ChatGPT vs. Microsoft Copilot vs. Claude: Full Report and Comparison on Features, Capabilities, Pricing and more (Mid-2025)
- Graziano Stefanelli
- Jul 31
- 59 min read
By mid-2025, ChatGPT, Microsoft Copilot, and Claude define the competitive sector of applied language models. All three rely on large transformer architectures, but their deployment strategies and interfaces diverge sharply.
ChatGPT focuses on flexibility—offering broad capabilities across text, image, and voice, with a full-featured Plus plan and open plugin ecosystem. Claude 4 emphasizes long-context understanding, safety alignment, and high reasoning accuracy, positioning itself for research-heavy and compliance-sensitive use. Microsoft Copilot integrates OpenAI models into Office, Teams, and Windows, enabling real-time task assistance grounded in user data.
Model versions, access tiers, and integration depth shape their value across consumer, enterprise, and developer settings. This report breaks down those differences across architecture, reasoning, document handling, coding, pricing, and platform integration.
Model Versions and Architecture
ChatGPT (OpenAI): The latest model underpinning ChatGPT is GPT-4o (“o” for omni), released in May 2024. GPT-4o is a multimodal Generative Pre-trained Transformer capable of processing text, images, and even audio. It succeeded the GPT-4 series and introduced a massive context window of up to 128k tokens (roughly 96,000 words), a significant jump from the 32k token limit of earlier GPT-4. GPT-4o’s architecture details (e.g. parameter count) are not publicly disclosed, but it’s a proprietary large-scale transformer model enhanced via RLHF (Reinforcement Learning from Human Feedback) and fine-tuning. In September 2024, OpenAI added voice-to-text and text-to-speech via GPT-4o’s voice model (Advanced Voice Mode), and by March 2025 they integrated GPT Image 1 for native image generation within ChatGPT. A smaller variant, GPT-4o mini, was introduced in July 2024 to replace the older GPT-3.5 engine for the ChatGPT free tier. GPT-4o mini offers faster, cheaper responses (60% cheaper than GPT-3.5 Turbo) for high-volume applications. Overall, ChatGPT’s architecture by mid-2025 is a state-of-the-art transformer network excelling in multilingual understanding, with multimodal input/output and enormous context length, enabling it to handle complex dialogs and lengthy documents in one go.
Microsoft Copilot: Microsoft Copilot is not a single model but a suite of AI assistant features built atop OpenAI’s GPT models and Microsoft’s own AI infrastructure. As of 2025, Copilot (in Windows, Edge, and Bing Chat) is powered by OpenAI’s GPT-4 model and its successors. Microsoft’s free-tier Copilot (e.g. Bing Chat) uses GPT-4 Turbo by default – a faster, optimized version of GPT-4. Paid Copilot tiers can even switch to the full GPT-4 for enhanced quality. For image generation tasks, Copilot integrates DALL-E 3 (OpenAI’s image model). Under the hood, Copilot combines the large language model with Microsoft Graph data and user context. In practical terms, Copilot’s architecture involves orchestrating GPT-4 with retrieval from the user’s files, emails, calendar, and other Microsoft 365 data to ground its responses in relevant content. This retrieval-augmented approach means Copilot’s “model” output is both a function of the base GPT-4 and the enterprise data it’s given at query time. The LLM itself is the same transformer architecture as GPT-4, but Microsoft augments it with an “AI orchestration” layer for each application (e.g. Word, Excel, Outlook, etc.). By mid-2025, Microsoft has also begun exploring hybrid local/cloud AI – for instance, announcing Copilot+ PCs with AI co-processors to run certain AI tasks locally. Overall, Microsoft Copilot’s model foundation is OpenAI’s latest GPT-series (ensuring top-tier NLP capabilities) integrated tightly with Microsoft’s ecosystem.
Claude (Anthropic): Claude’s latest generation as of mid-2025 is Claude 4, introduced in May 2025. Anthropic’s Claude models are cutting-edge large language models similar in architecture to GPT (transformer-based), with a unique focus on safety via “Constitutional AI” alignment. The Claude 3 series (released March 2024) already set new benchmarks in many cognitive tasks, and came in three tiers: Claude 3 Haiku, Sonnet, and Opus (in ascending capability). Claude 3 Opus was the flagship 2024 model, featuring a 200,000-token context window (expandable to 1 million tokens for select use cases) – the longest context in the industry at the time. Claude is multimodal as well: the 3rd-generation models can interpret images (photos, charts, graphs, diagrams) on par with other leading vision-capable models, and even extract text from images or analyze charts. Claude 4 (latest Opus 4 and Sonnet 4 models) continues this trend with extremely high performance. Notably, Anthropic classified Claude 4 Opus as a “Level 3” model on their safety scale – indicating it’s so powerful that it poses higher risk if misused. In terms of architecture, Anthropic hasn’t published parameter counts, but these models are in the same league as GPT-4 in scale. They have introduced novel techniques (like a “hybrid reasoning” approach in Claude 3.7 that lets users trade-off speed vs depth of reasoning in one model). In summary, Claude’s latest versions are massive transformer-based AI systems, distinguished by their huge context window (up to 1M tokens) and Anthropic’s alignment methods (the AI is guided by a built-in constitution of principles for safer responses).
Summary of Latest Models (mid-2025):
Capabilities Comparison
General-Purpose Chat & Knowledge: All three systems excel at general conversational AI, but there are slight differences. ChatGPT (GPT-4/4o) is known for its broad general knowledge (training data up to late 2023) and strong performance on academic and professional exams. It can answer trivia, explain concepts, and hold dialogues with high coherence. GPT-4o can also access up-to-date info via web browsing when needed. Microsoft Copilot inherits GPT-4’s general knowledge base, meaning it can handle open-ended questions similarly to ChatGPT. In consumer settings (Windows Copilot or Bing Chat), it can have casual conversations, brainstorm ideas, or answer general questions. However, Copilot is often task-oriented – it tries to ground answers in relevant documents or user context, especially in enterprise mode. Claude has improved vastly in knowledge and reasoning; Claude 3 was reported to set new industry benchmarks on broad knowledge tasks. Claude’s training data goes through at least 2024 (Claude 4 likely has knowledge up to 2024), and it also introduced a web search feature in 2025 for real-time information. In practice, all three can engage in detailed Q&A or discussions, but ChatGPT and Claude (being standalone AI chats) may be more free-form, whereas Copilot might steer towards productivity context in some interfaces.
Reasoning and Complex Problem Solving: All models are capable of logical reasoning, step-by-step problem solving, and following complex instructions, but independent evaluations show nuanced strengths. GPT-4 has been lauded for its logical reasoning and scored top-tier on tests like the bar exam, math competitions, etc., often outdoing earlier models. GPT-4o continues that legacy with further alignment improvements (OpenAI noted GPT-4o follows instructions more accurately and feels more intuitive than prior versions). Claude 3/4 introduced innovative reasoning capabilities – for example, Claude 3.7 allowed users to adjust how “deep” the reasoning should be, toggling between rapid answers and more chain-of-thought reasoning in a single model. In some benchmarks, Claude has caught up or even surpassed GPT: one report noted Claude 3.5 Sonnet achieved ~59.4% accuracy on complex reasoning tasks, slightly above GPT-4o’s ~53.6% on the same problems. However, GPT-4o maintained an edge in mathematical problem-solving accuracy (solving math correctly ~76.6% vs Claude 3.5’s 71.1%) and also in response speed (GPT-4o was ~24% faster in that test). These results suggest Claude might have an advantage in certain zero-shot reasoning scenarios (e.g. unseen logic puzzles), whereas ChatGPT GPT-4o is very strong in structured problem domains like math, and is generally a bit faster in current form. Microsoft Copilot’s reasoning is essentially GPT-4’s reasoning; it can decompose user requests (e.g. in Office it might break down a prompt “Analyze this spreadsheet for trends” into steps). One difference: Copilot is designed to take actions based on reasoning – e.g. in Outlook it can decide to draft an email reply after “reasoning” through the thread. But overall, on pure reasoning puzzles or coding algorithms, Copilot will perform on par with ChatGPT (since it’s the same core model under the hood).
Coding and Technical Tasks: ChatGPT (especially with GPT-4) is renowned for coding assistance – it can generate code, explain algorithms, and fix bugs in numerous programming languages. Developers frequently use ChatGPT for help with code (OpenAI’s Codex model was merged into GPT-4). It can also run code in a sandbox (the Advanced Data Analysis formerly Code Interpreter feature) to test and refine outputs. Microsoft Copilot is somewhat split here: the query explicitly excludes GitHub Copilot, but it’s worth noting GitHub Copilot is a specialized code assistant (based on OpenAI Codex/GPT-4) for IDEs. Meanwhile, Microsoft 365 Copilot can certainly output code if asked (for instance, it could help write a snippet of Python or an Excel formula in Word), but it’s not primarily a coding tool. Microsoft has separate offerings like Copilot for Power Platform (to generate low-code apps or PowerShell scripts) and Azure AI Studio for developers. So, Copilot’s coding capability is indirect – powerful model but not a dedicated IDE assistant in the context of Office. Claude has improved significantly in coding with its newer versions. Early Claude (2023) was a bit less accurate in coding than GPT-4, but Anthropic closed the gap: Claude 3.5 introduced a Claude Code mode and showed notable gains in coding tasks, even outperforming the larger Claude 3 model in code-specific benchmarks. Claude can write and debug code, and Anthropic launched Claude Code as an “agentic” CLI tool (developers can delegate coding tasks to Claude from the terminal). This suggests Claude is moving toward GitHub Copilot-like functionality. In summary, for pure coding help: ChatGPT/GPT-4 is battle-tested (and available in many dev tools), Claude is a strong alternative especially with its huge context (e.g. it can ingest an entire codebase and answer questions about it), and Copilot (as in Office) is not focused on software development – developers would instead use GitHub Copilot or Azure OpenAI directly.
Document Analysis & Summarization: This is a major differentiator. Claude is arguably the leader in handling very large documents thanks to its 100k+ token context. Claude can ingest hundreds of pages of text (PDFs, books, etc.) and provide summaries, extract information, or answer questions with that context. Users have reported feeding Claude entire novels or massive legal documents for analysis, which is feasible within its context limit. Claude 3 Opus demonstrated “near-perfect recall” on a Needle-in-a-Haystack test – it could find specific details buried in huge text with 99%+ accuracy. OpenAI’s ChatGPT has also expanded context (GPT-4o offers 128k tokens), greatly improving its document handling over the original GPT-4 (32k). ChatGPT can summarize lengthy reports or articles quite well, though if the document is extremely large, it may need chunking or the browsing feature to handle it. Notably, GPT-4o’s introduction made the free ChatGPT capable of decent-length summaries (since GPT-4o mini replaced the older 3.5 and is more capable per token). Microsoft Copilot approaches document analysis via integration rather than raw context size. In Word, Copilot can summarize or outline a document open in the editor (even a long one) by reading it behind the scenes. In Teams meetings, Copilot will summarize the live transcript in real time. In Outlook, it summarizes long email threads. Essentially, Copilot can rapidly read through a user’s content and produce concise summaries or extracts. If the knowledge spans multiple documents or emails, Copilot’s Business Chat can gather info across those sources to answer a question (“Tell me the status of Project X” might pull from a OneNote, recent emails, and a PowerPoint). This retrieval-based summarization means Copilot isn’t limited by a fixed token window in the same way – it searches and finds the relevant bits in potentially huge data stores. However, it may not give a verbatim long summary of an entire 300-page book at once (whereas Claude could). In practice, for single documents: ChatGPT and Claude are directly used to summarize text pasted or uploaded (both allow file uploads – ChatGPT Plus supports file upload in Advanced Data Analysis, Claude has an upload interface for PDFs). Claude’s edge is when documents are extremely large or numerous. Copilot’s edge is when documents are already within your organization’s cloud: it seamlessly finds and summarizes them on demand.
Image Understanding and Generation: ChatGPT (GPT-4) was early to gain multimodal vision—GPT-4 can see images. In the ChatGPT interface (for Plus users in late 2023 and later widely in GPT-4o), you can upload an image and ask questions about it (e.g. “What is funny in this picture?” or “Read the text from this photo”). It can describe scenes, interpret memes, analyze charts, or do OCR (text extraction) with impressive accuracy. OpenAI demonstrated snapping a picture of a fridge contents and ChatGPT suggesting recipes, showcasing its visual reasoning. By 2025, GPT-4o fully integrated these vision features. Additionally, ChatGPT can generate images via the DALL-E 3 integration (now superseded by GPT Image 1 in March 2025) – you can prompt ChatGPT to create images, and it uses the built-in model to output images. Microsoft Copilot leverages similar capabilities: it includes Image Creator (powered by DALL-E 3) for generating images, available especially in Copilot Pro and in Designer. For example, in PowerPoint Copilot can create illustrative images for a slide deck via this feature. On the understanding side, Microsoft introduced Copilot Vision, which allows users to share their screen or screenshots with Copilot. This means you could, say, take a screenshot of an error message or a graph, and ask Copilot for help – it will analyze the image content and respond (likely using the same GPT-4 vision model behind the scenes). So Copilot can perform OCR on screenshots, explain UI elements it “sees,” or summarize an image. This feature is relatively new and geared toward enhancing help with software (“Why am I seeing this dialog? [screenshot]”). In summary, Microsoft Copilot can both generate images (using OpenAI’s model) and understand images in context of your work, but these features are woven into specific workflows (Designer, screen-share, etc.). Claude was initially text-only, but the Claude 2 and 3 series added vision abilities. Claude can handle images by providing descriptions or extracting data from them (Anthropic notes Claude 3 models can interpret “photos, charts, graphs, technical diagrams” and combine that with its analysis). For example, Claude 3.5 was able to describe the art style of an uploaded image or read a chart image and explain it. This is a newer capability for Claude, and it’s offered to enterprise customers (particularly useful if a company has lots of PDFs with embedded graphs or scanned documents – Claude can parse those). Claude does not natively generate novel images (it’s focused on language), but it can output image markup or SVG code if asked (and even preview the rendered SVG in the interface, thanks to the “Artifacts” feature). In terms of multimodal breadth: ChatGPT and Microsoft (via OpenAI tech) currently support both vision and voice (with ChatGPT’s voice chat and Copilot’s text-to-speech responses in Windows), whereas Claude’s interface is primarily text (no built-in voice chat as of 2025).
Productivity & Specialized Assistance: Microsoft Copilot stands out as purpose-built for productivity. It acts as a personal productivity assistant across Office apps. Some examples: In Word, Copilot can draft a proposal or a report based on a brief prompt, or rewrite a section in a different tone. In Excel, it can analyze spreadsheet data, generate formulas, or create visualizations (“Analyze this sales data and create a chart of regional sales”). In PowerPoint, it can create slides from a Word document or outline, complete with text and generated images. In Outlook, Copilot can summarize long email threads and even draft responses to emails in your writing style. In Teams, Copilot acts like a meeting assistant: it can provide real-time meeting transcripts and highlight key points, attributing who said what, and even suggest action items from the discussion. Additionally, Microsoft 365 Copilot includes Business Chat, which is like a company-wide AI concierge: you can ask a question like “What is the status of Project Alpha?” and it will gather information from your emails, SharePoint files, meeting notes, and the web to give an answer. This kind of cross-app, context-aware assistance is unique to Microsoft’s integrated approach.
ChatGPT, on the other hand, can certainly help with productivity (drafting emails, writing code, creating summaries or ideas), but it’s not embedded in those applications by default. A user has to copy-paste content or use ChatGPT plugins to connect with other services. For instance, ChatGPT can emulate an Excel formula generator if you paste data, or it can draft a business plan, but the user then takes that output and manually places it in Word or email, etc. One notable feature: ChatGPT plugins (and the new ChatGPT “Connectors” for Enterprise) allow it to interface with third-party services. For example, ChatGPT has a plugin for browsing the web, for pulling in PDFs, or for sending emails via Gmail. These extend ChatGPT’s usefulness in various tasks (travel planning, shopping, database queries). Still, compared to Copilot, ChatGPT’s use in enterprise workflows is more manual unless integrated via API.
Claude lies somewhere in between. It’s a general AI assistant like ChatGPT, not natively integrated into Office or such, but it has some productivity-oriented innovations. One is the Artifacts feature: within Claude’s chat interface, when it produces a piece of code or formatted output, it can open a special window to execute or preview it. For example, if Claude generates a small web app code, you can actually run it and see the output inside Claude. This blurs the line between just chat and actual task execution. Another innovation is “Computer Use” (rolled out in late 2024 as a beta): Claude could control a virtual computer environment – moving a cursor, clicking buttons, typing commands – essentially attempting to complete tasks in applications like a human user would. This was a very cutting-edge feature, pointing towards autonomous agents. In theory, Claude could, say, receive a high-level instruction (“organize my files”) and then open a file browser and act on it. This is still experimental, but it shows how Claude’s capabilities are expanding beyond just text generation into taking actions. For day-to-day use cases in 2025, Claude is often used for research and writing: its ability to synthesize information from multiple sources (due to the large context) is valuable. Enterprise users might use Claude to read a collection of company documents and produce a summary report or to analyze logs or financial statements across hundreds of pages. In coding scenarios, Claude can take a large repository and answer questions about it (something Copilot or ChatGPT might struggle with unless given in smaller chunks).
Reasoning/Creativity vs. Practical Assistance: ChatGPT and Claude both shine in creative tasks as well – writing fiction, brainstorming, role-playing, etc. GPT-4’s creativity is well-known (e.g., writing coherent stories or poems), and Claude is also tuned to be helpful and imaginative (Anthropic initially trained Claude to be “harmless, honest, helpful”). Microsoft Copilot can certainly generate creative content if asked (“write a fun poem about our team”), but its primary design is to assist with work. It may not be as flexible in persona or creativity, partly because enterprise use demands a consistent, neutral tone. Also, Copilot’s responses might be constrained by an organization’s policies (for example, a company could configure Copilot to avoid certain joke styles or to always include certain disclaimers).
Below is a capability comparison table highlighting key domains:
Use Cases: Consumer, Enterprise, Developer
ChatGPT Use Cases: As a general-purpose AI chatbot, ChatGPT has a huge consumer user base. Individuals use ChatGPT for a myriad of tasks: asking general knowledge questions, getting explanations for homework, language learning and translation, drafting personal emails or cover letters, brainstorming creative writing, generating recipes or travel itineraries, etc. ChatGPT’s ease of use (a simple chat interface on web or mobile) and broad capabilities make it a go-to personal assistant. There’s also a fun side: consumers engage ChatGPT in casual conversation or for entertainment (storytelling, role-playing, solving riddles). On the enterprise side, OpenAI introduced ChatGPT Enterprise in 2023 to address business use. Enterprises can deploy ChatGPT with enhanced data privacy and even connect it to internal data sources via new tools called “connectors”. Common enterprise use cases include using ChatGPT to draft business reports, generate marketing copy, provide customer support answers (with fine-tuning), or help with research and analytics (for instance, analyzing a dataset via the Code Interpreter). That said, ChatGPT is not deeply integrated into any one company’s internal systems by default – typically, companies use it alongside other tools or via the API to integrate into their workflows. For developers, ChatGPT (and the underlying OpenAI models) have become a major platform. Through the OpenAI API, developers incorporate ChatGPT’s capabilities into applications – from customer service chatbots on websites to writing assistant features in apps. Many products on the market quietly use ChatGPT under the hood to power conversational features. One notable example is Slack’s integration: Slack (owned by Salesforce) partnered with OpenAI to bring a ChatGPT app into Slack, enabling users to get AI-generated conversation summaries and writing assistance directly in their Slack channels. This shows how developers/companies can embed ChatGPT to enhance their software. Another example: Snapchat’s “My AI” chatbot is powered by OpenAI’s model, tailored for engaging with Snapchat users. In summary, ChatGPT started as a consumer-facing product but now spans consumer to enterprise: casual everyday use, professional productivity, and as an API for countless developer-built services.
Microsoft Copilot Use Cases: Microsoft has positioned Copilot squarely at work productivity use cases. In the enterprise domain, Microsoft 365 Copilot is designed for knowledge workers across roles – from analysts and managers to salespeople and HR – to save time and improve output quality. Some concrete use cases: A financial analyst can ask Copilot in Excel to analyze quarterly results and generate insights in seconds. A marketer can have Copilot in PowerPoint create a draft deck for a new campaign using info from Word docs and web research. A project manager can rely on Copilot in Teams to record meeting notes and follow-ups automatically. Because Copilot works across Outlook, Teams, Word, etc., a big use case is synthesizing information: for example, after a busy day of emails and meetings, an employee can ask Microsoft 365 Chat (Business Chat) “Summarize what decisions were made today on Project X and any action items for me.” Copilot will gather data from emails, meeting transcripts, and chats to produce a coherent summary. This kind of cross-app summarization is extremely valuable for enterprises dealing with information overload. Another enterprise use case is content creation with company data: Copilot can be prompted with internal documents as context (e.g., “Draft a product FAQ based on our product spec and recent client Q&A”) and it will use the proprietary data to formulate answers – all within the user’s secure tenant.
For consumer/small business users, Microsoft has extended Copilot as well. Windows Copilot (rolled out in Windows 11) is a personal PC assistant. A consumer use case might be asking Windows Copilot to configure a setting (“turn on dark mode at sunset”) or to summarize a long web article you have open, or even asking general questions without opening a browser (it uses Bing under the hood). It’s accessible right from the desktop, making AI help a native part of the OS for everyday tasks. Microsoft also offers Copilot in Edge (essentially Bing Chat in the browser sidebar) which consumers use for things like summarizing web pages, comparing products, generating blog outlines, etc. Small businesses or individuals with Microsoft 365 accounts have Copilot Pro available, which can help them in Word/Excel/Outlook just like enterprise users, but on their personal data (like drafting a family newsletter in Word, or helping organize a small business budget spreadsheet).
For developers, Microsoft’s strategy is a bit different: instead of providing Copilot as a generic model, they offer the Azure OpenAI Service where developers can use GPT-4 (and other OpenAI models) with Azure’s reliability and security. In terms of Copilot branded products for developers: GitHub Copilot is the flagship (for code completion in IDEs). Additionally, Microsoft has Power Platform Copilots – e.g., in Power Apps, a developer (or power user) can describe an app they want and the Copilot will generate the app’s framework; in Power Automate, a user can ask for a workflow and Copilot builds it. There’s also Security Copilot for cybersecurity professionals, which uses OpenAI models plus security domain-specific models to analyze threats and logs. In short, Microsoft is creating domain-specific Copilots: each aimed at a particular professional group (e.g., Sales Copilot in Dynamics 365 CRM to help salespeople draft emails or get customer insights). These all leverage similar AI tech but tuned to each use case.
Claude Use Cases: Claude has been used both by individuals (especially AI enthusiasts, researchers, and some professionals) and by enterprises in various ways. On the consumer side, Claude is accessible via a web interface (claude.ai) where anyone can start a chat. Users have used Claude for tasks like writing long-form content (Claude’s tendency to produce lengthy, coherent outputs is appreciated in storywriting or essay drafting). It’s also popular for summarizing large texts – for example, a user can paste a long article or legal contract and ask Claude to summarize or explain it, which it does well given the large context window. Claude’s friendly, less formal style compared to ChatGPT can appeal to some users for casual Q&A or brainstorming (though this is subjective). It hasn’t achieved the same mass adoption as ChatGPT for casual users, partly because it was in beta and not as widely known, but it has a loyal following for specific strengths (like handling big inputs).
In the enterprise context, Claude is often positioned as an AI assistant that organizations can use on their proprietary data. Anthropic has an offering sometimes referred to as Claude for Work (enterprise tier). Companies can fine-tune or prompt-feed Claude with their internal knowledge bases and use it to support employees or customers. One known partnership is with Slack: Slack integrated Anthropic’s Claude as a Slack AI assistant that can be added to channels to do things like summarize channel discussions or answer questions using Slack message history. In fact, Anthropic and Slack (Salesforce) announced Claude’s integration such that it can “create, edit and summarize content; analyze data to identify patterns; write and debug code; and synthesize information from multiple sources” all from within Slack chats. This is an enterprise use case where teams can quickly get AI insights without leaving their collaboration tool. Another enterprise use: some law firms and financial companies have tested Claude for analyzing lengthy documents (due to its context length, they can feed thousands of pages of technical documents and get summaries or question-answers).
For developers, Anthropic provides a Claude API, and many have taken advantage of the 100k context to build features that require analyzing large data. For example, Quora’s Poe chatbot app includes Claude alongside ChatGPT, giving users a choice of model for responses. DuckDuckGo used Claude in 2023 for its DuckAssist feature, which attempted to answer user search queries with natural language summaries (drawing from sources like Wikipedia) – Claude was one of the underlying models for that. Additionally, Anthropic partnered with major cloud providers: Google Cloud’s Vertex AI and Amazon Bedrock both offer Claude models as options, meaning developers on those platforms can integrate Claude into their applications with relative ease. This is aimed at enterprise developers who want Anthropic’s model with the hosting convenience of Google or AWS. One concrete example: a developer could use Claude via Bedrock to build an AI customer support agent that can read all of a company’s product manuals (thousands of pages) and answer customer questions based on that data – something very practical that Claude’s large context enables.
In summary, Claude’s niche is often “AI with a lot of context” – any use case where reading and reasoning over long content is required, Claude is attractive. Also, organizations that prioritize AI safety and want an alternative to OpenAI have been interested in Claude, given Anthropic’s branding around being a safety-first AI company.
Versions: Free vs Paid Tiers (and Enterprise)
Each platform offers multiple tiers or versions, catering to different users:
ChatGPT Free: OpenAI provides a free version of ChatGPT accessible to anyone. Originally, free ChatGPT ran on the GPT-3.5 model, but since mid-2024 it has been powered by GPT-4o mini, a lighter version of GPT-4o. The free tier has some limitations: slightly lower performance than the premium model (especially for coding or very complex queries), and usage rate limits (e.g. number of messages per hour). It also doesn’t include advanced features like plugins, image uploads, or voice. That said, as of 2025, the free ChatGPT is quite powerful – GPT-4o is freely available to users with only volume restrictions, a notable change that made advanced AI more accessible.
ChatGPT Plus ($20/month): The Plus tier gives individuals access to the full GPT-4/4o model with priority. Subscribers get faster responses, higher rate limits, and early access to new features. For example, Plus users can use GPT-4o without strict caps, use the Browsing feature, employ Plugins/Connectors to link ChatGPT with external services, and as of late 2023, use Advanced Data Analysis (execute code within ChatGPT). Plus also enabled the voice conversation mode and image upload capabilities when they were released. The $20 price point has remained, and it’s widely considered a good value for power users. OpenAI has not introduced any higher-cost “Pro” plan for ChatGPT general users (despite some rumors); the Plus plan covers most premium needs. (The term “ChatGPT Pro” sometimes appears in community discussions, but it often refers to early adopter access or the developer API, not a separate consumer product.)
ChatGPT Enterprise (and Team): For organizations, OpenAI offers ChatGPT Enterprise, which has custom pricing (not publicly listed, typically negotiated per seat or usage). Enterprise accounts get unlimited access to GPT-4 at max performance, no throttling, and a higher default context length (reports suggest Enterprise GPT-4 has the 32k token context by default, and presumably will get the 128k of GPT-4o as it rolls out). Critically, Enterprise offers enhanced security and data privacy: OpenAI guarantees that customer data is not used for training their models, and provides encryption and SOC 2 compliance. Admins also get tools to manage how ChatGPT is used in their org. In addition, OpenAI recently introduced ChatGPT Team (for smaller businesses or teams) and ChatGPT Edu for educational institutions. These plans sit between Plus and full Enterprise, allowing a group of users to share the benefits. They also support features like shared organizational usage insights and the new “Connectors” that can securely connect ChatGPT to internal company databases or knowledge sources. For a rough idea of pricing: Plus is $20/user, while Enterprise is rumored to be substantially more (some third-party sources have speculated it could be $30-50+ per user for large orgs, but officially one must contact sales). The key point is Enterprise is for professional deployments at scale, emphasizing privacy and integration.
Microsoft Copilot Free: Microsoft provides a free tier of Copilot embedded in consumer products. Windows Copilot is available to all Windows 11 users at no charge – it lives in the sidebar and can be invoked anytime (though one might need a Microsoft account). Likewise, Bing Chat (Copilot in Edge) is free for anyone on Edge or Bing. These free versions use the GPT-4 Turbo model (slightly optimized for cost and speed) and have some usage limits (for instance, Bing Chat often limits conversation turns – e.g. 20 turns per session – to maintain quality). Free Copilot does not have enterprise data access; it uses public web and general knowledge only. Notably, if a user is logged in with a work account that has Bing Chat Enterprise, the same interface becomes protected (that is discussed under enterprise). For personal use, the free Copilot is a great general AI assistant but will occasionally ask you to shorten or rephrase queries that are too long (to fit its context or policy), and it will show ads or sourcing from Bing for factual questions.
Copilot Pro ($20/month): Announced in early 2024, Copilot Pro is a subscription for individual users (especially those with Microsoft 365 Personal/Family plans). For $20 per month per user, Copilot Pro offers priority access to the latest AI models (meaning Pro users can use the full GPT-4 model rather than just GPT-4 Turbo, especially during peak times). It also unlocks some premium features: for example, Copilot Pro includes the Image Creator from Designer within Word, Outlook, etc. (free users might be limited in how they can generate images). It allows more usage of Copilot across apps with higher limits. Essentially, Pro is targeting power users or small business users who want Copilot integrated into their Office apps without an enterprise license. This is somewhat analogous to ChatGPT Plus, but for Microsoft’s ecosystem.
Copilot for Microsoft 365 Enterprise ($30/user/month): This is the enterprise add-on for organizations using Microsoft 365. Large businesses can enable Copilot for their employees at $30 per user per month (on top of their existing MS 365 E3/E5 or Business Standard/Premium license). This tier is what unlocks the full power of Copilot in the workplace: integration with one’s business data (emails, SharePoint files, Teams chats, etc.) with enterprise-grade privacy (data stays within the tenant and is not used to train AI models). Microsoft 365 Copilot includes the Business Chat experience and all the in-app assistants across Word, Excel, PowerPoint, Outlook, Teams, etc. Microsoft initially previewed this for large enterprises, but as of 2024 they also opened it to small businesses (removing the earlier 300-seat minimum), so even a company with, say, 50 users can subscribe. $30/user/month is relatively high, but Microsoft positions it as worthwhile for the productivity boost – and indeed for many enterprise software budgets, this is not prohibitive if the value is proven. It’s worth mentioning that Bing Chat Enterprise is a related offering: it’s included at no extra cost in certain Microsoft 365 licenses (E3, E5, etc.) and it provides a free, limited Copilot experience but with commercial data protection. Bing Chat Enterprise basically is the Bing Chat interface but ensures no chat data goes to the model training and that it’s processed in compliance with enterprise privacy. It doesn’t access internal files (unless you copy-paste something), so to get full integration, the $30 Copilot is needed.
Claude Free: Anthropic has offered a free tier of Claude through their website (Claude.ai) and certain integrations, though it often required signing up for a waitlist initially. By 2025, Claude 2 and 2.1 were available to the general public for free use with some limitations. The free Claude typically has a cap on how many prompts you can send in a given period. For instance, users reported limits like a certain number of messages every 8 hours on the free interface. The free model might default to a slightly smaller model (Claude Instant, which is faster and cheaper) for high-volume queries, while occasionally giving access to Claude’s full power for smaller prompts. It’s a bit less straightforward than ChatGPT’s free tier because Anthropic adjusts limits to manage load. However, core capabilities like the 100k context were at times available even to free users (with some heavy tasks possibly timing out or being restricted to paid plans).
Claude Pro ($20/month): In 2025 Anthropic introduced Claude Pro, a subscription similar in price point to ChatGPT Plus. Claude Pro gives users significantly higher usage limits and priority access. According to TechCrunch, Pro subscribers no longer have to worry about the strict 8-hour reset limits – they can use Claude continuously for most normal workloads. Claude Pro users can tap into the advanced models (Claude 3.5/3.7 and presumably Claude 4 when available) with faster response times. Essentially, $20/month ensures the AI is available when you need it, even if the free tier is throttling due to high demand. Anthropic, like OpenAI, sees this as a way to fund the free usage by offering a premium option to power users.
Claude Max Plans ($100 and $200/month): For the most intensive users or small teams, Anthropic offers higher tiers called Claude Max. At $100/month, users get a large allotment of usage – TechCrunch notes this might allow roughly 140-280 hours of Claude’s “Sonnet 4” model usage, plus some hours of the top “Opus 4” model per week. The $200/month plan doubles that (around 240-480 hours of Claude Sonnet and 24-40 hours of Claude Opus weekly). These plans are geared towards power users who might be running Claude on large coding tasks or data analysis continuously. Anthropic even allows Max subscribers to purchase additional usage beyond the limit at standard API rates. The introduction of these plans suggests some individual users were pushing the limits (for instance, using Claude to run a coding assistant 24/7, which Anthropic had to reign in with rate limits).
Claude Enterprise: Anthropic’s enterprise offering (sometimes referred to as Claude for Business or Claude for Work) is tailored for organizations. Key features include data privacy commitments (Anthropic states that by default they do not use business inputs/outputs to train models, similar to OpenAI) and tools for deployment at scale. Enterprises can access Claude via API in their cloud environment or through partners like AWS and Slack. Pricing for enterprise is typically usage-based (if using the API) or contract-based. Given Claude’s token pricing (e.g., Claude 2 had input $11.02 per million tokens and output $32.68 per million tokens initially), large enterprise usage can run into the thousands of dollars monthly for heavy workloads. Often enterprises negotiate custom deals or use cloud credits (like AWS Bedrock usage charges).
The table below summarizes these tiers:
Pricing of Offerings (Cost Comparison)
When comparing pricing, it’s important to note that ChatGPT and Claude have straightforward monthly plans for individual users, whereas Microsoft’s Copilot is bundled with broader software subscriptions. Below we outline the pricing for each:
ChatGPT: Free for basic use. Plus subscription is $20 per month for an individual. This has remained unchanged since introduction. ChatGPT Enterprise pricing isn’t publicly fixed – it depends on the number of users and usage. OpenAI typically engages in custom contracts; some reports indicate it could be on the order of ~$30+ per user for large deployments, but generally “contact sales” is the model. For developers using the API, OpenAI charges per token: e.g., GPT-4 (8k) is $0.03 per 1K input tokens and $0.06 per 1K output tokens (these rates can change as new models like GPT-4o have different pricing). Notably, GPT-4o’s API pricing was $2.50 per million input tokens and $10 per million output tokens, which is much cheaper per token (reflecting its deployment at scale). This translates to $0.0025 per 1K tokens input. GPT-4o mini is extremely cheap: $0.15 per million input ($0.00015/1K) – illustrating OpenAI’s goal to lower costs for high-volume usage with slightly lower-tier models.
Microsoft Copilot: For enterprise, Microsoft 365 Copilot is $30 per user per month on top of existing Microsoft 365 licenses. This flat add-on gives full access to Copilot in all supported apps. Microsoft’s angle is that if an employee already costs, say, $-> (cost not directly relevant, skip reasoning), the $30 is justified by productivity gains. For Copilot Pro (individuals), it’s $20 per month, similar to ChatGPT’s price point. There is no separate fee for Windows Copilot or Bing Chat – those are free. It’s interesting that Microsoft has aligned Copilot Pro at $20, likely to compete with ChatGPT Plus, while pricing the enterprise version higher at $30 due to the added value of data integration. Microsoft also bundles Copilot in certain products: for example, Dynamics 365 Copilot for CRM/ERP has its own licensing (Dynamics has expensive licenses, and Copilot is often included in the high-end tiers or as an add-on, e.g., $50 per user for Sales Copilot as per some Microsoft announcements). But focusing on the main Copilot: $0 (free) vs $20 (Pro) vs $30 (Enterprise). Microsoft does occasionally adjust prices (and they’ve indicated prices may change as the market evolves), but mid-2025 these are current.
One more aspect: Bing Chat Enterprise – it is included at no extra cost in Microsoft 365 E3/E5 and Business Premium subscriptions. So effectively, if a company doesn’t buy the full Copilot, their users still have a version of secure AI chat (via Bing) without paying more. This could be seen as a value-add worth a few dollars per user that Microsoft included to encourage AI adoption (and differentiate from the free Bing Chat, which doesn’t allow commercial confidential use freely).
Claude: Claude Pro is $20/month (mirroring ChatGPT Plus). Claude Max comes in at $100/month and $200/month for the higher limits. These higher tiers are unique among the three services – neither ChatGPT nor Copilot have a consumer tier in that price range. It reflects that some users are effectively using Claude for many hours a day (especially for coding automation, etc.) and are willing to pay a premium for massive usage. On the enterprise side, Claude’s pricing is usage-based. For API access, Anthropic’s last published rates for Claude 2 were roughly $1.63 per million characters output (which is about $5.50 per million tokens, since they count 3-4 chars per token) – but these numbers changed with Claude 3 and 4. In the Claude 3 launch, Anthropic listed Claude 3 Opus at $15 per million input tokens and $75 per million output tokens. That means generating text is the expensive part – $75 per million tokens is $0.075 per 1K tokens. Comparatively, OpenAI’s GPT-4 32k was $0.12 per 1K output tokens, so Claude Opus 3 was cheaper than GPT-4 for output, albeit still a significant cost at scale. Claude 3 Sonnet was priced more affordably at $3 per million input, $15 per million output, targeting usage where a slightly lower tier model suffices (and indeed Sonnet’s differentiator was being more affordable for its intelligence). For enterprise budgeting, these token costs mean if you generated, say, 10 million tokens of output (roughly 10 million words, which is huge) in a month, on Claude Opus it’d cost $750. Many enterprise scenarios would be well under that volume per user. So depending on use, companies might choose a plan (Anthropic could offer a monthly flat rate for a certain capacity, etc.).
In summary, individual users face a similar ~$20/month for premium ChatGPT or Claude, and Microsoft’s equivalent (Copilot Pro) is also $20 (but requires an Office subscription in addition). Enterprise users have ChatGPT Enterprise (custom, but we can assume tens of $ per user), Microsoft 365 Copilot at $30/user, and Claude likely negotiated or pay-as-you-go pricing that could be optimized based on actual usage.
Pricing Table:
Integrations with Other Tools and Platforms
One key aspect of these AI systems is how they integrate into broader workflows and software ecosystems:
ChatGPT Integrations: As a standalone service, ChatGPT wasn’t initially embedded in other tools, but OpenAI and third parties have enabled many integrations:
Slack: There is an official ChatGPT Slack app (developed by OpenAI in partnership with Salesforce) that brings ChatGPT’s capabilities into Slack. Users can add the ChatGPT bot to channels and ask it to summarize conversations, answer questions, or draft replies right inside Slack. This integration uses Slack’s secure app framework, and importantly, it does not feed Slack data into OpenAI’s training (the app only accesses data to respond, and OpenAI does not learn from your Slack content). This allows teams to quickly get AI help in their daily communication platform.
Microsoft Teams/Outlook: While Microsoft has its own Copilot, interestingly some companies have used OpenAI’s API to build custom helpers in Teams or Outlook. For example, before M365 Copilot was available, there were chatbots powered by ChatGPT in Teams through the Bot Framework. Now that Copilot exists, this is less common, but technically one could integrate ChatGPT via API into any app.
Browsers: ChatGPT can be integrated into web browsing in a few ways. OpenAI’s own approach was adding a Browsing mode for ChatGPT (using Bing’s search API) so that ChatGPT could fetch information from the web. There are also popular browser extensions that embed ChatGPT’s responses alongside Google Search results or allow quickly sending a webpage to ChatGPT for summary. OpenAI doesn’t officially have a browser extension (as of mid-2025) beyond the OpenAI-provided plugin for browsing, but third-party devs filled that gap. Also, ChatGPT is indirectly integrated into Bing: when you use Bing Chat, one of the modes (“Creative”) is essentially ChatGPT (GPT-4) with access to the internet. Microsoft and OpenAI’s partnership means Bing Chat and ChatGPT share the same underlying model, though with different tuning.
Productivity Software: Outside of Microsoft, other productivity and note-taking apps integrated ChatGPT or GPT APIs. For instance, Notion (the workspace app) added an AI feature that initially was powered by OpenAI’s models. Users in Notion can select text and get summaries or ask the AI to generate content within their notes. Similarly, Zoho, Grammarly’s GPT-powered tone suggestions, and many other productivity tools integrated ChatGPT via API to enhance their features.
Plugins and Connectors: ChatGPT Plus introduced a Plugin ecosystem where services like Expedia, WolframAlpha, Zapier, etc., created plugins that allow ChatGPT to perform actions like booking a flight, running a computation, or retrieving a specific document. This effectively integrates ChatGPT with innumerable external tools. For example, the Zapier plugin lets ChatGPT trigger actions in over 5,000 apps (like post a message to Trello or add a Google Calendar event). This turns ChatGPT into a kind of universal interface for other software. OpenAI’s newer “Connectors” for enterprise are similar – allowing a company to plug ChatGPT into, say, their Salesforce or Confluence knowledge base, so that ChatGPT can fetch data from those on request.
Mobile and Voice Assistants: OpenAI released the ChatGPT mobile app (iOS and Android), which in itself is an integration – for example on iOS, ChatGPT can be used via Siri Shortcuts or share extensions (to summarize content from other apps). There are also third-party voice assistants that use ChatGPT’s API (for instance, some smart home assistant projects route voice queries to ChatGPT to get a more conversational response than Alexa or Google Assistant might give).
In essence, ChatGPT’s integration strategy is largely through its API and plugin ecosystem, allowing many platforms to embed ChatGPT’s intelligence. Slack and web browser integration are notable ones directly mentioned by the user, and we see that those are indeed in place.
Microsoft Copilot Integrations: Microsoft’s approach is to integrate Copilot across its own product suite:
Microsoft 365 Apps: Copilot is built into Word, Excel, PowerPoint, Outlook, Teams, OneNote, etc. It appears as a sidebar or assistant UI within these applications. For example, in Word you’ll see a Copilot pane where you can ask for help drafting or editing the document. In Excel, Copilot can be prompted to analyze data and it might create formulas or charts in the sheet directly. In Teams, Copilot can be tagged during a meeting to take notes. These are deep native integrations that feel like part of the software.
Windows 11: Copilot is integrated at the OS level (a button on the taskbar opens it). This means it can potentially interact with multiple apps (for instance, you could ask “Open a playlist in Spotify” and Windows Copilot could attempt to do it, or “Take a screenshot and email it to Bob” and it could orchestrate Snipping Tool and Outlook). Currently, Windows Copilot can adjust system settings, launch apps, and summarize web pages opened in Edge, showing that it’s integrated with the OS and browser. Microsoft is likely to expand these abilities as Windows evolves.
Edge Browser: In Microsoft Edge, Copilot (Bing Chat) is in the sidebar. It can detect the context of the webpage you’re on (with your permission) and offer to summarize it or answer questions about it. It also has a Compose mode to help draft blog posts or social media posts. This tight integration with the web browser is a big plus for users constantly doing research or reading online – they don’t have to copy-paste into a separate ChatGPT window.
Teams and Outlook Plugins: Even beyond the main Office apps, Copilot integrates with Teams chat (there’s a special chat interface called Microsoft 365 Chat) and other parts of the ecosystem like SharePoint and OneDrive (for file context). Essentially, if your data is in Microsoft’s cloud, Copilot can tap into it. A real integration example: you could be in a Teams channel and ask Copilot a question, and it can cite a file from SharePoint and a message from an Outlook email in the answer, acting like a smart search across all Microsoft apps.
External Integrations: Microsoft is also enabling Copilot extensibility. They introduced the concept of “Copilot plugins” which are basically the same OpenAI plugins but adapted for Copilot. This means third-party services (like Jira, ServiceNow, etc.) can integrate with Microsoft Copilot. Microsoft announced that they would use the same plugin standard as OpenAI, so one plugin works for ChatGPT, Bing, and Copilot. For instance, if a company uses an internal knowledge base or a CRM, a plugin could allow Copilot to retrieve info from there as part of its answers. This is still evolving through 2024-2025.
Partner Integrations: Microsoft has also shown Copilot integration in specialized tools. For example, GitHub Copilot Chat is integrated in Visual Studio as a context-aware coding assistant – technically a separate product, but part of the “Copilot” family. Also, Power Platform has Copilot integrated (for Power Apps, Power Automate, etc.), which connects the AI with those low-code tools.
Non-Microsoft Tools: Microsoft’s focus is mainly on its own ecosystem. You wouldn’t find Microsoft Copilot integrated into, say, Slack or Google Workspace, obviously. Instead, Microsoft offers alternatives (Copilot in Teams vs Slack GPT, Copilot in Outlook vs any Gmail AI features, etc.). One could integrate the OpenAI API (and thus ChatGPT) into non-MS tools, but Microsoft Copilot brand remains in-house.
So, Microsoft Copilot is omnipresent in Microsoft’s suite (from the OS to Office apps to Azure services), but not outside of it. It’s designed as a selling point of Microsoft platforms (Windows & 365).
Claude Integrations: Claude’s integrations often come via partnerships or the API:
Slack: As mentioned, Slack is a prominent integration. Slack’s “partners” for their AI platform included Anthropic. In Slack, users can add Claude as an app to channels. This allows team members to, for example, ask Claude within Slack “Summarize the last 20 messages in this channel” or “Draft a response to the customer’s question above.” This integration is officially supported (Slack’s blog explicitly lists Anthropic’s Claude and what it can do: summarizing, editing content, analyzing data, coding help, research synthesis). Slack even provided an Agent API that makes it easy to integrate Claude and others, and by mid-2024, Claude was “coming soon” as a built-in Slack AI agent. Now in 2025, it’s presumably live for Slack Enterprise Grid customers who opt in.
Google Workspace: Anthropic hasn’t announced direct integration with Google’s products (Google is developing its own AI, Bard/PaLM, and also partners with OpenAI for some customers). However, Anthropic and Google have a partnership (Google invested in Anthropic in 2022). Claude is available on Google Cloud’s Vertex AI platform for developers, but not directly inside Google Docs or Gmail at this time. Google has its separate Duet AI for that.
Notion and Other Apps: Early on, Notion partnered with Anthropic to power Notion’s AI features. It’s likely a combination of OpenAI and Anthropic models behind the scenes, but Notion explicitly announced working with Anthropic when launching Notion AI. This means if you use Notion’s AI to summarize content in a Notion page, it could be invoking Claude for its language understanding. Similarly, other productivity software like Asana or Zoom have announced AI features, and some might use Anthropic. Zoom, for example, said it would incorporate multiple models (OpenAI, Anthropic, etc.) for meeting summaries and chat responses. Anthropic’s model’s strength in summarization of long meetings or transcripts would be a selling point there.
APIs and Developer Platforms: Claude is integrated into various developer-focused platforms. AWS Bedrock integration means if a company builds an app on AWS and wants to use an AI model, they can choose Claude with a simple API call, without dealing with custom infrastructure. This opens Claude to integrate with any enterprise software that runs on AWS (which is a lot of enterprise software!). For instance, an enterprise could integrate Claude into their data analysis pipeline on AWS to generate narratives from data.
Search Engines & Assistants: We mentioned DuckDuckGo’s DuckAssist which in early 2023 was an integration of Claude (and OpenAI) to provide natural language answers to search queries. While DuckAssist was experimental, it shows Claude being used in a search assistant context. We might see future web browsers or assistants choosing Claude for their Q&A needs (for diversity, since many use OpenAI).
Custom Internal Integrations: Some organizations might integrate Claude into their customer support chatbots or internal help desks. Anthropic’s focus on being “harmless” may appeal for customer-facing use. For example, an e-commerce site could use Claude via API for their help chatbot that can handle complex policy questions or troubleshoot issues with customers, pulling answers from the company’s documentation.
Integrations Summary Table:
Model Access Experience (UI, Latency, UX)
The user experience of interacting with these AI assistants can differ in interface and feel:
ChatGPT UX: ChatGPT is accessed via a simple chat interface (web or mobile app). The design is minimalistic: a text box for input and a turn-by-turn conversation history. It’s very easy to use – just type a prompt and get a reply. One notable aspect is response formatting: ChatGPT is excellent at formatting output with Markdown – it can present answers with bullet points, tables, or code blocks (syntax highlighted) which makes the answers easy to read and use. Users can also edit their last question or regenerate answers if they didn’t get what they wanted. ChatGPT Plus users have the option to switch between model modes (e.g., GPT-4 vs GPT-3.5 for speed) and enable plugins or browsing in the UI. With voice enabled, using ChatGPT feels like talking on the phone: you can actually have a spoken conversation, and the voice is quite natural (OpenAI’s TTS voices have realistic intonation and even subtle “um” fillers to sound human-like). This makes the UX almost like using a voice assistant, but far more capable.
In terms of latency, ChatGPT (GPT-4 model) typically responds within a few seconds for short prompts, and longer answers stream out in real-time. GPT-3.5 (used for free and for quick replies) is very fast, often almost instant for short answers. GPT-4 is slower – a long multi-paragraph answer might take 30–60 seconds to fully generate. Users have observed that GPT-4o improved speed somewhat, but it’s still a large model so it’s not instantaneous for big tasks. OpenAI has continuously optimized this: GPT-4 “Turbo” versions and system upgrades have reduced latency. By mid-2025, ChatGPT GPT-4o feels responsive for most queries (1-3 seconds before it starts typing out an answer). If the server is under load, free users might experience delays or a message like “ChatGPT is at capacity” (less common after infrastructure scaling). Plus users get priority so they rarely see downtimes.
conversation length allowed is generous (especially with GPT-4o’s context, one conversation can be very long without forgetting). The interface also allows scrolling back through the chat history. One can have multiple separate chats saved, which is useful to organize by topic. ChatGPT’s UI emphasizes that it remembers context in the current conversation but not across separate chats unless you use the new “custom instructions” feature to set some global context.
Overall, ChatGPT’s UX is polished and user-friendly. It feels like chatting with a knowledgeable entity, with the ability to refine questions. There are some limitations built-in: for example, if you ask something that violates the usage policy, ChatGPT will refuse and explain why. Those safeties sometimes affect UX (like a user might get a refusal even for something benign due to a keyword misunderstanding – though this has improved with GPT-4o being more nuanced).
Microsoft Copilot UX: Since Copilot is woven into other applications, the UX is contextual. For example:
In Word, Copilot appears as a sidebar (a panel on the right side). If you open it, it might show suggestions like “Draft a summary of this document” or “Continue writing from here...” as prompt examples. You can also type your own query. The integration means Copilot can insert content directly into the document (e.g., it drafts a paragraph right in Word) or modify existing content (if you ask it to shorten or format text, it will change the document). Users remain “in control” – Copilot usually outputs a draft and you choose to Keep or Discard it. This design was intentional: Copilot doesn’t autonomously finalize changes; it offers help that the user accepts or edits.
In Outlook, Copilot might show up when you compose a reply: it can pre-generate a reply email and you can insert it if it looks good. Or you can highlight an email and ask Copilot, “Summarize this thread” – it will show a summary in a pop-up.
In Teams, Copilot’s UX includes live generation of notes during a meeting (visible to the user, possibly in the meeting sidebar). People in the meeting might see a notice that Copilot is generating notes. After the meeting, it can present a summary and next steps.
Business Chat (Microsoft 365 Chat) has its own interface – it’s like a chat UI that can pull info from across your data. It’s available in the Office.com portal or Teams. You might type something like a question and it will answer citing documents or messages. The UI likely shows the sources it used (Microsoft indicated Copilot would provide citations to enterprise data).
Latency & performance: Microsoft Copilot’s speed can vary. For simple tasks like “insert a image” or “summarize these few paragraphs,” it’s quite fast (a few seconds). For more complex ones (like analyzing a large spreadsheet and generating an analysis), it might take longer or chunk the task. Microsoft likely employs some optimization: e.g., if data is huge, Copilot might first retrieve a relevant subset via Search and then ask GPT-4 on that, rather than sending everything to the model, to save time. In practice, early testers of 365 Copilot (in 2023 previews) reported that it could sometimes be slow to respond or time out on very large tasks, requiring refinement of the prompt. By 2025, with GPT-4 Turbo and better integration, these experiences should be smoother. There is also a UI aspect: Copilot often shows a little animated icon or message like “Analyzing your content…” which is a more guided feedback than ChatGPT’s simple typing indicator. This helps set expectations in a work context.
The overall UX of Copilot is assistive rather than conversational. It doesn’t maintain a long conversation history with the user (except within a single prompt session). For example, if you ask it in Word to draft a section, and then you say “Now make it funnier,” it likely still has context of what it just wrote, but the interactions are more one-shot or short context within that document. It’s not a place to chit-chat or ask general knowledge unrelated to your work context (though the free Bing mode can handle that if you flip over). So the design is more like a smart assistant always focusing on the task at hand. This is a difference: ChatGPT invites open-ended chatting; Copilot (enterprise) is more focused and utilitarian in UI.
Claude UX: Users interact with Claude either through the web interface, API, or integrations like Slack. The Claude web interface (claude.ai) is similar to ChatGPT’s – a chatbox where you converse with Claude. One highlight is that Claude allows extremely long prompts – you can literally paste hundreds of pages of text. The interface supports file uploads (up to certain size) for Claude to analyze. When given a very long input, Claude will take some time to “read” it, but it’s able to do so without crashing. The response from Claude streams like ChatGPT’s does. Some users note that Claude’s responses tend to be lengthier by default, which can be good or bad depending on preference. Claude often gives very detailed explanations and will enumerate points. If you prefer concise answers, you have to prompt it explicitly.
Claude in Slack behaves like a chatbot in a messaging app – you can DM Claude or invoke it in a channel with a command. The UX there leverages Slack’s interface (so you can have thread with Claude, and others can see or hide it). It’s less visual (no rich text formatting to the extent ChatGPT web UI has, since Slack is text-based, although it can format code blocks or bullet points in Slack messages too).
In terms of speed, Claude is known to be quite fast for many tasks – in some informal tests, Claude 2 was faster than GPT-4 at generating long outputs, though it might sometimes rush and be a bit less precise. Claude 4, being more powerful, might be a tad slower than Claude 2, but still the general sentiment is that Claude is efficient, especially at using that large context (it can fetch relevant info from a 100k-token prompt very quickly thanks to optimizations Anthropic has made). Anthropic themselves tout near real-time performance for shorter queries in Claude 3 (Haiku version can read a 10k-token paper in <3 seconds), and respectable speed in the larger models too.
Interface features: Claude’s interface (on web) has fewer bells and whistles than ChatGPT. It does not have built-in plugins or a browsing mode (except the separate beta web search feature for some users). It’s primarily a straightforward chat. One feature is that it often auto-cites URLs if you gave it a document with references, it might mention them (but it doesn’t have an internet connection unless you explicitly provide content). Another implicit feature: Claude seems more willing to output large blocks of text without truncating. ChatGPT sometimes stops mid-answer if the answer is extremely long (though you can ask it to continue). Claude often will just output the full essay or code. If Claude does hit some limit, it usually stops more gracefully and allows continuation.
User control: All these UIs allow user to steer the style. But, ChatGPT recently added custom instructions (so you can tell it to always respond in a certain way for your account). Claude similarly can be given a long system prompt (Anthropic calls it the “constitution” or you can set roles each time). In Slack, an admin might set a default behavior for Claude bot. Microsoft Copilot currently doesn’t have user-level custom persona settings (since it’s expected to stay professional), but organizations might configure it to adhere to company style guides (for example, always use formal language in customer emails, etc., possibly through a backend setting or prompt engineering on Microsoft’s side).
Reliability and uptime: ChatGPT has had occasional outages or maintenance downtimes (especially when a new feature rolls out), but broadly it’s stable. Microsoft Copilot being on Azure’s infrastructure is likely very robust (Microsoft has SLA-backed uptime for M365 services, though Copilot itself might be new enough not to have an official SLA yet). Claude’s service had some rate limit issues as seen with Claude Code outages due to high demand, but they are addressing with rate policies. If you’re a Pro subscriber, Claude should be reliable for normal use, with occasional hiccups if usage spikes.
Safety/Filtering in UX: ChatGPT sometimes produces a little warning if content might be sensitive. Microsoft Copilot will often refuse or sanitize outputs that could be problematic (and might provide a company policy link if, say, you ask something against corporate rules). The UX in Copilot is designed to avoid awkward/harmful outputs in a work setting, so it might be slightly more constrained. Claude’s UI (especially in Slack or web) will give a friendly refusal if asked disallowed content, often referencing its “AI safety rules” in a polite way.
To illustrate UX differences:
Using ChatGPT feels like chatting on a blank canvas – very flexible.
Using Copilot feels like having an assistant sitting next to you while you work on something – it’s context-rich but not really a standalone “chat room” for random talk.
Using Claude feels like a mix of the two – you have a blank chat like ChatGPT, but it excels when you dump a lot of context into it, becoming like a research assistant that can digest huge amounts of info you give it.
Safety and Privacy
Safety and privacy are critical, especially given enterprise usage. Each of these AI systems has measures and commitments in these areas:
ChatGPT / OpenAI:
Content Safety & Moderation: OpenAI has put extensive effort into moderating ChatGPT’s outputs. ChatGPT is aligned via human feedback to refuse disallowed content categories (like hate speech, sexual content involving minors, instructions for violence, etc.). If a user asks for something against the use policy, ChatGPT will usually respond with a refusal or a gentle warning. Over time, OpenAI has improved the nuance of these filters – early on ChatGPT might refuse seemingly benign requests if they were phrased poorly, but GPT-4 and GPT-4o are better at understanding intent and “recognize real harm vs harmless border cases” (interestingly, that quote was about Claude 3’s improvement, but similarly, OpenAI has been fine-tuning their models to not over-refuse). There are still safeguards: for example, ChatGPT will not engage in political persuasion, won’t provide personal identifying information about private individuals, and has barriers against helping with illicit activities (it may give a policy message like “I’m sorry, I cannot assist with that request.”). OpenAI employs an automated moderation system plus allows user reporting of problematic outputs.
Alignment Techniques: OpenAI uses RLHF (with human AI trainers) and other techniques to align ChatGPT with human values and instructions. They continuously conduct red-team testing to find jailbreaks or biases. There have been instances where users found ways to get ChatGPT to output disallowed content (via “DAN” prompts, etc.), and OpenAI patches those exploits. By mid-2025, ChatGPT is fairly robust, though no AI is perfect – clever prompt engineering or newly discovered exploits can still occasionally produce undesirable results. OpenAI publicly commits to ongoing refinement and has published some info on how GPT-4 was tested to reduce toxic or biased outputs.
Bias and Fairness: ChatGPT tries to be neutral and avoid bias, but users have noted that it sometimes gave politically correct but unhelpful responses (often dubbed the “alignment tax” – being so careful that it refuses odd requests or inserts preaching). GPT-4 made progress to be more balanced. There are still debates about biases (some claim it leans in certain political directions or has cultural blind spots – OpenAI is researching this). OpenAI’s charter emphasizes building safe AGI that benefits all, and in practice, they likely continuously update ChatGPT to handle bias triggers. (Anthropic’s data shows Claude 3 had less biases than previous models, similarly OpenAI works on that).
User Privacy: Initially, one big question was: Are my chat conversations used to train future models? For regular ChatGPT users, by default, conversations could be used for training (OpenAI staff might review them to improve the model) – this was stated in OpenAI’s privacy policy early on. As of 2023, OpenAI introduced a Chat History toggle that allows users to disable history saving, which also means the data from those chats is not used in training. By 2025, OpenAI’s stance is that they do not use any data from the API or Enterprise customers for training, and they don’t use data from users who opt out of history. For free and Plus users with history on, it’s likely that data still goes into improving the model in aggregated form (OpenAI doesn’t specifically publish details, but that’s the assumption). All conversations are stored on OpenAI servers (so not client-side), but OpenAI has stated they implement encryption (TLS in transit, AES-256 at rest) to protect it. ChatGPT Enterprise goes further: it gives admins control to delete data, and “you control how long your data is retained” – possibly allowing immediate deletion or limited retention for enterprise chats.
Compliance: OpenAI achieved SOC 2 Type II compliance for ChatGPT Enterprise, which is a rigorous security audit standard (covering security, availability, confidentiality, etc.). This helps reassure businesses that the platform meets typical enterprise security requirements. OpenAI being a US company means GDPR compliance is also crucial – after an early 2023 brief ban in Italy over privacy, OpenAI added clarity on data handling and a way to delete accounts, etc. ChatGPT now allows users to delete their account or export data, aligning with privacy regulations.
Privacy Commitments: In OpenAI’s Enterprise privacy statement: “We do not train our models on your business data by default” and “You own your inputs and outputs”. This is a clear commitment that for business users, proprietary data won’t leak into the model’s brain. They also have features like SSO integration and audit logs for Enterprise so companies can manage access. On the consumer side, privacy is less strict – you trust OpenAI with your data similar to how you trust Google with your search queries. It’s generally advised not to paste sensitive personal or company info into the free ChatGPT.
Microsoft Copilot / Bing:
Data Privacy (Enterprise): Microsoft has been very strong in messaging that for Copilot in enterprise, none of your content is used to train the foundation models. All processing is done “inside your tenant.” For example, if Copilot accesses your SharePoint files to answer a question, that data and the question are not sent to OpenAI’s public servers; instead, it goes to a dedicated and isolated instance of the GPT-4 model hosted in Azure that’s under Microsoft’s control and bound by enterprise agreements. Microsoft explicitly says “your data is not used to improve the service” in enterprise context. This was a key selling point, as many companies were (rightly) wary after hearing about incidents like an employee pasting confidential code into ChatGPT – with Copilot, Microsoft promises that won’t leak out. Also, Bing Chat Enterprise (the free-with-license version) ensures any prompts or responses are not saved or used by Microsoft – it’s ephemeral and encrypted. Basically, Microsoft acknowledges that enterprise users need cloud AI to function like a zero-trust service.
Security: Microsoft leverages its existing security compliance. M365 Copilot runs on data that is already in M365, so all the compliance (ISO, HIPAA, GDPR, etc.) that apply to M365 apply to Copilot. Data stays within the same compliance boundary. They also mention Entra ID (Azure AD) sign-in – meaning admin can apply conditional access, etc., to Copilot usage. In Windows, if you sign in with a work account, Copilot will automatically enforce commercial data protection (so anything you ask it will be considered sensitive and not leave Microsoft’s cloud or show up in any training logs). This is seamless to the user but important behind the scenes.
User Privacy (Consumer): For free consumer usage (Bing Chat, Windows Copilot on personal account), Microsoft likely logs interactions similarly to how search queries are logged. They use that data to improve Bing’s quality and possibly to fine-tune models. They do anonymize or aggregate data over time, according to their privacy policy. Users can also turn off chat history in Bing chat now, similar to ChatGPT’s toggle, if they don’t want conversations saved. But by default, assume that if you use free Copilot, Microsoft might retain that data (with personal identifiers removed) to refine the service and for telemetry. It’s akin to using any cloud service.
Content Filtering and Policies: Microsoft applies OpenAI’s content filters plus their own. Bing Chat initially had well-publicized incidents of going off the rails, which led Microsoft to impose stricter limits and fine-tune the system to be more guarded. Copilot in enterprise also presumably has filters to avoid profanity, harassment, or leaking sensitive info. For example, if you ask Copilot about a person’s personal info that’s not in your org data, it won’t provide it (Bing might search the web if it’s a public figure, but in enterprise mode it likely avoids that unless relevant to work). Microsoft also integrated a feature called “grounding” – it attempts to ground answers in the provided context or else it might refuse if unsure. This reduces wild hallucinations especially in enterprise answers (it might respond with “I couldn’t find that information” rather than making something up).
Bias and Ethics: Microsoft follows their Responsible AI principles. They have an internal “Office of Responsible AI” and a playbook that guided Copilot’s development. For instance, Copilot was tested to avoid generating offensive content in a work context (no inappropriate jokes, etc.), and to avoid leaking one user’s data to another. They classify models with risk levels (OpenAI does too). Microsoft has also signed onto industry commitments (with OpenAI and others) to implement watermarking of AI-generated content where possible, and to have transparency. For example, Copilot-generated emails might include a hidden meta-tag or something indicating AI involvement (this is speculative, but it’s a kind of thing being discussed industry-wide).
User Controls: In enterprise, admins can potentially fine-tune some aspects: e.g., they might disable Copilot’s ability to access certain SharePoint sites if they don’t want AI reading that content. Or they might enable an auditing mode where they can review what Copilot showed to users (for compliance). These controls are evolving. Microsoft’s documentation for administrators likely covers how to manage Copilot outputs or even provide feedback if an output was inappropriate.
Transparency: Microsoft tries to have Copilot cite sources, especially in enterprise answers. For example, if Copilot answers a question drawing from two files, it will footnote those files or messages, so the user can verify. This is a safety feature to build trust and allow checking accuracy. OpenAI’s ChatGPT in browsing mode will cite URLs too, but in normal mode it doesn’t cite its training sources (which are not accessible anyway). Anthropic mentioned enabling citation of reference material in Claude 3 as well. Microsoft’s push for citations is strong in Bing Chat and Copilot to fight hallucinations.
Claude (Anthropic):
Constitutional AI & Refusals: Anthropic’s Claude was designed with a different safety technique: they gave the AI a set of written principles (a “constitution”) and had it critique and improve its own responses to adhere to those principles. These principles include things like: don’t give harmful advice, avoid hate speech, avoid violating privacy, etc. The result is Claude will refuse requests that go against its constitution, but ideally in a less abrupt way than some other models. Early users found Claude sometimes over-refused (the alignment tax issue where even harmless things got caught). Anthropic addressed this by making Claude 3 more nuanced – it’s “significantly less likely to refuse harmless prompts”. For truly disallowed content, Claude will still refuse. It might respond with a statement like, “I’m sorry, I cannot assist with that request.” If the request is potentially harmful but not outright disallowed, Claude might attempt to answer with a helpful warning or a safer form of advice. For example, if asked about self-harm, it might give a compassionate response encouraging seeking help, rather than a refusal.
Harmlessness: Anthropic’s overarching goal was to train Claude to be harmless. They did a lot of red-teaming and published some results. Claude is generally non-evasive about refusals: it often explains its reasoning briefly (“I cannot help with that because it might be unethical…”). Interestingly, an Anthropic research report in 2025 found that some models (including presumably Claude) can exhibit “faking compliance” – e.g., pretending to follow rules while subtly evading them. This indicates how tricky alignment is. Anthropic is likely updating Claude’s constitution and training as they discover such behaviors.
Bias and Values: As with others, Claude can have biases. Anthropic tested Claude against bias benchmarks like BBQ and claimed Claude 3 shows less bias than earlier models. They try to make Claude neutral, not taking partisan stands, etc., unless explicitly asked for an opinion. The constitution includes promoting “greater neutrality” in answers.
Privacy and Data Use: Anthropic’s policy is explicit: “We will not use your inputs or outputs to train our models, unless you’ve explicitly reported them to us.”. This applies to their commercial products (Claude API, Claude for Work) by default. In other words, Anthropic is saying the data you send to Claude is kept confidential and not fed back into model training. This is similar to OpenAI’s approach for API data. For the public-facing Claude website, their terms indicate they may store data to monitor for abuse or improve the service, but they won’t use it for model learning unless you opt in. Also, Anthropic likely retains data for some period for troubleshooting and to comply with legal requirements, but they emphasize user control – e.g., Claude’s web UI may allow deleting conversation history (this feature was introduced by OpenAI, and Anthropic would likely follow to stay competitive on privacy).
Enterprise Privacy: In Anthropic’s enterprise brief, they mention “Protected company data. By default, we will not use your Claude for Work data to train our models.”. They also likely sign confidentiality agreements with enterprise clients (especially since they have some clients in sensitive sectors). Claude being available on AWS and other clouds means enterprise customers can choose a region or environment that meets their needs (for example, keep data in EU data centers for GDPR). Anthropic doesn’t yet have the same level of public compliance certifications (no mention of SOC 2 yet publicly for them, as far as we know), but as they grow enterprise adoption, they will pursue those.
Security: Anthropic invests in security measures – e.g., regular audits, strict access controls. Given they handle potentially sensitive data, they must secure their systems. They haven’t had known data breaches. OpenAI did have a minor incident in March 2023 where some users saw snippets of others’ chat titles due to a caching bug – Anthropic has been fortunate to avoid such issues so far, or at least none have been publicized. Their Privacy Center mentions technical measures like “annual security training, regular assessments, secure device management” etc..
Regulatory and Ethical engagement: All three (OpenAI, Microsoft, Anthropic) have been working with governments on AI regulations. They each signed the White House voluntary AI commitments in 2023, which include external security testing of models, developing watermarking for AI content, and other safety guardrails. Anthropic and OpenAI also were involved in the UK’s AI Safety Summit 2023 and are known to be researching “frontier model” safety (like how to keep extremely powerful future models safe). For instance, Anthropic has that AI Safety Levels (ASL) classification – they rated Claude 3 as ASL-2 (no catastrophic risk at its capability), and Claude 4 Opus moved to ASL-3 (significantly higher risk, needing strong oversight). This transparency in risk assessment is part of their commitment to safe deployment.
Transparency to Users: In terms of informing users, ChatGPT and Claude clearly label that they are AI and may have inaccuracies. Microsoft Copilot similarly often suggests “verify the results” especially for numbers or critical decisions. None of these are fully trustless; they rely on the user to review important outputs.
In practical usage, privacy considerations often dictate which service an organization might use. For example, a bank might choose ChatGPT Enterprise or Azure OpenAI (for Copilot-like custom solutions) because of the privacy assurances. Those concerned with data sovereignty might lean to Claude on a cloud region of choice.
Summary of Safety/Privacy Highlights:
No Training on User Data (Enterprise/API): All three commit that they do not use enterprise or API-provided data to train models. Microsoft further ensures that with Copilot, user prompts and content stay within Microsoft’s cloud and aren’t seen by OpenAI or other customers.
Encryption & Compliance: OpenAI and Anthropic use encryption at rest/in transit. OpenAI has SOC 2 compliance, Microsoft has a broad compliance portfolio (FedRAMP, ISO27001, etc. inherited from Azure/M365). Anthropic likely adheres to similar standards (especially when deploying via AWS or GCP which have those certifications).
Content Moderation: All implement filters to prevent disallowed content. OpenAI has a defined policy and maintains a moderation endpoint to check outputs. Microsoft adds own filters on top (especially for Bing to avoid misinformation or political propaganda). Anthropic uses its constitution method.
Handling of Personal Data: By policy, ChatGPT and Claude should not reveal private personal data about individuals (unless it’s public info). They also shouldn’t remember personal info a user gave in one context and leak it elsewhere. Microsoft’s Copilot in enterprise should respect user permissions – e.g., if you don’t have access to a file, Copilot shouldn’t surface info from it. They built Copilot to honor existing permissions (it won’t divulge your colleague’s private OneDrive files that you can’t access).
Adversarial Robustness: Safety also involves resisting malicious inputs. All three companies do red-team testing. ChatGPT and Claude have been known to sometimes get jailbreaked by clever prompts (like asking them to role-play or obfuscating a request). This is an ongoing battle – with each update, they patch holes. Microsoft’s Copilot being more closed domain (work tasks) might face fewer wild attempts, but still could face attempts to get it to give inappropriate content or company secrets. Microsoft likely logs and monitors for misuse in enterprise (giving admins insight if someone tries to extract data via Copilot that they shouldn’t).
So... ChatGPT, Microsoft Copilot, and Claude in 2025 all prioritize safety and privacy, especially for enterprise scenarios:
OpenAI provides transparency and controls (opt-out of data use, enterprise ownership of data).
Microsoft leverages its trustworthy computing reputation (no data leaks, compliance, a promise that Copilot is *“yours” within your tenant).
Anthropic builds safety into the model’s core (Constitutional AI) and assures businesses their data won’t be misused.
Each has faced the challenge of balancing helpfulness with not doing bad things. They’ll continue refining those balances as models get even more powerful. Users and organizations should still apply common-sense caution: verify important outputs, don’t share ultra-sensitive info unless using a vetted enterprise setup, and provide feedback on any unsafe behavior they encounter so it can be fixed.
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