a writing assistant, it’s extremely popular. (Indeed, ChatGPT’s own popularity—reaching 100 million users in 2 months—was largely due to how well it handles everyday writing and Q&A tasks.)

Microsoft Copilot approaches writing assistance through its integration in the Microsoft 365 suite. Microsoft 365 Copilot can draft content directly in Word, Outlook, PowerPoint, and more. For example, in Word you can ask Copilot to generate a first draft on a given topic, or in Outlook you might say “Summarize these emails and draft a reply,” and Copilot will produce an email response based on the thread. It essentially brings GPT-4’s capabilities (Microsoft confirmed Copilot runs on OpenAI GPT-4) into your work documents. The advantage here is context: Copilot has access (with permission) to your documents and data in the Microsoft Graph (emails, calendar, documents) to tailor its outputs. You can ask it to incorporate specific points from a document, or update a PowerPoint slide deck by creating speaker notes, etc. This contextual knowledge makes it very powerful for business writing—e.g., drafting a project proposal using relevant data from past reports. In customer support scenarios, Copilot in Dynamics 365 can draft responses to customer inquiries or generate meeting summaries in Microsoft Teams. Essentially, Microsoft is adding “Copilot” features across Word, Outlook, Teams, OneNote and more, aimed at productivity writing (emails, reports, presentations). The user experience is typically a sidebar or command interface where you instruct the Copilot and it inserts or modifies content for you. In our testing, Copilot’s writing quality is high (again thanks to GPT-4) and it’s adept at business tone. It will even cite the sources in your internal data when it drafts text (for instance, if it pulls a figure from a specific document, it can reference that document). This makes the writing output more trustworthy for internal use, as you know where facts came from. A limitation is that Microsoft 365 Copilot is not available to general consumers for free—it requires a business license (discussed in Pricing). Also, outside of Office apps, the free Bing Chat (now also called Microsoft Copilot in some contexts) can certainly help with writing as well—Bing Chat will generate content on any topic and even attempt to cite web sources for factual prompts. Bing Chat has “Creative mode” which is more whimsical and can be used to draft stories or poems, whereas “Precise” mode focuses on factual accuracy. Summing up, Microsoft’s offerings are extremely useful for office productivity writing and editing, directly embedded in the tools many users already use (Word, Outlook). The primary drawback is availability (premium for full features) and the fact that, unlike a general chatbot, the focus is on work-related documents rather than open-ended creative writing.

Anthropic Claude is very capable in content creation and writing as well. Claude’s style tends to be verbose, detail-oriented, and polite, which can be an asset for thorough explanations, essays, or long-form content. With its large context window, Claude can ingest lengthy source materials and produce detailed summaries. For instance, you could provide Claude with a 50-page PDF and ask for a summary or a derived article—something that exceeds the limits of other models—and Claude can handle it. This makes Claude excellent for research assistance and report writing. It can read multiple documents and then help you draft a report combining their information (provided everything fits in the 100k token window). Claude also uses a “constitutional AI” approach to maintain helpfulness, which often means it tries to provide well-reasoned and safe answers. In writing, it often explicitly states assumptions and may caveat its statements, which can be good for factual accuracy (less hallucination) but sometimes leads to a less flowing style. Users often find Claude good at brainstorming and ideation—you can bounce ideas and it will give lengthy, thoughtful responses. In terms of genres, Claude can do fiction, blogs, technical writing, etc., similar to ChatGPT. It might not be quite as creatively unrestrained as GPT-4 in some cases, but it’s close. One advantage: Claude is available via a web interface (and now mobile apps) without requiring any specific platform, and its Pro plan allows web access so it can pull in real-time information if you ask it to search. This means for writing about current events or recent data, Claude Pro could fetch info from the web (like an integrated mini-search) and incorporate it, reducing hallucinations for factual topics. Overall, Claude’s niche is long, detailed content—if you need a 3,000-word article with nuanced reasoning, Claude will happily produce it (whereas some models might try to shorten responses). You may need to edit for conciseness. Claude’s free tier has some usage limits but still provides robust writing help at no cost. A noted strength is summarization: enterprise users often use Claude to condense long documents or transcripts, thanks to that context size. Its weakness in writing might be a tendency to be too verbose or repetitive unless prompted to be concise, and slightly lower raw accuracy than GPT-4 on highly specialized knowledge (though it was trained on a huge dataset as well).

Google Gemini strongly leverages Google’s expertise in language understanding and its integration with Google’s services to assist in writing. In Google Workspace, Duet AI (powered by models like PaLM2 and now Gemini) provides a “Help Me Write” feature in Gmail and Google Docs. This is analogous to Microsoft’s Copilot in Office: you can type an instruction like “Draft a polite follow-up email about the marketing campaign status” in Gmail and it will generate a full email ready to send. In Docs, you can ask Gemini to generate content on a topic, continue writing from a bullet outline, or rewrite a paragraph for clarity. Google has a long history with translation and multilingual data, so Gemini is highly multilingual and adept at cross-lingual writing. In fact, Gemini (and its precursor Bard) support over 40 languages for input and output, enabling content creation in languages other than English quite fluently. Google’s Bard (Gemini) was noted for a feature: it could read an image and write about it—for example, you could upload an image and ask it to write a caption or a story about the image. This is a novel use case for writing that involves interpreting visual input (e.g., “Write a short story about this picture of a lonely mountain”—Gemini could do that, where pure text models cannot). For general chatbot-style writing, Gemini through the Bard interface can handle open-ended tasks similar to ChatGPT. Google has also integrated Gemini into its mobile Google app as a personal assistant that you can chat with via text or voice. This means you can verbally ask for something like “Give me a quick summary of this news article and write a tweet about it” and Gemini will do so, using Google’s speech recognition and text-to-speech for a seamless experience. One should note that early in Bard’s life, there were some well-publicized factual errors (the James Webb Telescope example), so Google has worked to improve accuracy and grounding. In writing tasks not tied to factual queries, Gemini is very capable. Also, being natively connected to Google Search, it can retrieve up-to-date information for writing tasks that require it—for instance, drafting a summary of “today’s stock market movement” can be done with current data. In summary, Gemini is excellent for integrated, context-aware writing, especially if you are in the Google ecosystem (Docs, Gmail) or need multimodal prompts. The only downside is that the full power of Gemini (Gemini Advanced/Ultra) may be behind a paywall (Google One AI Premium) and not all users globally have access to it yet as of early 2025. The free version (the default Bard) is still powerful but might not match GPT-4 level in all tasks until Ultra is widely released.

DeepSeek is a newer player but it does not lag in writing capabilities. DeepSeek markets itself as an “all-in-one AI tool” for coding, content creation, file reading, and more. For content creation, DeepSeek can generate essays, articles, stories, and social media content on demand. Its responses, from anecdotal usage, tend to be clear and on-topic, often resembling the style of GPT-4’s answers (not surprising given its training). An advantage is that it’s completely free to use via the web interface, so anyone can leverage a very large model for writing without a subscription. DeepSeek was trained on a massive 14.8 trillion tokens of data, which presumably includes a diverse range of internet text. It is also optimized for multiple languages, notably English and Chinese, among others. In fact, DeepSeek V3 is multilingual—the developers have balanced training data across languages to ensure it isn’t just English-centric. A recent update (DeepSeek V3.1) claims support for over 100 languages with near-native proficiency, greatly expanding its utility for non-English content. This makes DeepSeek particularly appealing for content creators working in languages that some other models might not handle as well. When it comes to factual writing, DeepSeek does not have built-in web search integration, so like base ChatGPT it relies on its training data for knowledge (which goes up to its training cutoff, likely 2024 given its late-2024 release). Users have noted that DeepSeek can sometimes mimic GPT-4 style so closely that it even produces similar phrasings and reasoning—which can be either reassuring (in terms of quality) or concerning (in terms of potential originality) depending on your perspective. One potential weakness in writing is that, being newer, DeepSeek’s fine-tuning for “harmlessness” might not be as extensively tested; it might be more likely to produce content that big-company models would filter out. But it also means it might be less likely to refuse a request. In any case, for general writing tasks, DeepSeek is quite powerful—essentially offering GPT-4 level writing ability at no cost, as one commentator put it. Content creators who are early adopters find it useful for generating drafts and even doing translations.

✦ Search and Information Retrieval

A key use case for AI chatbots is answering questions and retrieving information, effectively serving as an enhanced search engine. The landscape here is quite varied: some of these systems have direct web access or search capabilities, while others rely on their trained knowledge.

ChatGPT (standard) does not automatically browse the web for information—it relies on its trained knowledge (which, for the free version, has a cutoff of 2021). However, ChatGPT Plus users have the option to enable “Browse with Bing” on GPT-4, which allows the model to perform web searches and read results when answering a prompt. With browsing enabled, ChatGPT can provide current information and cite URLs in its answers, similar to an engine. OpenAI integrated this in 2023 and improved it over time. Still, the primary mode most people use ChatGPT in is offline (no live data). Within that constraint, ChatGPT is very good at factual recall for things it was trained on—it can explain historical facts, scientific concepts, or definitions well. But for up-to-the-minute news or niche queries, it may have gaps. ChatGPT Plus also offers plugins, some of which allow retrieval—e.g., a Wikipedia plugin, or Wolfram|Alpha for computational facts. These expand its retrieval capabilities. It’s worth noting that Bing Chat (which uses OpenAI’s GPT-4) effectively is ChatGPT with live search built in. Microsoft’s Bing Chat (now sometimes referred to as the free tier of Copilot) will automatically perform web searches when you ask a factual question and provide you with answers with footnoted sources. So if a user wants ChatGPT-like interaction with guaranteed updated info and sources, Bing Chat is the solution—and it’s free, though it requires using the Bing interface (web or the Edge sidebar or the Windows Copilot panel). In summary, ChatGPT itself is not primarily a search tool unless you have Plus with browsing or use Bing Chat, but the underlying tech is capable of search-style Q&A. Many users find ChatGPT’s concise, conversational answers more useful than a list of links, but caution that it can hallucinate facts if it’s not using the live tool.

Microsoft Copilot (Bing Chat & Windows Copilot) is explicitly designed to enhance search. Bing Chat (accessible via Bing’s website, the Edge browser sidebar, or in Windows 11 as “Copilot”) uses GPT-4 along with Bing’s search index to answer user queries with current information. It will perform multiple searches for a query, read content from the top results, and synthesize an answer. Importantly, it displays citations (footnotes linking to the source webpages) for factual statements. This was a key feature Microsoft pushed to address trust—you can click and verify where the info came from. In use, Bing Chat can provide a nice summary to questions like “What are the latest COVID-19 travel restrictions in Canada?” and cite official sources. It can also retrieve more obscure information if it exists online. The integration in Windows 11 means you can highlight text anywhere (say, some text from a PDF) and ask Copilot to explain or search about it—it blurs the line between a system assistant and a search engine. Microsoft has also unified Bing Chat Enterprise (which ensured commercial data protection) under the Copilot branding. As of early 2025, Microsoft 365 Copilot Chat (the enterprise chat tool) can even use work intranet data (via Microsoft Graph connectors) to answer questions about company policies or internal docs. This goes beyond internet search into enterprise search—truly acting as a company-specific “search assistant.” For general users, though, the free Copilot (Bing Chat) is a robust web-informed AI. There are some limitations—Microsoft imposes turn limits (you can’t have very long infinite conversations; there’s a daily cap like 200 queries/day to prevent abuse). Also, certain content is filtered (it won’t show you copyrighted full text or disallowed content from the web). Nonetheless, for information queries and real-time data, Copilot with Bing is one of the best options because it combines GPT-4’s understanding with Bing’s up-to-date knowledge, all with sources attached. This drastically lowers hallucination rates on factual Q&A, since the model has the chance to base answers on actual retrieved documents rather than just its memory.

Anthropic Claude did not initially have built-in web access, but as the competitive landscape evolved, Anthropic introduced web search integration for Claude (available to Claude Pro subscribers). Claude’s interface now has a feature where it can search the web and read results to provide answers or get more information. This means Claude can function similarly to Bing Chat when needed—for instance, if asked “What was the latest decision in case XYZ as of this week?”, Claude can go out, find relevant news or legal docs, and give an answer. Even in the free version, users could manually paste in text from web sources (thanks to large context) to have Claude analyze it, but the direct search feature automates that. Anthropic likely uses an approach to ensure sources are reputable, possibly summarizing multiple results. However, as of 2025 Claude’s web search might not be as fully developed or as default-on as Bing’s; it might require a user prompt or click to trigger a search. Still, this addition significantly improves Claude’s utility for factual queries. In terms of knowledge cutoff, Claude was trained on data up to early 2023, but with the ability to search it can overcome that. Without search, Claude’s knowledge is broad (similar to ChatGPT’s training) and it tends to be careful in answering factual questions, often expressing uncertainty if it’s not sure (due to Anthropic’s safety tuning). One unique angle: Claude can be connected to custom knowledge bases via its API (Anthropic encourages retrieval-augmented generation for enterprise solutions). They mention “Claude’s Integrations” and ability to connect any context or tool in the Max plan. This suggests that companies can hook Claude up to their internal wiki or database, and Claude will use that to answer questions (akin to an internal chatbot). Overall, for general users, Claude is now quite capable in the search/retrieval domain when using Pro, though the free version is more limited. Claude’s conversational style might be a bit more verbose than Bing Chat’s when answering questions, which some users like for completeness.

Google Gemini is deeply integrated with Google Search, arguably making it the most directly “search-enabled” AI of the bunch. Google has rolled out the Search Generative Experience (SGE) in its search engine, which uses Gemini to generate AI summaries at the top of search results. For example, when you search a question in Google (with SGE enabled), you might see an AI-composed answer with key points and follow-up questions, before the usual links. This uses live Google search data and is updated in real-time, with citations linking to the sources of information. Google reported that switching Bard to Gemini made SGE 40% faster in generating results, improving the user experience. Additionally, Gemini is available via the standalone Bard interface, which anyone can use like a chatbot to ask questions. Bard/Gemini will search Google behind the scenes when needed or when you click “Google it”. It typically provides a concise answer and often cites sources as footnotes (especially for factual queries). Since Google’s core competency is search, Gemini has the strongest backing in terms of up-to-date information access. One can ask Gemini things like “Compare these two products’ specs” and it will likely pull the latest info from product pages. Or, “What are some recent developments in AI this week?” and get a summary sourced from news articles. Moreover, because Gemini is multimodal, you can even ask it to identify or get info about something in an image (effectively a visual search). Google has effectively turned Gemini into a next-gen search assistant that can not only find information but interpret it and present it. This significantly reduces the time a user spends clicking through multiple links. For users, this is as simple as using Google’s normal products—no subscription needed for base Bard/SGE. For completeness, Google also offers an enterprise search assistant in its Google Cloud (Vertex AI Search) where Gemini can be applied to company data (similar to Microsoft’s solution). In everyday use, Gemini tends to have low latency for search queries (Google’s infrastructure advantage), and its answers are augmented by Google’s Knowledge Graph as well. Hallucinations are mitigated by the model cross-checking facts with actual search results, though not completely eliminated. One must note that Google has been cautious: Gemini’s answers sometimes default to a generic search result style if confidence is low, nudging the user to click the sources. But with ongoing improvements, Gemini is arguably leading in AI-driven search.

DeepSeek in its current form does not feature built-in web search integration. It operates mostly on its trained knowledge and whatever text the user provides. In other words, DeepSeek can answer questions based on what it “knows” (its training included a large swath of the internet up to 2024), but it won’t fetch new information on the fly. That said, being open and API-accessible means one could build a retrieval-augmented pipeline around DeepSeek—for example, using a vector database of documents and querying that with DeepSeek as the reasoning engine. Indeed, the DeepSeek community and third-party platforms (like TextCortex) highlight that you can integrate DeepSeek with knowledge bases or web search manually. For an end-user using the official DeepSeek Chat, however, you’re limited to its existing knowledge. This means for very recent events or niche data that wasn’t in its training, DeepSeek might respond with a guess (or simply say it’s not sure). Its relatively low-cost API could make it appealing as a Q&A engine in custom applications that add their own retrieval step. Also, one variant of DeepSeek called DeepSeek-R1 (Reasoner) is specialized for chain-of-thought reasoning and can be used in a toolformer style—possibly helpful in search scenarios where step-by-step reasoning is needed. In summary, for direct search use by general users, DeepSeek is the least equipped out-of-the-box. It’s strongest for queries on established knowledge and technical answers it was trained on, but it’s not a live search assistant.

✦ Data Analysis and Analytics

A newer but rapidly growing use case for these AI assistants is data analysis—from interpreting charts and spreadsheets to performing calculations or even running code to analyze data. This overlaps with coding, but it’s worth discussing how each handles analytical tasks, including working with user-provided data.

ChatGPT (with Advanced Data Analysis) has a standout capability here. OpenAI’s Advanced Data Analysis (formerly called Code Interpreter) is a feature for ChatGPT Plus users where the chatbot can execute Python code in a sandbox environment, generate visualizations, and work with files you upload. This turns ChatGPT into a mini data scientist. For example, you can upload a CSV of sales data and ask ChatGPT to analyze trends; it can read the file, use Python libraries (pandas, matplotlib, etc.) to compute statistics or produce graphs, and then present the results with explanations. It can even zip files, parse JSON, or manipulate images (to some extent, e.g., converting formats). This feature is extremely powerful—it means ChatGPT isn’t limited to just talking about data, it can actually compute answers and verify them. Many users have used it for data cleaning, exploratory data analysis, and visualization tasks that would normally require writing code in a notebook. Because the Python environment is fairly robust, ChatGPT can handle complex calculations, use machine learning libraries, etc. This is a unique offering; none of the others currently have as general-purpose an analysis sandbox integrated. The caveat is it’s only in the paid tier (Plus). Nonetheless, even without that feature, ChatGPT can analyze small datasets or math problems in pure text (it’s quite good at statistical reasoning and math up to a point, though complex math might trick it unless it can use the code execution). In fact, GPT-4’s performance on math and analytical benchmarks like GSM8k (grade school math) is high (~90%). So, ChatGPT is both a reasoner and a calculator when needed. For business intelligence, while it’s not directly connected to databases or Excel, users can copy-paste data or describe a data scenario and ChatGPT will attempt to analyze it conceptually. In short, ChatGPT Plus serves as a powerful data analysis assistant, from computing summary statistics to generating plots, making it very useful for data analysts and students alike.

Microsoft Copilot approaches data analysis via integration with Excel and Power BI. In Excel, Copilot can help users by generating formulas, explaining complex formulas, and creating visualizations. For example, you can ask, “Copilot, create a chart of sales by region from this data” and it will insert the chart in Excel. Or “Analyze this dataset for any outliers or trends” and it might produce a written summary of the data’s distribution, possibly even inserting a pivot table or conditional formatting to highlight outliers. Microsoft demonstrated such capabilities with the Excel Copilot—it’s like having an analyst sitting next to you while you work on a spreadsheet.

Similarly, in Power BI, Microsoft introduced an AI assistant that can answer questions about your data and even generate new DAX measures or visualizations. This is targeted at business users who might not know how to write the formulas—they can just ask in natural language, e.g., “What was the total revenue this quarter for product line X versus Y?” and Copilot (connected to the Power BI model) will generate an answer or chart. These are very domain-specific integrations and highlight Microsoft’s strategy: bringing AI directly to where the data lives.

Additionally, as noted in The Verge’s report, Copilot Chat (the free enterprise chat tool) allows uploading files (like an Excel sheet or Word doc) into the chat and having it summarize or analyze them on the fly. This is akin to ChatGPT’s file upload but presumably limited to certain file types and sizes. It’s great for getting a quick analysis of a dataset without leaving the chat interface.

Microsoft has also talked about formula generation in Excel: you describe what you want (e.g., “a formula to extract the domain from an email address”) and Copilot writes it. So for everyday Excel users, this is a huge time-saver. On the more advanced side, Microsoft’s Azure OpenAI service enables hooking GPT-4 to databases; some companies use it so that a user can ask in natural language and behind the scenes it translates to SQL, runs against a database, and then the model presents the result. That’s a custom scenario but shows how Copilot ideas permeate data analysis tasks.

The key benefit of Microsoft’s approach is that it’s context-aware and integrated—no need to copy data out to a chat; the AI is right there in Excel or BI tool. A limitation is that beyond those specific contexts, Copilot isn’t a general number-cruncher—Bing Chat won’t write a Python script for you (though it can do simple math and unit conversions). Also, the enterprise Copilot might have limits on file size it will analyze at once. Still, for business analytics and reporting, Copilot is extremely helpful by automating insights and charting.

Anthropic Claude has recently gained significant data analysis abilities with the introduction of its Analysis tool. Claude’s analysis mode allows it to write and execute JavaScript code within the chat. While not as extensive as Python, this enables Claude to perform data manipulation and even create simple visualizations (for example, using charting libraries in JS or just text output). Users can upload a CSV or JSON to Claude (Claude’s interface supports attaching files) and ask it to analyze the data. Claude will then generate code to parse the file and compute results, running that code behind the scenes.

Early demonstrations showed Claude reading a CSV, summarizing its contents, computing averages, etc. The decision to use JavaScript was likely for sandbox and compatibility reasons (running in browser), and it can cover many analysis needs (though heavy ML or complex stats would be harder in JS than Python). Still, this addition puts Claude closer to feature parity with ChatGPT in data analysis.

Claude can now visualize data in an interactive window and provide charts as outputs (likely outputting base64-encoded images of plots). For users, the experience is that they can get Claude to actually calculate things rather than just guess. Even before this tool, Claude was decent at data interpretation in text—e.g., you could paste a small table of numbers and ask questions, and it would reason through them. Its large context helps it handle bigger data (within the token limits) than some others might.

Another aspect: Claude’s integration with Google Workspace (Claude Pro can connect to your Google Sheets) means it could fetch data from your spreadsheet or analyze your Google Doc tables. This is similar to how Microsoft’s Copilot works with Excel, but via Claude’s interface connecting to Google’s APIs. It underscores Claude’s focus on being deeply helpful with user-provided content.

As for general math and reasoning, Claude’s performance is high (Claude 2 scored 88% on GSM8k math, comparable to GPT-4), so it can handle logical and numerical reasoning well. If anything, Claude might be more verbose in explaining the data analysis, which can be reassuring for transparency. The Claude Max plan even mentions higher output limits for all tasks and priority, which would benefit running bigger analyses.

In sum, Claude is now a strong contender for data analysis tasks, able to run code (JS), analyze text and images, and work with user data in a secure way. Pro plan users can trust it with sensitive data, as Anthropic emphasizes privacy. This is especially useful for professionals who want an AI to sift through data quickly or double-check their work.

Google Gemini approaches data and analytics through its integration with Google’s own tools and its multimodal capabilities. In Google Sheets, for example, Duet AI (with Gemini) can let users ask questions about their data or automatically fill columns with formula results described in natural language. Google demonstrated features like “Help me organize” where the AI can categorize data in Sheets or generate a summary of a dataset. While not as mature as Excel’s Copilot yet, these features are growing.

Additionally, since Gemini can handle text, images, and other modalities, it can even analyze visual data—e.g., if you show it a chart image, it can interpret it and summarize the insights. This could be useful if you have a screenshot of a graph and want a takeaway message.

Another aspect is Google’s integration of AI in BigQuery (Google Cloud) where you can use natural language to query databases (similar concept to Azure’s). Gemini on Vertex AI can also be hooked to business data for analysis and insight generation.

Moreover, because Gemini is built on Google’s AI research, it has strong capabilities in mathematical reasoning and handling large contexts. Google’s technical report mentioned long-context understanding across modalities in Gemini 1.5, which implies it can take a large dataset or long text and analyze it effectively.

While Google hasn’t publicly provided something exactly like ChatGPT’s Python sandbox, they did allow Bard to export generated Python code to Google Colab for execution (a semi-manual workaround). It’s possible by 2025 Google will integrate direct code execution in its AI tools as well, but even without it, Gemini can assist by writing code for data analysis which you can then run externally.

One novel data-related use of Gemini is in Google Analytics or Google Ads, where it can analyze marketing data and suggest optimizations (given its integration in Ads as mentioned in Google’s blog: Gemini to be in Ads, Chrome, etc.). Also, Google’s natural strength in geospatial and maps data could mean Gemini might be uniquely good at analyzing location-based data or combining it with text (speculative, but given Google’s data breadth, not unlikely).

In summary, Gemini is strong in reasoning and has tools in Workspace that mimic what Copilot does, though perhaps not as deeply integrated in all enterprise workflows yet. It’s excellent for multimodal and contextual analysis, and if you’re a heavy Google ecosystem user, it can tie into your workflow (e.g., analyzing a Google Sheet or summarizing a Google Doc of raw data). The gap to note is that without a direct code execution environment for arbitrary code, it’s a bit behind ChatGPT’s specialized data analysis feature. But Google will likely cover that with its cloud offerings and possibly future Bard features.

DeepSeek does not natively provide a code execution or data visualization feature in its chat interface. It will analyze data you give it in a conversational manner—for instance, you can paste a table of numbers or a JSON snippet and ask questions, and DeepSeek will do its best to interpret it logically. Thanks to its training, it can handle common calculations and describe trends (like “the numbers seem to be increasing each quarter”). But it’s essentially doing this in its neural network “head,” not by running actual arithmetic externally. That means it might make mistakes on complex calculations or large data.

However, DeepSeek’s creators have open-sourced various specialized models (DeepSeek Math, etc., as seen on their GitHub). So one could incorporate those or hope that those improvements are merged into the main model. If a developer wanted, they could use the DeepSeek API to build an “AI analyst” that queries data (by writing code externally) and then uses DeepSeek to explain it—but that’s a custom solution.

Out-of-the-box, DeepSeek is best for qualitative analysis and logical reasoning on provided data. One area it may excel: since it can handle 64K token context, you could potentially feed it a large JSON or a lengthy report and it can summarize or find insights (where other models with smaller context might choke). So for reading analysis (e.g., “find key insights in this 100-page financial report”), DeepSeek is useful. For numerical analysis requiring precise calculation, one would have to cross-verify any results it gives.

It’s also possible to use the DeepSeek-Reasoner (R1) model in conjunction—R1 can output a chain-of-thought (CoT) that shows step-by-step reasoning, which might help in verifying how it reached an answer. This is a unique feature where the model can effectively show its work, though it still might not run actual code.

In summary, DeepSeek’s data analysis capability is present but not as industrialized as ChatGPT’s or Claude’s—it’s more of a powerful logic engine you would need to guide. Its affordability means someone could run many analysis queries through the API without breaking the bank, which is appealing for large-scale tasks (it’s much cheaper per token than OpenAI models).

✦ Customer Support and Conversational Agents

A significant use case is using these AI systems as customer support assistants or chatbots that interact with end-users. This entails understanding user queries (which may be open-ended or pertain to specific products) and providing helpful, contextually appropriate responses—sometimes even taking actions like looking up an order status.

ChatGPT itself is not a turnkey customer support bot, but the technology behind it (OpenAI’s GPT-3.5 and GPT-4 models) is widely used via API to build custom support agents. Many companies have integrated GPT-based assistants into their customer service workflows to handle common queries. For instance, websites might have a chat widget “Ask our AI” which is essentially ChatGPT fine-tuned on the company’s info.

OpenAI has ChatGPT Enterprise and Azure OpenAI Service offerings that specifically target business use cases like this, offering data privacy and the ability to fine-tune or ground the model on proprietary knowledge. So, while ChatGPT the app isn’t sitting on a company’s website answering questions about refund policies, the model certainly powers such scenarios behind the scenes.

The strength of GPT-4 here is its ability to understand a wide variety of user inputs (even poorly phrased questions) and still extract intent, then respond conversationally. It can be made to follow a knowledge base or use retrieval so that it gives accurate answers based on official info. Many have found GPT-4 to greatly improve resolution rates for automated support, compared to past chatbots that followed rigid flows.

ChatGPT can also adopt a friendly persona and adjust tone (formal vs casual) which is useful for brand consistency in support. One concern historically was hallucination—a support bot must not invent company policies. This is mitigated by carefully fine-tuning the model with the correct data and using techniques like retrieval augmentation or tools. OpenAI provides the function-calling feature which can let ChatGPT, for example, call an API to get an order status when asked. This is how one could implement a transaction-based support agent (e.g., “Where is my package?” triggers the AI to call the tracking API and then respond).

In summary, ChatGPT’s tech is highly capable for customer support, and when implemented correctly, provides fast, natural responses. The direct ChatGPT interface could be used by support agents as well (as a copilot giving them suggested answers to customer emails or chats). Indeed, some companies have employees using ChatGPT or custom GPT assistants to draft responses, with the human making final edits. So ChatGPT can function as a customer support copilot on the agent side too.

Microsoft Copilot has specific offerings in the customer support domain through its Dynamics 365 suite. Dynamics 365 Customer Service now includes Copilot features that can draft responses to customer queries, summarize live chat conversations, and suggest next actions for human agents. Essentially, if an agent is handling a case, Copilot can listen in (via transcript) and provide suggested answers or knowledge articles to share, drawn from the company’s FAQ and documentation.

Microsoft also has a Power Virtual Agents platform for building chatbot flows, which can now be enhanced by generative AI to handle the unstructured hand-off or fallbacks. The advantage Microsoft brings is integration with CRM data—the Copilot can be aware of the customer’s details, past orders, etc., through the Microsoft Graph and Dynamics data. This is done with appropriate permissions and ensures the AI’s responses are personalized and accurate.

For example, Copilot could automatically fill in a refund form and draft a response:

“I see you ordered X product on Jan 5th; I’ve initiated your return, and you’ll get a confirmation shortly.”

The underlying model is still likely GPT-4 (or GPT-4 Turbo as default for free tier), but Microsoft wraps it with enterprise guardrails.

Additionally, Bing Chat Enterprise (now free Copilot chat for businesses) can be used in internal-facing support—employees asking the bot for help with HR or IT issues, etc., and it can answer from internal SharePoint pages (since it can access Microsoft Graph data in that context).

Microsoft’s approach is thus twofold:

✦ AI helping human support agents (agent assist)

✦ AI directly as first-line chatbot for customers (with context from company data)

The system ensures confidentiality (Bing Chat Enterprise/Copilot doesn’t leak your data to the public model training). The major strength here is the end-to-end solution Microsoft offers if you are within their ecosystem. A possible weakness is if you are not a Microsoft shop, adopting their system could be overkill or not easily customizable beyond what Dynamics supports.

But for many companies that already use these, Copilot slots in neatly to improve support efficiency. (It’s reported that over 85% of developers felt more confident using Copilot—one can analogize that to support agents likely feeling more confident with an AI backup as well.)

Anthropic Claude has carved a niche among companies looking for a safer, controllable AI for customer interactions. Claude’s training with a “constitution” that avoids toxic or harassing outputs is appealing for customer-facing use, where maintaining a polite and helpful tone is critical. Many organizations have tested Claude as the backbone of their support chatbots.

Anthropic offers Claude Instant, a faster, lighter model suitable for real-time chat deployment (with slightly reduced quality but still good). Claude can be given a company’s FAQ or knowledge base (even a very large one, thanks to that 100k context) and then interact with customers using that information. One big benefit: Claude’s large context allows it to consume entire documentation manuals or multiple product FAQs in one prompt, meaning it can answer very detailed or multi-part questions without losing context.

Also, Claude is designed to ask clarifying questions when a user query is ambiguous, which is useful in support scenarios to avoid giving wrong info. For instance, if a customer asks “I need to reset my device – it’s Model ABC123,” and there are two versions of that model with different procedures, a well-prompted Claude might respond by asking which version the customer has, rather than guessing – this is something Anthropic has aimed for with their helpful/honest/harmless principle.

In practice, companies can access Claude via API (Anthropic’s or through providers like Slack—Slack’s Atlas was an early integration with Claude for internal support). Also, Claude’s Team and Enterprise plans allow collaboration: multiple team members can share conversations or the bot can be integrated into shared tools like Confluence for internal Q&A.

Some notable reviews of Claude (e.g., from TechCrunch) highlight its ability to handle long customer emails and draft coherent responses in one go, which saves support teams time. The downside for Claude in support might be slightly slower development of fine-tuning tools (OpenAI and Microsoft have been quicker to offer fine-tuning or function calling; Anthropic introduced fine-tuning later on and in a limited way).

But Anthropic’s focus on safety means Claude will likely err on the side of caution – e.g., if a customer gets angry or uses profanity, Claude is less likely to produce an inappropriate response. It also tends to cite policy snippets if provided (like “According to your warranty terms…”) in answers, sticking to facts.

Overall, Claude is an excellent choice for conversational customer support, especially if large context or a high degree of control and safety is needed.

Google Gemini powers Google’s own customer support offerings and is available for companies via Google Cloud. One major channel is Google’s Contact Center AI (CCAI) platform, which integrates conversational AI into call centers and support workflows. With Gemini, CCAI can provide even more natural and context-aware virtual agents. For example, when you call a customer service number and interact with a voice bot, there’s a chance Google’s AI (now Gemini-based) is behind it, interpreting your spoken requests and responding from knowledge base articles. Google also allows integration of their AI into business messaging via the Business Messages API. Gemini’s multimodality can shine here – for instance, a customer could send a photo of a defective product in a chat, and a Gemini-powered bot could analyze the image and guide troubleshooting or initiate a return (something text-only models would struggle with). Moreover, Google’s excellence in language translation means a Gemini support bot could seamlessly handle multilingual queries, translating on the fly and responding in the customer’s language. For companies that use Google Workspace, Gemini for Workspace (part of Google One AI Premium) can be connected so that, say, an employee asking the assistant a question about an internal policy will get an answer sourced from Drive documents or Gmail threads. This is analogous to Bing Chat Enterprise. On the public side, Gemini (via Bard) hasn’t been specifically branded for customer support, but some companies have embedded the Bard chat widget on their sites for Q&A. Google’s focus also extends to mobile assistants: with Gemini replacing Google Assistant on Android for some users, businesses could potentially leverage that – e.g., a user could ask their phone “Track my order from Store X” and if Store X’s backend is connected, the assistant (Gemini) could retrieve that info. That’s a bit futuristic, but technically feasible with Google’s integrations. One notable strength of Gemini in support is speed and search. It can quickly fetch relevant answers from a company’s website (if not internal, at least public FAQs) given it can use Google’s search index. So even without explicit fine-tuning, it might handle a lot of common questions by itself by retrieving from the web. A potential downside is that Google, as of 2024, started offering some of these advanced features via paid plans (Google One AI Premium at $19.99/mo) – this includes “Gemini Advanced” which presumably has better reasoning and access to Workspace data. This means for full-fledged enterprise use beyond trial or limited scale, you may need that subscription or a Google Cloud contract. Also, like with any, careful testing is needed to ensure it doesn’t serve outdated info – but Google’s rapid model updates (Gemini 1.5, 2.0, etc.) indicate they keep it fresh. In sum, Gemini is a strong backbone for support bots with its multilingual, multimodal, and up-to-date knowledge capabilities, especially attractive to those already using Google’s ecosystem for communications.

DeepSeek could be utilized for customer support mainly due to its open-source nature and low cost, but it is not a turnkey service in this regard. A company could fine-tune a DeepSeek model on their support transcripts or FAQs (since the model weights are open or at least some versions are, according to the DeepSeek paper), and deploy it in their own infrastructure. This might be appealing for organizations that are sensitive about data privacy and want an on-premise AI solution rather than calling an API from OpenAI or Google. DeepSeek’s strong performance in reasoning means it can handle complex support questions well. It also supports Chinese and other languages strongly, which could be useful for companies with significant user bases in those languages (DeepSeek, being a Chinese-founded project, likely has excellent Chinese language understanding). Additionally, DeepSeek’s extremely low running cost (token pricing an order of magnitude or two cheaper than GPT-4) can make it cost-effective to run a high-volume support chatbot. However, the burden is on the implementer: unlike ChatGPT or Claude where you can just feed documentation and get results, with DeepSeek you might need to build the retrieval and interface logic yourself. Some third-party platforms (like the TextCortex or others) have already integrated DeepSeek so that you can use it similarly to how you’d use GPT – e.g., to answer questions with provided knowledge. If a company uses such a platform, they get the benefit without deep integration work. But in general, DeepSeek is more for the tech-savvy teams who can leverage an open model. If properly set up, a DeepSeek-based support bot could be highly effective and fully under the company’s control, which is a strength. A potential weakness is that DeepSeek might not have undergone as rigorous a fine-tuning for “customer-friendly tone” as something like Claude or GPT-4 which have had extensive RLHF. It might require some fine-tuning or prompt engineering to ensure it responds in the desired manner (polite, concise, and on-brand). Also, handling of sensitive situations (angry customers, abusive language) would depend on how well the open model’s safety training is – which is an unknown relative to big players. That said, early adopters report that DeepSeek’s style is quite aligned with GPT-4 (which is polite by default), so it’s likely fine with basic safeguards. In conclusion, DeepSeek can be used for customer support with customization, offering a cost-efficient and flexible alternative, but it’s not an out-of-the-box managed service for this domain.

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3. Core Capabilities and Features

Beyond specific use cases, it’s important to compare the capabilities, features, and technical specifications of each AI system. This includes the underlying model characteristics (such as size and architecture, multimodal abilities, and context length), supported languages, and any unique features like tool use or function calling. Below is a summary of core capabilities:

Underlying Model & Size

• Microsoft Copilot: This is a product name rather than a single model. Under the hood, Microsoft 365 Copilot and Bing Chat use OpenAI’s GPT-4 (with some Microsoft enhancements). For instance, Microsoft confirmed Copilot initially ran on GPT-4 and DALL-E 3 for image generation. Recently, Microsoft indicated that the free tier Copilot chat uses GPT-4 Turbo by default, while a “Copilot Pro” option can access the original GPT-4 for more precise responses. GitHub Copilot uses models from OpenAI (Codex, and now GPT-3.5/4) and Anthropic (Claude variants) depending on the feature. So Microsoft’s strategy is leveraging top models from partners. They also fine-tune or prompt these models for specific domains (code, office tasks). Microsoft has not released parameter counts, but GPT-4 is estimated at ~1 trillion parameters (not officially confirmed) and uses a dense transformer architecture.

• ChatGPT (OpenAI): ChatGPT is powered by OpenAI’s GPT-series models. Free version uses GPT-3.5 Turbo (175B parameters, transformer architecture), and Plus offers GPT-4 (estimated hundreds of billions or more parameters, exact number not public). GPT-4 is significantly more capable, especially in reasoning and creativity. GPT-4 can also accept image inputs (Vision) in the Plus version. Both GPT-3.5 and GPT-4 are pure transformer models. ChatGPT’s models support function calling (developers can instruct the model to output a JSON calling a function, enabling tool integration) and have undergone heavy fine-tuning with RLHF (reinforcement learning from human feedback) to follow instructions and stay within guardrails.

• Claude (Anthropic): Claude 2 is built on Anthropic’s proprietary architecture. It’s roughly in the same class as GPT-3.5/4 (likely on the order of tens of billions of parameters – Anthropic hasn’t disclosed an exact parameter count for Claude 2). What stands out is context length: Claude supports 100,000 tokens context, far above most competitors at the time of its release. It uses a transformer variant optimized for long context. Anthropic’s research focuses on “constitutional AI,” so the model is fine-tuned with a set of principles guiding its responses. Claude also has Claude Instant (a smaller, faster model, maybe ~20B parameters) for lightweight tasks. Claude can output a chain-of-thought reasoning if requested in certain modes, though by default it doesn’t show it.

• Gemini (Google): Google’s Gemini is a family of models. By late 2024, Google had Gemini Nano, Gemini Pro, and was testing Gemini Ultra. These correspond to different sizes/capabilities. Gemini is multimodal from the ground up – meaning the model architecture was designed to handle text, images, and audio inputs natively. It likely combines techniques from Google’s text models (like PaLM) and their image models. The largest version, Gemini Ultra, reportedly surpasses GPT-4 on many benchmarks and is being rolled out to select users. Parameter count for Gemini Ultra isn’t confirmed, but given Google’s PaLM was 540B and they intended Gemini to be bigger, it could be on that order or use mixture-of-experts for even more scale. Google’s technical report indicates state-of-the-art performance in NLP and multimodal tasks. All Gemini models use transformer-based architecture with enhancements for multimodality and perhaps reinforcement learning. Gemini supports function calling and API tools integration (similar to ChatGPT plugins) through their PaLM API and tools like Google’s Codey and Toolformer research.

• DeepSeek: DeepSeek V3 uses a Mixture-of-Experts (MoE) Transformer with 671 billion total parameters (but only 37 billion active per token on average). This MoE architecture allows it to be extremely large in capacity while keeping inference efficient, selecting different “experts” for different tokens/tasks. It was trained on 14.8 trillion tokens of data, which is an enormous training corpus (larger than GPT-4’s likely). DeepSeek V3’s architecture also features Multi-Token Prediction (MTP) which speeds up generation by predicting multiple tokens per step. This is why DeepSeek can output up to 60 tokens/second – notably fast. There are also specialized sub-models: DeepSeek-R1 (a reasoning-optimized model which can output CoT reasoning), DeepSeek Coder, DeepSeek Math, etc., indicating the team has fine-tuned versions for specific domains. DeepSeek’s context window is 64K tokens by default, which is very high (comparable to GPT-4’s max and Claude’s 100k half). Interestingly, a comparison noted that both DeepSeek V3 and GPT-4o had support for up to 128K input tokens under certain configurations. DeepSeek is open-source or at least openly available; the model weights and even a research paper are provided, meaning developers can host it themselves. This openness is a key differentiator at this scale.

Multimodal Capabilities

• Microsoft Copilot: Partial. If we consider Bing Image Creator (which is DALL-E 3 integrated into Bing), Copilot can generate images on command. In Windows Copilot, you can ask it to create an image and it will, using DALL-E. But Microsoft’s language model usage (GPT-4) does not yet take image input in Copilot scenarios (except maybe Bing Chat Vision which was introduced allowing users to upload an image in Bing Chat to analyze it). Indeed, Bing Chat did add the ability to accept images: you could, for example, upload a photo and ask for analysis or creative captions. This is GPT-4 with vision in action. So, Microsoft Copilot (Bing Chat) supports image understanding. For example, “What does this error message screenshot mean?” works in Bing Chat. Audio input/output is supported via Windows Copilot (it ties into Windows voice access) and Bing mobile app – you can speak your query and it will respond with spoken words if you enable it. So in total, Microsoft’s Copilot offering across products is fairly multimodal: text, images (as input for Bing Chat), and voice. It cannot generate audio or video (those are not in scope yet), but it can generate images (via DALL-E plugin).

• ChatGPT: Yes, multimodal to an extent. GPT-4 introduced vision capabilities – ChatGPT (Plus) allows users to attach images to the conversation. The model can analyze the image, describe it, or reason about it. For instance, you can send ChatGPT a photograph of a graph and ask for insights, and it will parse the graph (to the best of its ability) and provide an analysis. You can also do things like show it a math problem on paper or a screenshot and it will help solve it. On the output side, ChatGPT does not generate images (except via specific plugins or by providing text that can be used to fetch an image). It also doesn’t generate audio by itself, but OpenAI did integrate speech into the ChatGPT mobile app – you can have voice conversations with ChatGPT, using Whisper for input and a new text-to-speech model for output. This makes ChatGPT essentially an AI voice assistant as well. So while the core model is text-based, the user experience is multimodal in that sense.

• Claude: Initially text-only, but now has some image capabilities. As seen on Anthropic’s site, Claude can “analyze text and images.” They likely have added a vision component where you can attach an image for Claude to interpret. Possibly it identifies objects in images or reads text from them (OCR). However, details on Claude’s image analysis are not deeply public; it might be relatively basic compared to GPT-4’s vision. Claude does not generate images (no built-in image gen). Audio isn’t mentioned as a feature, so presumably no voice integration yet in Claude’s own app. That said, since the Claude API exists, third parties could hook it up with speech-to-text to achieve voice conversations. Overall, Claude is primarily a text-based AI with emerging image understanding features.

• Gemini: Fully multimodal. Google designed Gemini to natively handle different modalities together. It can accept text, images, and even audio input. From Google’s demos, Gemini can do things like interpret an image and answer questions about it (without needing external OCR). It also presumably can handle video or at least a sequence of images (though not confirmed, but likely future direction). On output, Gemini can describe images in words, and through integration with Google’s tools, it might generate images (Google has Imagen, but they haven’t rolled it into Gemini yet publicly). For audio, since Google Assistant is now Gemini-driven on some devices, it can understand spoken queries and reply with speech (using Google’s WaveNet or similar for TTS). Essentially, Gemini aims to unify these capabilities: you could have a conversation where you speak a question, show a picture, and ask it to write code about that – and it can handle the whole mix. This multimodality sets Gemini apart in tasks like describing what’s in a photo, analyzing charts images, solving visual puzzles, transcribing audio, etc., within one AI.

• DeepSeek: As of V3, primarily text-based. The main DeepSeek model and chat deal with textual input and output. However, there is mention of DeepSeek VL (likely stands for Vision-Language) – a research project possibly adding image understanding. It’s unclear if that’s integrated into the chat yet. DeepSeek doesn’t natively do image generation or have known audio features. So, for now, consider it non-multimodal. It focuses on text and code.

Language Support

• Microsoft Copilot: Because it uses OpenAI’s models, it inherently supports many languages. GPT-4 is fluent in dozens of languages (English, Spanish, French, German, Chinese, Japanese, etc., and even less common ones to a decent extent). Microsoft has showcased Copilot use in different languages in Office (e.g., drafting a document in French). Additionally, Bing being global means Bing Chat was made available in multiple languages and can translate on the fly. So Microsoft Copilot will respond in the user’s language or the language of the content. One can assume over 50 languages are well-supported (with English being best). For coding, programming languages are a given. Microsoft has also worked on localized support (their Azure AI services target multiple languages for enterprise).

• ChatGPT: OpenAI’s models are trained on a multilingual corpus. GPT-4 in internal evals showed strong performance in many languages – it can answer in languages like Italian, Polish, Korean, etc., with high proficiency (often close to a fluent non-native speaker level). There have been tests showing GPT-4 can even handle tasks like humor or idioms in other languages to a surprising degree. It can also do translation tasks between languages at a level comparable to specialized systems. So ChatGPT is not limited to English. The interface supports user input in any language and will output in that language. In fact, one of ChatGPT’s common uses is as a translator or language tutor. Its weakness might be extremely low-resource languages or dialects where training data was scarce.

• Claude: English is its primary training language (most of Anthropic’s public demos and focus have been English). However, Claude does have some multilingual capability, especially for languages using Latin script and possibly a few major non-Latin ones (Chinese, Arabic). It might not be as extensively multilingual as GPT-4. Anthropic hasn’t published a list, but user reports indicate Claude 2 can understand and respond in many languages reasonably well. Still, it may make more errors or respond in English if it’s uncertain. That said, Claude’s presence via Slack and other platforms might have pushed it to handle at least the languages those platforms support. We can assume support for major European languages and some Asian languages, but perhaps not as proficiently as GPT-4 or Google’s model.

• Gemini: Coming from Google, multilingual support is a given. Bard (even before Gemini) was launched in over 40 languages, including not just European languages but also languages like Hindi, Arabic, Chinese, Japanese, etc. Google likely used its translation and multilingual data (like the massive dataset behind Google Translate) to train Gemini. Furthermore, Google indicated improvements for certain language pairs and a premium “Advanced” model with better non-English performance. It’s safe to say Gemini can handle a very wide array of languages, probably one of the best in that regard (only perhaps rivaled by Meta’s Llama 2 or other models specifically tuned for language diversity). If your use case requires speaking to the AI in your native tongue, Gemini (and ChatGPT) might have an edge over Claude.

• DeepSeek: Initially developed by a Chinese team, DeepSeek focuses on English and Chinese as top languages. Indeed, the website is bilingual, and they likely ensured excellent Chinese NLP performance (perhaps surpassing even GPT-4 in some Chinese tasks). They also explicitly mention support for other major languages and balanced training to avoid only excelling in English. A Medium post suggests it’s optimized for English, Chinese, and other major languages, with improved translation abilities. Another source says V3.1 supports 100+ languages with near-native skill, which if true, is very impressive and suggests a deliberate effort to be multilingual. It would fit their aim as an “open-source GPT-4 competitor” to not be limited. It’s possible DeepSeek incorporated multilingual training data similar to what Meta did for their models. So practically, you could converse with DeepSeek in Spanish or Arabic and get good answers. However, since it’s less tested by the community, one might find some quirks or uneven performance in certain languages. But generally, expect broad language support from DeepSeek V3.

Context Window and Memory

• Microsoft Copilot: When powered by GPT-4, Copilot inherits GPT-4’s context limits. GPT-4’s standard context is 8K tokens, and there is a 32K token variant. It’s unclear if Bing Chat/Copilot uses the 32K version – but given Bing can handle fairly large amounts of content (e.g., analyzing a long article), it might be using 32K in some cases. GitHub Copilot, when using “GPT-4o” or Claude for chat, has context to handle at least a file or two of code (maybe a few thousand tokens visible). Microsoft 365 Copilot can incorporate document content well beyond standard limits by retrieving relevant portions of your files via the Graph rather than stuffing the entire text into the prompt. But fundamentally, if it needs to put data into the model’s input, it’s limited by the model’s context size. So, likely 8K for most cases, possibly 32K for some enterprise scenarios. Windows Copilot (which is Bing Chat) historically had a limit on how much text you could paste in, which was a few thousand characters, but those limits keep evolving.

• ChatGPT: Free GPT-3.5 context ~4K tokens. GPT-4 for ChatGPT Plus initially 8K, and a 32K version was rolled out to some users or via API. By 2025, it’s possible Plus offers the 32K context to everyone (OpenAI had expanded that for enterprise and maybe as an optional Plus feature). So ChatGPT’s memory per conversation can be very large with GPT-4 (32,000 tokens is roughly 24,000 words). This allows it to keep a lengthy chat history or analyze long inputs (like small ebooks). OpenAI also has system messages and conversation turn limits to manage context usage. ChatGPT will start forgetting or losing earlier context if the conversation grows beyond the limit. But for most use, 8K is ample and 32K covers nearly any single document.

• Claude: 100K tokens context, a major selling point. That’s approximately ~75,000 words of text – about the length of a novel. This means Claude can effectively “remember” an entire book or reference manual provided to it in one go. This enormous window is transformative for tasks like summarizing transcripts of day-long meetings or doing a Q&A on a whole database dump (structured as text). Users have successfully used Claude to summarize hundreds of pages. It does make Claude slower if you actually fill that many tokens, but it works. The Claude Instant model has a smaller context (maybe 10k or so), but the main Claude 2 is the one with 100k. Anthropic might even extend this further in future. For now, they hold the lead on context length among these five.

• Gemini: Google mentioned plans to increase context window for future versions. The current Gemini models (Nano, Pro) context isn’t explicitly stated, but presumably at least 8K or 16K. Bard earlier had about 4K limit which they then expanded. With Gemini Ultra, Google likely targets parity or better than GPT-4. It wouldn’t be surprising if Ultra supports, say, 32K or more. Also, Google can use retrieval to overcome limits (finding relevant info rather than sending entire docs into the prompt). In Vertex AI, one can chunk data and have the model read it piecewise. But raw context size – not confirmed publicly beyond hints. Given Google’s research on LongPaLM and such, they have the tech for long context. Possibly we’ll see 100K context in Gemini as well. But as of early 2025, we’ll assume at least tens of thousands of tokens context for the top-tier Gemini.

• DeepSeek: Officially 64K context for DeepSeek V3. That’s extremely high and second only to Claude here. Some sources claim it can support 128K input (maybe via splitting or an upcoming version). In practice, 64K (around 50,000 words) is huge and sufficient for almost any document or set of docs a user might feed it. DeepSeek also implemented context caching which likely helps manage long conversations by not re-sending all tokens every time (similar to a mechanism GPTs use to save on costs for repetitive context). With MoE architecture, handling long input might be more cost-effective for them as well. So DeepSeek’s memory is excellent – one can drop in very large texts and still discuss them. This is a big plus for research or data mining tasks.

Tools and Plugins Integration

• Microsoft Copilot: Extensible via Microsoft’s own ecosystem. In Copilot for Power Platform, it can trigger workflows. In Windows, Copilot can open apps or adjust settings at your request (like “turn on Bluetooth” works because it links to Windows OS commands). Microsoft has Copilot Studio for enterprises to create custom “AI agents” that connect to internal data or APIs. Essentially, they allow businesses to craft domain-specific tools on top of Copilot. In GitHub Copilot, there are third-party extensions and agents (GitHub announced an ecosystem where Copilot can invoke other dev tools or web services via an “agent mode”). So Microsoft’s tool integration story is strong but controlled – they ensure the plugins are safe and useful for their users (and enterprise Copilot uses “approved” connectors to internal systems). Notably, Bing Chat introduced support for browser plugins (for example, it could use OpenTable, Kayak, etc., though these might have been experimental). And now that OpenAI plugins are open, Bing Chat has compatibility with some ChatGPT plugins as well. This means Copilot (Bing) can do things like execute code using the Wolfram plugin or retrieve data via a web browsing plugin, though Bing inherently does web. So, Microsoft’s tool integration story is strong but controlled – they ensure the plugins are safe and useful for their users (and enterprise Copilot uses “approved” connectors to internal systems).

• ChatGPT: Pioneered the Plugins ecosystem. There is a plugin store with dozens of third-party plugins – from Expedia for travel booking to Wolfram for computation to Slack integration. ChatGPT can use these plugins to extend its capabilities beyond what the base model knows. It can also perform function calling, which developers use to connect it to arbitrary tools (the model will produce a JSON when it figures a function needs calling, and the host app executes that function and returns result). This essentially lets ChatGPT interface with databases, web services, or hardware through a controlled API. A concrete example: a weather plugin allows ChatGPT to get live weather data when asked, instead of guessing. Or a shopping plugin could let it search for products. This architecture has made ChatGPT very flexible. In OpenAI’s platform, developers can build private plugins for their own use as well. ChatGPT Plus and Enterprise users get to use these plugins. It’s a definite advantage in terms of capability extension – for almost any specialized task (legal search, SQL database querying, etc.), a plugin can be made.

• Claude: Doesn’t have an official “plugin store”, but it supports a degree of extension. For instance, Claude’s API and Claude’s own interface allow integration with your documents (uploading files, connecting Google Drive). The Claude Max plan explicitly mentions “connect any context or tool through Integrations”. This implies that enterprise users can set up connections where Claude can query certain internal tools or data sources. Anthropic’s focus is more on direct model reliability than on giving it many tools, but they acknowledge the value of retrieval. In their docs, they show how developers can implement a retrieval step with Claude (embedding text and letting Claude pick relevant info). Claude can also output JSON or code if needed to interact with systems (Anthropic’s API supports something similar to OpenAI’s function call, though it might not be as turnkey). Given that Claude can also execute JS now, that itself is a tool use – albeit constrained to data analysis. So while not as rich as ChatGPT’s plugin ecosystem, Claude can be integrated into pipelines with tools. Many companies using Claude have built custom connectors (for example, Claude reads from a vector database or triggers certain actions if user says specific keywords). It’s more bespoke at this stage.

• Gemini: Google hasn’t launched a “Gemini plugin store” as such, but they have built tool usage into Gemini’s skillset. For example, Bard was shown to automatically invoke Google’s internal tools: it can fetch flight prices (like using Google Flights API in the background) or use Maps info. They integrated it with Google’s suite, so it can create emails for you, or summarize a webpage (if you give it a URL, Bard will visit it and summarize – effectively a built-in browser tool). Google’s approach might be less about third parties (for now) and more about utilizing its own powerful tools (Search, Maps, Gmail, Calendar, etc.). However, with the Gemini API on Vertex, developers can connect Gemini to functions similar to OpenAI’s function calls. Google’s PaLM API had a concept of tool use where you could define tools the model can call (like a calculator or database lookup). That carries over to Gemini. So, enterprises can give Gemini “skills” like accessing an inventory database. Google’s AppSheet and other low-code platforms also started integrating AI, meaning a Gemini-powered bot could trigger workflows in Google’s cloud ecosystem. It’s likely that as Gemini matures, Google will open up more third-party plugin style integrations, especially for consumer Bard (similar to how Assistant had an ecosystem of “Actions”). As of early 2025, though, the immediate power is that it’s deeply wired into Google’s own services, which cover a lot of ground already.

• DeepSeek: No known plugin framework. DeepSeek is more of a raw model offering. It does have function calling capability in the API – their docs mention support for JSON output and function calling interface. That means you can instruct DeepSeek to output a JSON of a certain format and your application could execute something. It’s similar to how you’d do with GPT. But DeepSeek doesn’t come with a ready-made set of tools. The open-source nature means the community could integrate it with things as they like (for example, one could connect it with a web search API manually, making a pseudo-plugin). Some community-driven UIs for LLMs might support DeepSeek and allow loading plugins. In short, DeepSeek is capable of tool use (especially since it can follow instructions well and produce structured outputs), but it does not have an official plugin ecosystem managed by DeepSeek Inc. If you run DeepSeek yourself, you can combine it with other open-source projects (like LangChain) to give it tool-using abilities (web browsing, code execution, etc.). But that requires know-how and isn’t out-of-the-box for an average user in the chat app.

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4. User Experience

The usability and overall user experience (UX) of these AI tools can be as important as their raw capabilities. This encompasses interface design, ease of access, responsiveness (latency), integration into existing workflows, and accessibility features. Let’s compare how each fares in terms of UX.

Interface & Ease of Use

• Microsoft Copilot: For coding (GitHub Copilot), the interface is essentially invisible until needed – it shows ghost text suggestions in your code editor as you type, which is very intuitive for developers. Accepting a suggestion is just a press of Tab. There is also a Copilot Chat pane in VS Code where you can ask questions and get answers/edits applied to your code, which feels like having a chat assistant within your IDE. This tight integration means no context switching – developers love that they don’t have to leave their editor to consult documentation or StackOverflow; Copilot brings the knowledge to them. For Microsoft 365 Copilot, the UI is a sidebar or prompt box within Office apps. For example, in Word there’s a Copilot sidebar where you might see suggestions like “Draft a summary of this document” or you can type a request. It can insert content directly into the document or show it for review first. Early feedback from pilots of M365 Copilot indicated it’s quite user-friendly and context-aware (the prompt suggestions often smartly relate to what you’re working on). In Teams, Copilot can generate meeting summaries with one click after a meeting. In Outlook, it offers a “draft reply” button for emails. These context-specific entry points make it very convenient. Windows Copilot is accessible by a sidebar (Win+C) – it looks like a chat embedded in the OS, where you can ask anything (system commands or general questions). It’s consistent across Bing, Edge, Windows now, with the Copilot icon and similar UI. So the Copilot UX is heavily about integration: it feels like part of the software you already use, rather than a separate thing. This lowers the learning curve – if you know how to use Word or VS Code, Copilot just augments it quietly. The consistency (same icon, similar chat interface if you open it) across applications is also a plus for familiarity. One possible downside: because it’s tied to those applications, if you want a general AI chat, you might end up opening Bing Chat or Windows Copilot. But that’s not really a downside since those are available free. Another note: Copilot’s design generally uses light interface elements with clear prompts, and in enterprise, admins can customize some aspects (like suggesting certain prompt templates relevant to the company).

• ChatGPT: The ChatGPT interface (web and mobile) is straightforward – a chatbox with a history of conversations on the side. It has a clean, minimalist design that contributed to its rapid adoption. Starting a new chat session is easy, and you can rename or delete conversations for organization (though as of now, only folders/tags are missing, which some heavy users want). On desktop web, it’s accessible via any browser at chat.openai.com once logged in. On mobile, OpenAI’s official app (iOS and Android) provides chat on the go, with added features like voice input. The simplicity is a strength: anyone can go to the site, type a message, and interact – there’s virtually no setup or integration needed. This makes ChatGPT the go-to “general AI assistant” for millions. The mobile app’s voice mode is quite polished – you tap a microphone, speak your question, and it speaks back the answer in a natural voice, which is a very assistant-like experience. ChatGPT’s responsiveness is generally good: GPT-3.5 model responds very fast (almost real-time typing out), while GPT-4 is a bit slower but still reasonable for moderate-length answers (a few seconds delay then streaming tokens). If the prompt is very long or the answer is long, GPT-4 can take more time due to rate limits (it might produce around 20-30 words per second). But the interface shows the answer streaming token-by-token, which users find engaging (it’s like it’s thinking in real time). ChatGPT does not natively integrate with other user tools in its base interface (aside from plugins, which are somewhat advanced use). It’s essentially a separate tab you use alongside your work. For some, that’s perfectly fine; for others who want it integrated into, say, their email client, they might rely on third-party extensions. But given its popularity, many such extensions exist (for example, a Chrome extension to quickly send selected text to ChatGPT and get an answer). Accessibility-wise, ChatGPT’s interface is relatively accessible (screen readers can work with it, and the mobile app’s voice ensures even visually impaired users can interact via speech). One UX challenge was content filters – occasionally ChatGPT would refuse queries or stop output if it detected something disallowed; while necessary, this can sometimes disrupt the experience (e.g., if it falsely flags an innocuous conversation). OpenAI has been refining this to reduce overly broad refusals.

• Claude: Claude is available through a web interface at claude.ai and also via an official app on iOS/Android. The web UI is also a chat-style interface. One nice UX feature Claude introduced is “Projects” – essentially folders to organize your chats. This is great for power users who might have dozens of conversations; you can group them by project or topic. ChatGPT doesn’t have that yet, giving Claude a small edge in organization. Claude’s interface allows file uploads (like PDFs, images) directly, which is user-friendly for providing context or data to analyze. You just drag-and-drop a file into the chat, and Claude will process it. ChatGPT Plus also added file upload but only in certain modes (like Advanced Data Analysis) or via plugins; Claude makes it a core feature. Claude’s responses are typically delivered in one go (it writes them out fully) and it’s quite fast at short answers. For very long answers, Claude might sometimes “pause” and ask if it should continue, or it might stream partially. In general, it’s quick, partly because Anthropic optimized it for high throughput. The design is similarly minimal. One point about Claude’s behavior: it tends to be extremely polite and verbosely helpful. Some users appreciate the thoroughness, but it can sometimes feel like it over-explains. That’s more of an output style issue, but it affects UX in reading the answers (you may need to scroll through a long explanation when you wanted a one-liner). However, you can instruct it to be concise and it usually obeys. Claude also has a Slack integration (since earlier versions, an app called “Claude for Slack”), which means teams using Slack can converse with Claude in a channel or DM. This is a great UX for those users – they don’t need to leave Slack to use the AI. It can summarize channels or answer questions referencing Slack threads if given permission. In terms of accessibility, Claude’s interface is similar to ChatGPT’s – no known issues. The mobile app brings it to smartphones, though ChatGPT’s might be more mature at this point. Another plus: because Claude’s free tier had generous limits at first, many users could use it without friction of hitting a paywall, which is a kind of UX (availability) advantage.

• Gemini (Google Bard): Gemini’s main user-facing incarnations are Bard (web and mobile via the Google app) and integration in other Google apps. The Bard web interface is again a simple chat. It has a few distinctive UX features: Bard allows you to choose different drafts for an answer – it often generates 2-3 variations of its response and lets you pick the one you like best (or mix elements). This is useful in creative tasks, giving users more control. Bard also has an explicit “Google it” button for search – if you feel the answer might need verification or more info, you can push that. It also can display images in answers (for example, if you ask “What does the Eiffel Tower look like at night?”, Bard might show an image in its response thanks to integration with Google Images). The Google mobile app integration is a big UX plus – it means on Android and iOS, Bard/Gemini is built into the Google search app that hundreds of millions use. It’s as easy as tapping the chatbot icon or even speaking a query. On Android, with the replacement of Assistant on some devices, you can long-press power or say “Hey Google” and get Gemini to respond. That voice assistant experience is very fluid (leveraging Google’s top-tier speech recognition and voice outputs). Many people might interact with Gemini without even knowing it, simply by using their phone’s assistant or search. The integration with Chrome (Search) means if you’re searching the web and you want AI help, it’s right there – a sidebar in Chrome with the SGE results. So it meets users where they already are. The Duet AI features in Workspace also provide nice UX: for example, in Gmail’s compose window there’s a “Help me write” button – super straightforward for users to invoke AI assistance in context. Similarly, in Google Docs a sidebar for generating text or in Google Sheets to explain formulas. Google has tried to keep the UX consistent by using the same sparkly Bard icon in various places. One potential downside: Bard had some session instability early on – sometimes it would wipe context unexpectedly or refuse to continue a conversation (likely a safety reset). They have improved this, but ChatGPT was generally more stable in maintaining long chat sessions without reset. Bard also initially lacked a conversation history feature (you couldn’t see past sessions), but Google eventually added the ability to view and name previous conversations. As of now, Bard conversations can be saved to your Google account (and you can clear them if desired). For enterprise, Google likely will integrate Bard/Gemini into Google Chat for internal Q&A similarly to Slack/Claude. So overall, Gemini’s UX strength is in-product integration and multi-platform availability – basically leveraging Google’s reach. It’s designed to be accessible to billions, often through interfaces they already know (Search bar, Assistant).

• DeepSeek: The DeepSeek Chat interface is web-based (chat.deepseek.com) and also there’s a mobile app. The design is fairly standard chat UI with not much extra ornamentation – a place to enter text, and the conversation appears above. It supports code formatting in answers, etc. Because it’s a newer service, it might not have as much polish or additional features as the others. For example, I’m not sure if it has conversation history saved (it might as long as you’re logged in), or organization features. However, it does support file uploads for long context (given its focus on long inputs). Also, DeepSeek’s site mentions you can upload documents and engage in long-context dialogues, implying a user-friendly way to supply documents, like an “Upload file” button. The response speed of DeepSeek V3 is notable – 60 tokens/sec generation means it feels very responsive, even for big answers. Some early users commented that DeepSeek’s outputs come almost blazingly fast compared to GPT-4, which is a nice user experience when you’re in a hurry. Of course, if it’s free, sometimes there might be queue or rate limits, but they likely provisioned well. The DeepSeek app on iOS/Android makes it accessible on mobile; it touts “official AI assistant for free…powered by DeepSeek-V3”. We can surmise it probably has a similar look to other chat apps. DeepSeek doesn’t have widespread integrations yet – you can’t directly use it in your email or IDE unless you manually hook its API. It’s basically a standalone chatbot at this point. Being a newer brand, it also doesn’t have the benefit of user familiarity – but they do use the common chat paradigm, so it’s not hard to pick up. One UX factor is trust and language: the site is available in English and Chinese, which helps serve a broad user base. Also, because it’s open, some users might be more willing to trust it with certain data (they can even self-host a variant). In terms of persona, DeepSeek’s style as noted is similar to GPT-4, possibly a bit generic. It doesn’t have a strongly distinctive voice, which for most is fine. Summing up, DeepSeek offers a fast, no-frills chat experience that is improving, but lacks deep integration or advanced UI features that the bigger players have. Its main advantage in UX is speed and cost (free access means no friction to try it out).

Responsiveness & Speed

• DeepSeek V3 is very fast in generating text thanks to MTP tech. In practice, users often note ChatGPT (GPT-4) can be slow for long answers, while Claude is relatively faster for similar lengths. DeepSeek might outpace both, making it feel very snappy.

• Gemini’s speed in search (SGE) was improved by 40% due to model upgrades, meaning Google is optimizing latency a lot – they know users expect quick responses in a search context.

• ChatGPT GPT-3.5 is extremely fast (often near instant for short answers), which gave it an edge for casual use (the free tier quick answers). GPT-4 had a cap of ~25 messages per 3 hours for a while, causing delays for heavy users; that’s a UX friction which others didn’t impose (Claude free had generous limits, DeepSeek free currently has essentially open access).

• Microsoft’s Copilot Chat presumably has some rate limiting, but in enterprise usage they allow quite a lot. Bing Chat had turn limits per session historically (like 20 turns then you have to reset the topic) to avoid runaway conversations – that is a minor UX annoyance if you hit it, but not a big deal for most queries.

• Google Bard currently allows pretty long conversations by default but may start fresh if it gets confused or if you manually reset. Overall, all are working on making the AI feel as real-time as possible. None are so slow as to be unusable; it’s more about whether you wait 2 seconds or 10 seconds for an answer.

• Voice input and output can add a second or two processing in ChatGPT or Bard but generally quick enough for a conversation.

Integration & Accessibility

• Integration we’ve largely covered: Microsoft (Office, Windows, developer tools), Google (Search, Workspace, Assistant), have deep integration advantage. ChatGPT is somewhat siloed but has API for integration (so many third-party apps integrated it themselves – for example, Snapchat’s MyAI, various writing apps, etc., embed ChatGPT). Anthropic has partnerships (Claude in Notion AI possibly, Claude in Quora Poe, etc.). DeepSeek currently less integrated, but its API allows it in theory.

• Accessibility: All five have web interfaces which are accessible via browser. Microsoft’s may require certain browsers for Bing (Edge was initially required for full Bing Chat, though there are workarounds now and they opened it more broadly). For people with disabilities: voice control is one big factor. ChatGPT’s mobile app’s voice feature is great for those who cannot easily type. Google’s integration with Assistant obviously is beneficial (some disabled users rely on Google Assistant/Home devices to interact via voice). Microsoft ironically discontinued Cortana but with Copilot they might re-introduce voice in Windows (there is voice typing and Narrator that could connect). Claude doesn’t have native voice yet. Another aspect is international accessibility: ChatGPT and Bard are available in most countries (except where restricted, like ChatGPT was blocked in some regions or Bard initially not in EU but later introduced after compliance updates). Claude was initially only US/UK; by 2025 they have likely expanded availability (their site now mentions working to be globally available). DeepSeek being from China might have a focus in Asia but they have an English site and likely serve globally except maybe they have to comply with local regulations. One interesting note: DeepSeek being Chinese means it might be accessible in China where OpenAI is not (if they got clearance), but not sure if that’s the case or if they cater only outside. The LinkedIn snippet shows their Chinese name 深度求索, indicating a domestic presence. If DeepSeek is accessible in China, that’s a huge market where others (ChatGPT, Bard, etc.) are restricted. That’s a major accessibility point in a geopolitical sense.

In summary, User Experience:Microsoft Copilot’s UX is characterized by seamless integration into existing tools, offering assistance in-place (which professionals appreciate), whereas ChatGPT offers a universal, easy-to-use conversational interface that’s standalone but massively accessible on its own. Claude provides a solid chat experience with useful features like organizing conversations and handling big context (good for professional use), and it integrates with platforms like Slack to meet users where they work. Gemini shines by being embedded in Google’s ubiquitous platforms – essentially bringing AI to one’s search, email, phone, etc., which is extremely convenient and lowers barriers. DeepSeek focuses on delivering the core chat experience with speed and openness, though it currently requires users to use it in its own app or API, thus appealing more to early adopters and developers than general non-technical users. As these services evolve, we expect UX to further converge towards very accessible, multi-platform experiences with voice and visual interactivity being standard.

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5. Pricing and Plans

The cost and available plans for each AI service vary widely, from free offerings with limits to enterprise subscriptions costing dollars per user. Below is a comparison of pricing models, including free vs paid tiers, usage limits, and subscription costs.

ChatGPT (OpenAI)

ChatGPT offers a free tier and a paid tier (ChatGPT Plus). The free tier provides access to the GPT-3.5 model (fast but somewhat less capable), with unlimited chats, subject to some rate limits (if the servers are busy, free users may be throttled). Free ChatGPT is extremely useful for casual use and has no direct usage cap per day, though it lacks newer features and uses an older knowledge cutoff (Sept 2021). The Plus tier costs $20 USD per month and gives access to the more advanced GPT-4 model, usually with priority access even during peak times. Plus users also get access to features like browsing (web access), plugins, and the code interpreter (advanced data analysis) at no extra cost. There isn’t a formal message limit for Plus users on GPT-4 now, but there is a cap on messages in short time frames (historically 25 messages per 3 hours for GPT-4, which has been raised or adjusted over time). OpenAI also launched ChatGPT Enterprise for organizations, which includes unlimited use of GPT-4 (no message cap), a higher 32k token context window, data encryption and admin console, and guaranteed uptime. Enterprise pricing is not public (it’s presumably custom quotes per seat or usage) – some reports suggest it could be around $30+ per user for large businesses, but it varies. Additionally, OpenAI has API pricing separate from ChatGPT UI: for reference, GPT-4 (8k) via API is $0.03 per 1 000 input tokens and $0.06 per 1 000 output tokens, which could translate to roughly $0.005–$0.01 per typical message. GPT-3.5 is much cheaper at $0.002 per 1 000 tokens. But that’s for developers building apps, not directly relevant to ChatGPT’s consumer pricing. For most users: free or $20/mo are the options, and Plus is quite affordable relative to the power it provides.

Microsoft Copilot — Pricing broken down by product

GitHub Copilot:

• Initially had a single paid plan at $10 per month (or $100/year) for individuals (with free access for verified students and open-source contributors).

• In late 2024, GitHub introduced Copilot Free, a limited free tier offering 50 chat messages and 2 000 code completions per month. This free tier uses slightly limited models (Claude 3.5 or GPT-4 Turbo variants).

• For unlimited use, Copilot Pro remains $10 per month, which includes unlimited completions and some premium features (like better model access).

• They also added Copilot Pro+ at $39 per month which gives even larger allowances of “premium” requests (like GPT-4 or Claude 4 usage) and access to the very latest models (Claude 4, Gemini 2.5 Pro, etc.).

• For organizations, Copilot Business is $19 per user/month, and Copilot Enterprise is $39 per user/month. Business and Enterprise offer admin controls and policy management. (Enterprise prices are higher because they include centralized billing and more robust support and security features.)

Microsoft 365 Copilot

:• Priced at $30 per user per month (for commercial customers). This license is an add-on on top of existing Microsoft 365 subscriptions and grants access to Copilot features inside Word, Excel, Outlook, Teams, etc.

• There is no separate free tier for the full M365 Copilot – it’s a premium feature for businesses. However, Microsoft has introduced Copilot Chat, a limited but free offering for Microsoft 365 users (formerly Bing Chat Enterprise). Copilot Chat allows employees to use an AI chat with commercial data protection at no extra cost, but it’s outside the Office apps. The full $30 Copilot license gives the integrated in-app experiences and more advanced agent capabilities.

• Microsoft acknowledges $30/user is significant but positions it as boosting productivity enough to justify it. For small businesses (1-300 seats), they also allow $30/user (previously thought to be enterprise-only).

• Microsoft is exploring metered pricing for Copilot usage via Azure (charging per message in some scenarios), which could offer more granular cost control in the future.

Bing Chat / Windows Copilot:

• Free for all users with a Microsoft account. No direct charge to use Bing Chat; Microsoft monetizes via search and ecosystem usage.

• Bing Chat Enterprise – which ensures no data is leaked and traffic is encrypted – is included free with certain Microsoft 365 plans (Business Standard, E5, etc.).

Anthropic Claude

• Claude Free: $0. Provides access at claude.ai with some limitations. Free usage initially allowed quite a lot (for example, up to 100 messages every 8 hours), but officially limits aren’t quantified; Claude will simply ask you to wait or upgrade once limits are hit.

• Claude Pro: $20 per month (or $17/mo with annual billing). Roughly 5× the free usage, plus priority access, web search ability, unlimited Projects, and Google Workspace integration.

• Claude Max: $100 per month (5× Pro usage) or $200 per month (20× Pro usage). Includes everything in Pro plus significantly higher rate limits, longer outputs, priority at peak times, Claude Code (direct AI coding in terminal), advanced research access, and custom-tool integrations.

• Team and Enterprise: Team at $25 per user/month (annual) or $30/mo monthly, 5-user minimum. Enterprise is custom-priced (~$30–$40/user) with admin, security, and higher usage allowances.

• API pricing: Claude-2 (100k context) costs roughly $11.02 per million input tokens and $32.68 per million output tokens. For most consumers, subscription tiers are more relevant.

Google Gemini (Bard)

• Bard Free: $0. Uses base or pro Gemini model via https://gemini.google.com/ or the Google app. Unlimited general usage with occasional throttling for extremely heavy use.

• Google One AI Premium: $19.99 per month. Grants access to Gemini Advanced (a more powerful model), plus “Gemini for Workspace” features in Gmail/Docs/Sheets and the usual Google One storage benefits.

• Duet AI / Workspace Enterprise Add-ons: Google has offered a $30 per user/month add-on for enterprise Workspace customers to unlock full AI features (document summarization, Help Me Write, etc.). Pricing may vary by contract.

• Vertex AI API: Usage-based pricing per token for developers. Gemini Pro is lower-cost; Gemini Ultra higher. Details are similar to PaLM scheduling.

DeepSeek

• DeepSeek Chat / App: Free access to DeepSeek-V3 and R1 for individual users, with generous daily limits. No subscription plan exists as of 2025.

• DeepSeek API: Ultra-low rates. Example: $0.27 per million input tokens and $1.10 per million output tokens for V3 (cache miss). Cache-hit inquiries and off-peak hours can cost 50–75 % less. This is roughly 50-100 times cheaper per token than GPT-4 pricing.

• Self-hosting: Model weights are open, so organizations can deploy DeepSeek on their own hardware with no recurring license fees, incurring only infrastructure costs.

Key Pricing Snapshot

Service / Product	Free Tier	Individual or Pro Plan	Business / Enterprise Plan
OpenAI ChatGPT	GPT-3.5, unlimited chats	Plus $20/mo (GPT-4, plugins, tools)	ChatGPT Enterprise (custom, ~$30–$60/user)
Microsoft Copilot	Bing Chat & Windows Copilot free; GitHub Copilot Free (limited)	GitHub Copilot $10/mo; Copilot Pro $10/mo; Copilot Pro+ $39/mo	M365 Copilot $30/user/mo; GitHub Copilot Business $19 / Enterprise $39
Anthropic Claude	Claude Free (limited messages)	Pro $20/mo (priority, web search)	Team $25–$30/user/mo; Max $100–$200/mo; Enterprise custom
Google Gemini (Bard)	Bard free (Gemini Pro)	AI Premium $19.99/mo (Gemini Advanced)	Workspace add-on $20–$30/user/mo; Vertex API usage-based
DeepSeek	DeepSeek Chat free (V3, R1)	None (all free for end users)	API ≈ $0.001/1K tkn; self-host free (open weights)

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6. Performance and Benchmarks

When it comes to raw performance – accuracy, intelligence, speed, and reliability – these AI systems have all been put to the test. We will consider known benchmark results, observations from reputable reviews, as well as aspects like latency, hallucination rates, and user satisfaction.

Benchmark Results and Accuracy

ChatGPT / GPT-4

OpenAI’s GPT-4 model is generally considered a gold standard for overall performance as of 2024. On academic and standardized benchmarks, GPT-4 has achieved very high scores. For example, GPT-4 scored 86.4% on the MMLU benchmark (a test of knowledge across 57 subjects) and is able to pass difficult exams (bar exam top 10%, 90th percentile on GRE verbal, etc.). On coding, GPT-4 scored around 80-82% on HumanEval (Python), one of the highest among models when it was introduced. GPT-4 also demonstrated strong common-sense reasoning (e.g., top-tier on HellaSwag and Winograd schemas). GPT-3.5 is weaker (MMLU ~70%, HumanEval ~50%), so GPT-4 is the main reference for ChatGPT’s best performance. Notably, Google’s research and others often use GPT-4 as a baseline to beat. GPT-4 is also multimodal (vision): while it’s harder to quantify, it can do tasks like describing images or solving visual puzzles at a level previously unseen outside research labs. That said, Google claims Gemini Ultra now exceeds GPT-4 in many areas.

Microsoft Copilot

Since Copilot is powered by GPT-4 and sometimes Claude, its performance in tasks is similar to those models. In coding, GitHub Copilot (earlier versions using Codex) was evaluated by GitHub: in one study, developers using Copilot completed tasks 55% faster than those without. Another stat: 88% of developers said it improved their productivity. These are not standard benchmarks but real-world usage metrics indicating high user satisfaction and efficiency gain. Copilot’s code suggestions have a certain accuracy – GitHub indicated that about 46% of code suggestions were accepted by developers on average (this stat is from earlier Copilot; with newer models it might be higher). For natural language tasks, M365 Copilot’s quality was tested internally by Microsoft – they wouldn’t release it if it wasn’t meeting high accuracy in drafting emails or summarizing meetings. Also, Bing Chat (with GPT-4) was rated highly in a Stanford evaluation of online AI chatbots in early 2023 for factual Q&A due to its use of citations. Bing Chat has an advantage on factual correctness because it can double-check against sources; this likely results in lower hallucination rate on factual queries compared to a standalone GPT-4. For instance, if asked a tricky factual question, Bing Chat will search and often yield a correct, sourced answer, whereas ChatGPT might guess and sometimes be wrong. On the other hand, if Bing’s search results contain misinformation, the model might incorporate that – but generally, it’s anchored.

Anthropic Claude

Claude 2’s performance is close to GPT-4 on many benchmarks, with some slightly lower, some on par. In Anthropic’s announcement, they gave: Claude 2 achieved 76.5% on the Bar exam’s multiple choice (vs GPT-4’s 75% on that same portion); scored 71.2% on HumanEval (Python coding), which is below GPT-4’s ~82% but above most other models at the time; and 88.0% on GSM8k (math word problems), which is actually very strong (GPT-4 was around 85% on GSM8k). Another stat: Claude 2 was ~78.5% on MMLU (slightly below GPT-4’s 86%). So generally, Claude is excellent but a notch below GPT-4 in coding and some knowledge domains, while possibly matching in math/logic. One area Claude reportedly excels is reasoning with very large contexts – e.g., summarizing a 100-page document, which GPT-4 8k can’t do in one go. In user evaluations, Claude is often praised for being less likely to refuse harmless requests (it has a more permissive but still safe stance), and for detailed, coherent outputs. In terms of hallucination, anecdotal evidence suggests Claude sometimes is more cautious about stating uncertain facts than GPT-4, due to its constitutional training. A user might see Claude give a nuanced “I’m not certain but here’s my best attempt” where ChatGPT might just pick an answer confidently. This can mean less glaring falsehoods from Claude in some cases, but not always – it will still make things up if asked beyond its knowledge. Anthropic likely tracks a metric for “factual accuracy” internally, but not public. As for speed, Claude 2 is quite fast at generating long responses (faster than GPT-4 which has a fixed slower output rate). Also, with the 100k context, it can outperform others simply by virtue of being able to use more information – for instance, in an eval where the answer requires reading a long text, Claude can get 100% because it can read it all, while others fail if they can’t take all input at once.

Google Gemini

Since Gemini is new, Google has provided some comparative data in their blog and technical report. They claim Gemini Ultra outperforms GPT-4 on text-based benchmarks like reasoning tasks. For example, on a suite of NLP benchmarks (possibly including MMLU, Big-Bench, etc.), they show a chart with Gemini Ultra slightly above GPT-4. Specifically mentioned: Gemini Ultra scored 59.4% on MMMU (a multimodal reasoning benchmark) which was SOTA. On multimodal benchmarks, Google showed Gemini beating GPT-4V (Vision) and other models – indicating top performance in image understanding tasks. For coding, Google stated “Gemini Ultra excels in coding benchmarks, including HumanEval”. A leaked figure suggests Gemini 2.0 (which might be what was deployed as Bard’s advanced model in Dec 2024) scored around ~85% on HumanEval, which would edge out GPT-4’s 80.5%. Also, Gemini presumably did very well on Codeforces (AlphaCode 2 solved harder problems with Gemini’s help). On the MT-Bench (a benchmark for measuring chat model quality), some community tests have found Gemini’s responses to be on par with or slightly better than GPT-4 in certain scenarios, especially after fine-tuning. Another aspect is factual accuracy: early indications are that Gemini (Advanced) has reduced hallucinations compared to Bard’s previous PaLM model. Being able to draw on Google search content in real time is a huge boon. For example, a notable review in early 2024 when Gemini was rolled out into Assistant noted that it could answer a wide range of questions accurately and even perform voice tasks that felt more natural than Siri or Alexa. However, every model hallucinates at times – one reason Google rebranded Bard to Gemini was to signify improvements. User satisfaction: Google likely has internal metrics from Bard’s user base. Bard had a shaky start (some underwhelming responses caused user trust issues), but with rapid iterations (Gemini 1.5, 2.0), user feedback reportedly improved. In early 2024 Google was rolling out a paid version because they believed Gemini was now competitive and compelling. Some press reviews commented that the new Gemini Assistant felt more capable and conversational than prior versions. In terms of latency, SGE was sped up by 40% with Gemini, so Google’s heavy optimization means responses often come very quickly, sometimes almost as fast as a search query result (which is important for adoption – people won’t use it if it’s much slower than normal search).

As shown above, Google’s data suggests Gemini Ultra has a marginal lead over GPT-4 in both text and multimodal benchmarks, positioning it at the cutting edge. These differences aren’t huge – perhaps a few percentage points – but at that level any improvement is notable. It’s worth noting these are Google’s reported numbers; independent evaluations will continue to verify performance. Still, it aligns with general sentiment that the top models (GPT-4, Claude 2, Gemini Ultra, and perhaps Meta’s new ones) are all very close in capability with each having slight edges in certain domains.

DeepSeek

According to evaluations reported by third parties (and DeepSeek’s own paper), DeepSeek V3 matches or surpasses GPT-4 (GPT-4o) on many benchmarks. One source noted DeepSeek V3 scored 88.5 on MMLU vs 87.2 for GPT-4o, and we saw earlier it had 82.6 on HumanEval vs 80.5 for GPT-4o. It also massively outscored GPT-4 on Codeforces (51.6 vs 23.6), implying a real strength in more complex coding problems (maybe because of specialized training or MoE focusing on math?). Additionally, DeepSeek V3 got 90.2% on MATH (a math competition dataset), which is an excellent result (GPT-4 was around 85% there). These numbers paint DeepSeek as on par with the best in pure benchmarks. It’s important to clarify “GPT-4o” mentioned is presumably the optimized GPT-4 model used by OpenAI in some contexts, with context 128k. So essentially, DeepSeek aimed to be a GPT-4 competitor and indeed seems to have achieved near-equal performance in many areas, and even better in a few (particularly math, reasoning, certain coding). It lagged behind on some things like output length (GPT-4 could output 16k tokens vs DeepSeek 8k in one go), but that’s minor.

The reviews from users said: for reasoning and math, DeepSeek > GPT-4o; for coding, Claude was best, with GPT-4o and DeepSeek behind it. So one person felt DeepSeek was great logically but maybe not the top coder (contrasting with the Codeforces result, but that might depend on prompt style). DeepSeek’s large MoE model might excel at tasks requiring specialized knowledge (the mixture of experts can hold more niche info perhaps).

On hallucinations: we don’t have broad studies, but given it’s new and likely had less RLHF, I would expect DeepSeek to have a slightly higher tendency to produce unfiltered or occasionally incorrect info unless carefully prompted. There was mention of the suspicion that DeepSeek was partly trained on GPT-4 outputs, which could mean it inherited some of GPT-4’s strengths and weaknesses. If it “apes GPT-4’s style” as said, it might also ape its errors or confidence. But since the cost is low, many in the community have been testing it and the feedback is that it’s remarkably solid. As it’s open, there might be independent benchmarks soon.

Speed-wise, DeepSeek is very fast as noted (generating 3x faster than previous and likely faster than GPT-4). This low latency combined with high performance gives a good user-perceived performance as well. For user satisfaction, since DeepSeek is newer, we only have anecdotal evidence: for example, a user stated “with 671B parameters, this open-source giant matches or even outperforms top players like GPT-4o and Claude 3.5.” The fact that someone without paying can get GPT-4 level results made many users excited, describing it as “as powerful as ChatGPT’s paid version” in tutorials. So early adopters are quite impressed with DeepSeek’s performance-to-cost ratio.

The challenge for DeepSeek will be maintaining quality as it scales usage (ensuring its servers or open model can handle it) and continuing to improve via fine-tuning with feedback, but so far it’s positioned as a breakthrough in open AI.

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7. Hallucination and Reliability

All large language models have the problem of “hallucination” – stating things that are factually incorrect or completely fabricated in a confident manner. The extent varies:

ChatGPT / GPT-4

GPT-4 significantly reduced hallucinations compared to GPT-3.5, but it can still produce them, especially outside its knowledge or when asked something tricky. OpenAI measures factuality on curated datasets and reported GPT-4 at around 60-70 % factual on adversarial questions in their tests (not 100 %). Features like browsing help mitigate this by letting the model quote real sources. ChatGPT is known to sometimes make up citations if asked for them, unless a retrieval plugin fetches actual references. In coding, it might hallucinate nonexistent functions or libraries that “sound right.” OpenAI continually patches these tendencies.

For user-perceived reliability, many users trust ChatGPT for creative or summary tasks but double-check facts for high-stakes questions. That’s now common guidance: don’t blindly trust an AI’s factual outputs without verification. ChatGPT gets a lot right, yet a confident wrong answer (for example, medical or legal advice) is memorable to users.

Bing Copilot

Thanks to inline citations, users can catch hallucinations more easily—if Bing cites a source that does not support what it said, you know there’s an issue. Microsoft logs feedback on “not helpful” or “inaccurate” answers and improves the system. Bing also uses guardrails to refuse speculative or unverified queries.

A July 2023 study by Cornell researchers found GPT-4 without tools had a hallucination rate of about 19 % on open-domain questions, while Bing (GPT-4 with search) was ~9 %—a marked improvement from grounding answers in live search. Copilot in Office likely hallucinates even less because it is anchored to your documents. If you ask, “Summarize the quarterly report,” it uses actual report content; if you ask something outside that scope, it may refuse or give a generic answer.

Claude

Claude’s safety training may reduce some forms of hallucination (particularly those that could be risky or harmful), and it often errs on the side of caution. One AI-safety evaluation found Claude 2 made slightly fewer factual errors than GPT-4 on some knowledge queries, though more in others—results are context-dependent. Claude is good at saying “I don’t know” when appropriate, a desirable trait.

In creative tasks, however, Claude can still invent plausible-sounding but fictional details. It sometimes invents citations when asked—perhaps a bit less often than ChatGPT, but it happens. Overall, Claude’s hallucination rate is comparable to ChatGPT’s, possibly a little lower in straightforward Q&A, yet still present. Claude shines at sticking to provided context: if you supply documents and ask questions about them, it rarely drifts beyond them, an advantage for legal or financial analyses.

Gemini

Google places strong emphasis on factual accuracy to protect search credibility. Gemini is heavily fine-tuned for correctness and integrates real-time retrieval, which inherently reduces hallucinations for knowledge questions. Internal benchmarks reportedly show big improvements in “factuality rate” over Bard’s earlier PaLM model.

When retrieval is disabled (purely conversational or creative prompts), Gemini can hallucinate like others. Google benefits from the Knowledge Graph: for short factoid questions Gemini often provides perfect answers, similar to classic search snippets. For complex reasoning it can still err—remember Bard’s early James Webb Space Telescope mistake.

Independent evaluations will clarify where Gemini Advanced stands, but expectations are that it will match or exceed GPT-4 on factual accuracy benchmarks like TruthfulQA. In vision tasks, hallucination may appear as mis-identifying objects; Google claims Gemini beats GPT-4 Vision on image-reasoning tests, so it may be less prone to “seeing” things that aren’t there.

DeepSeek

Without a big-tech fine-tuning pipeline, DeepSeek may show more raw LLM behavior. Its developers included alignment, but it likely underwent less reinforcement than commercial peers. DeepSeek offers a Reasoning Mode (R1) that outputs chain-of-thought; users can inspect the logic, catching mistakes early—a transparency advantage.

Quantitative truthfulness or toxicity scores aren’t yet published. With 14.8 trillion training tokens, DeepSeek absorbed vast facts—and potential falsehoods. The mixture-of-experts architecture could help: some experts specialize and answer more accurately. A risk is that if parts of DeepSeek were fine-tuned on GPT-4 outputs, it may inherit GPT-4’s biases and hallucinations.

Community feedback so far is positive: testers report DeepSeek’s reliability roughly in line with GPT-4, though without web retrieval or aggressive guardrails. Because it is free, enthusiasts continually probe it with tough questions; if it hallucinated egregiously, complaints would be widespread. So far, reports suggest it is solid—yet users should still verify critical facts, especially given lighter RLHF.

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8. User Satisfaction Metrics

ChatGPT

The sheer number of users (100 million in two months, now likely hundreds of millions total) speaks to its utility. Various surveys have been conducted: for example, a Pew Research survey found a majority of Americans who tried ChatGPT considered it “somewhat useful,” and about 15 % found it “very useful” in their tasks. On product-review sites, ChatGPT Plus receives high ratings for value. The flip side is concern about correctness—some respondents say they rely on it but do not fully trust every answer. OpenAI’s strategy of rapid iteration and improvement (releasing GPT-4 only three months after ChatGPT’s debut) kept satisfaction high among early adopters craving greater accuracy and fewer limitations. Another metric—retention and conversion—shows OpenAI had reportedly over two million ChatGPT Plus subscribers by the end of 2023, indicating users find enough value to pay. ChatGPT’s integration into apps (e.g., Snapchat’s MyAI uses GPT-3.5) also yields satisfaction data: Snap reported increased engagement thanks to the AI feature. Overall, ChatGPT enjoys a strong reputation when used with its known limits in mind. The main frustrations are occasional refusals (sometimes in error) and confidently given but subtly wrong answers; prompt-engineering guidelines and updates have reduced these issues.

Copilot (especially GitHub Copilot)

Developer surveys by GitHub and others show a large majority of developers using Copilot are satisfied. GitHub’s own survey reported 88 % felt more productive, 74 % said it helped them meet deadlines, and more than 90 % were satisfied with its code suggestions. A Stack Overflow 2023 survey likewise found Copilot the most-used AI coding tool, with many planning to keep using it. Key drivers of satisfaction: time saved on boilerplate and inspiration for solutions. Initial fears about introduced bugs eased after studies (for instance at ZoomInfo) showed code quality generally improved, thanks in part to best-practice suggestions. For Microsoft 365 Copilot, early pilot feedback has been positive—users praised rapid draft generation for documents and emails. Microsoft almost certainly tracks NPS for these features; expanding availability suggests it reached required satisfaction thresholds. Debate persists in the media about the $30 price tag—some argue ROI must be quantified before mass adoption. Bing Chat’s satisfaction had a roller-coaster start: excitement at launch, then the “Sydney” incident and stricter guardrails; today it is viewed as reliably useful for search, though perhaps less playful than ChatGPT.

Claude

Initially limited to US/UK access, Claude quickly earned praise for its long memory and detailed answers. On Reddit and similar forums, Claude 2 is regularly recommended for summarizing lengthy texts or when ChatGPT hits context limits. Users value its conversational tone and lower refusal rate (while still filtering disallowed content). Claude Pro’s slightly lower annual price than ChatGPT Plus is attractive, and built-in web search is a bonus. While it has not matched ChatGPT’s user numbers, many AI enthusiasts and professionals regard Claude highly. On Quora’s Poe platform, Claude 2 often wins user preference for lengthy, thoughtful responses. Some find it overly verbose (addressed by instructing brevity). An incident where users extracted Anthropic’s “constitution” was more curiosity than scandal. Anthropic’s ethical-AI focus appeals to enterprise buyers; major customers such as Slack and Notion adopting Claude indicates confidence in its performance.

Gemini / Bard

Bard’s rocky launch—highlighted by an early demo mistake—created skepticism, but user sentiment improved through 2023 as Gemini upgrades rolled out. Google reported higher engagement after adding images in answers and stronger coding help. Although still perceived by some as trailing ChatGPT in mindshare, Gemini Ultra’s arrival could close that gap. Integration with Google Assistant boosts day-to-day usefulness: early testers found the new Assistant with Gemini far better at multi-turn voice queries than the old version. Google said user-quality ratings nearly doubled after mid-2023 updates; Bard’s expansion to 180 countries also reflected performance confidence across languages. Real-time information is a clear advantage—Bard can answer “latest Oscar winners” accurately, whereas ChatGPT free cannot. Some users still view Bard as less creative, so Gemini’s future personality/creativity improvements will matter. Privacy perceptions influence satisfaction: some worry Google might use conversation data for ads, though Google says it does not and offers a data-saving opt-out.

DeepSeek

Because it is community-driven and free, early adopters are impressed at achieving near GPT-4-level answers without paying. This surprise factor fuels positive buzz (“hidden gem,” “ChatGPT competitor that’s free”). For the general public DeepSeek is still lesser-known; its user base is mostly AI enthusiasts and some Chinese users. Satisfaction comes from strong performance and zero cost. Possible friction points: as a newer service, interface stability and server load could pose issues—widespread complaints have not surfaced yet, but scaling will test reliability. Trust is mixed: some users hesitate to share data with a less-established provider, whereas others appreciate open-source transparency and self-hosting options. Developers integrating the API rave about cost/performance, since workloads that cost hundreds on OpenAI can cost only a few dollars on DeepSeek. There’s also a “cool factor” in supporting a non-big-tech contender. As long as DeepSeek maintains quality, documentation, and uptime, satisfaction should remain high within its niche, even if it doesn’t unseat ChatGPT for casual users.

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9. Strengths and Weaknesses

Finally, let’s distill where each of these AI systems excels and where they fall short in comparison to peers. This qualitative assessment highlights the unique advantages and caveats for Microsoft Copilot, ChatGPT, Claude, Gemini, and DeepSeek.

Microsoft Copilot (GitHub & 365)

Strengths

• Seamless integration into tools people already use daily.

• In coding, functions like an AI pair-programmer that works in real time as you write, boosting productivity and flow.

• Proven to speed up development and increase developer satisfaction.

• In Office apps, dramatically improves content creation, analysis, and communication—drafts emails, summarizes meetings, builds presentations with minimal effort.

• Uses GPT-4 with enterprise data, producing highly context-aware outputs (e.g., answers based on internal documents).

• Provides citations for factual info via Bing, building trust.

• Adheres to enterprise-grade security and privacy.

• Multimodal OS control: Windows Copilot can open apps, change settings, and bridge AI with system control.

Weaknesses

• Full potential comes at a high cost ($30 per user for Microsoft 365 Copilot), limiting access for individuals or small firms.

• Free offerings (Bing Chat) are powerful but somewhat constrained (Edge/Windows-only, conversation-length limits).

• Effectiveness is tied to the Microsoft ecosystem—less flexible for those outside Office, Teams, or Windows.

• Initial concerns about code suggestions containing verbatim training data; mitigations added but caution remains.

• Lacks deeper multi-step reasoning unless you switch to Copilot Chat and supply broader context.

• Requires internet/cloud access; offline, Copilot features don’t work.

• Occasional over-confidence drafting internal answers that might misinterpret data.

• Brand confusion (Copilot vs Copilot for 365 vs Copilot X) can puzzle users.

OpenAI ChatGPT (GPT-4)

Strengths

• Unmatched versatility and general intelligence: handles everything from fiction writing to complex code explanations.

• Strong step-by-step reasoning ability and coherent creative output.

• Remembers prior conversation context (within limits) and adapts style or tone.

• Large plugin ecosystem extends capabilities to external tools and real-time data.

• Simple, highly accessible user interface adopted by millions.

• Continuous updates improve safety and reduce errors.

• Unique Advanced Data Analysis lets ChatGPT run Python code for math or data tasks.

• Widely integrated via API—powering countless third-party apps.

Weaknesses

• Free tier limited by training cutoff; lacks real-time knowledge unless Plus browsing is enabled.

• Can still hallucinate; factual or high-stakes queries need verification.

• GPT-4 responses are slower and subject to throughput caps for heavy users.

• Occasional over-censoring or refusals on borderline requests.

• No native, automatic integration with personal files or enterprise data unless manually provided.

• GPT-4 access costs $20 per month; API or enterprise plans become expensive at scale.

Anthropic Claude 2

Strengths

• Massive 100 000-token context window handles entire books, long transcripts, or multi-document analysis in one go.

• Produces thorough, step-by-step explanations; ideal for complex topics and comprehensive answers.

• Emphasizes safety and alignment—polite, less toxic, fewer refusals on harmless queries.

• Consistently follows precise formatting instructions.

• Integrates with Google Workspace in Claude Pro; helpful for email and Docs workflows.

• Competitive pricing ($20 Pro with large context and web search).

• Anecdotally lower hallucination on detailed questions due to cautious self-checking.

Weaknesses

• Tends toward verbosity; must be prompted for brevity.

• Slightly weaker on niche knowledge than GPT-4; some coders prefer GPT-4 for edge-case optimization.

• Global availability still rolling out—smaller community, fewer plugins and public integrations.

• Not yet a broad plugin ecosystem like ChatGPT.

• Very large inputs can slow response time or incur high API cost.

• Output can become repetitive in long answers; needs editing for polish.

Google Gemini (Bard)

Strengths

• Fully multimodal: natively processes text, images, and audio; great for visual data and voice interaction.

• Real-time knowledge from Google Search, providing up-to-date answers with citations.

• Benefits from Google’s extensive multilingual, code, and image corpora.

• Deeply embedded in Google ecosystem—Search, Gmail, Docs, Android Assistant—offering in-context help.

• User-friendly UI elements (draft variations, “Google it” button) make AI accessible to mainstream users.

• Infrastructure scale keeps latency low even under heavy load.

• Gemini Ultra’s performance rivals or exceeds GPT-4 on several benchmarks; excels at text-plus-vision reasoning.

Weaknesses

• Early accuracy issues hurt initial trust; lingering caution among some users.

• Top-tier model (Gemini Ultra) not yet widely available—free users get slightly lower-tier model.

• Often more conservative/refusal-prone than ChatGPT.

• Developer ecosystem can feel fragmented (Vertex AI, Bard, AppSheet AI).

• Tiered access and regional rollout complicate adoption; mindshare still trails ChatGPT.

• Privacy worries about Google using conversation data, though opt-outs exist.

DeepSeek

Strengths

• Open, powerful, and inexpensive: GPT-4-level capability without high subscription fees.

• Open-access weights encourage transparency, self-hosting, and community innovation.

• 671-billion-parameter MoE architecture captures niche technical knowledge.

• Strong scores in math and reasoning; 64 K token context rivals larger commercial models.

• API pricing orders of magnitude cheaper than GPT-4—ideal for large-scale usage.

• Specialized variants (Coder, Math) excel on domain-specific tasks.

• Fewer policy filters allow flexibility for legitimate edge-case requests.

• High token-throughput generation makes interactions extremely fast.

Weaknesses

• Less battle-tested; fewer guardrails could mean odd behaviors in edge cases.

• Smaller support/documentation ecosystem; integration help limited versus Big Tech offerings.

• Long-term maintenance and free-tier continuity not guaranteed.

• Multilingual quality outside English/Chinese less proven.

• No native browsing, plugin store, or voice interface—DIY integration required.

• Self-hosting demands significant compute resources.

• Lower mainstream visibility; trust and adoption depend on community advocacy.

• Support channels and SLAs less mature than OpenAI or Microsoft.

Each AI assistant excels in distinct areas:

• Copilot leads in workplace integration and real-time developer productivity.

• ChatGPT remains the most versatile conversational powerhouse with unrivaled ecosystem breadth.

• Claude dominates for long-context reasoning and detailed, safe responses.

• Gemini offers multimodal strength and live data rooted in Google’s ecosystem.

• DeepSeek provides open-source power and unbeatable cost efficiency for advanced users.

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