ChatGPT vs Claude vs DeepSeek: Full Report and Comparison on Features, Capabilities, Pricing, and more (August 2025 Update)

Model Evolution and Release Timeline

ChatGPT (OpenAI): OpenAI’s ChatGPT debuted with the GPT-3.5 model in November 2022, followed by the more advanced GPT-4 in March 2023. By 2024–2025 OpenAI introduced a new “o-series” of models focused on deeper reasoning. Notably, OpenAI o1 (preview launched Sept 2024) was a reasoning-optimized model that outperformed GPT-4 on many benchmarks. In April 2025, OpenAI released OpenAI o3 – their most powerful model to date – along with a smaller o4-mini model. These latest ChatGPT models can autonomously use tools (e.g. web search, code execution, image analysis) during a conversation. An interim GPT-4.5 (research preview) was also made available to ChatGPT Pro users in early 2025, reflecting an upgrade in general knowledge and reduced hallucinations. (As of Aug 2025, GPT-5 is not released.)

Claude (Anthropic): Anthropic launched Claude 1 in 2022 and Claude 2 in July 2023. Claude 2 brought improved coding/math skills and a much longer memory (100,000-token context). By late 2024, Anthropic introduced Claude 3.5 “Haiku”, a fast, smaller model, and by Feb 2025 Claude 3.7 “Sonnet” – their first “hybrid reasoning” model combining chain-of-thought reasoning with general conversational ability. In May 2025, Anthropic unveiled its flagship Claude 4 generation: Claude Opus 4 (their most powerful, 200K context model) and Claude Sonnet 4 (a high-performance but cost-efficient model, also 200K context). These latest Claude models (Opus and Sonnet 4) markedly improved coding, “agentic” tool use, and complex reasoning capabilities, and are considered state-of-the-art in mid-2025.

DeepSeek: DeepSeek is a Chinese AI startup (founded 2023) that rapidly iterated its models. It released DeepSeek Coder (V1) and the first DeepSeek-LLM in Nov 2023. DeepSeek V2 followed in May 2024, and an enhanced V2.5 in Sept 2024. In late 2024, DeepSeek previewed a reasoning-specialized model R1-Lite and then launched DeepSeek V3 (base and chat versions) in Dec 2024. DeepSeek-R1 (full version) was released in Jan 2025 alongside a public DeepSeek chatbot app. R1 is a reasoning-optimized model trained via reinforcement learning (no supervised fine-tuning) to excel at complex problem solving. (As of Aug 2025, DeepSeek-R2 is still in development; the CEO has delayed its release to refine performance, so R1 remains the latest reasoning model.)

Reasoning and General Intelligence

All three AI systems emphasize advanced reasoning, but they approach it differently:

• ChatGPT (OpenAI): Newer ChatGPT models (the o-series) are explicitly trained to “think longer” and produce a chain-of-thought before answering. This yields strong logical reasoning on complex tasks like math and coding. OpenAI o3 can dynamically decide if and when to use tools (e.g. browsing, Python) to solve a problem. In evaluations, o3 makes 20% fewer major reasoning errors than the earlier GPT-4-based model. It’s adept at multi-step analysis, visual reasoning, and generating hypotheses in fields like biology and engineering. Overall, ChatGPT’s latest models demonstrate high general intelligence and the ability to plan solutions, thanks to techniques like chain-of-thought prompting and extensive fine-tuning on diverse tasks.

• Claude (Anthropic): Claude was designed with “Constitutional AI” principles, meaning it was trained to be helpful, honest, and harmless by following a set of ethical guidelines. This gives Claude a very human-like conversational style and a tendency to explain its reasoning clearly. Claude 2 and later models can handle very long context inputs (up to 100K–200K tokens) and “think” through extended documents or dialogues. The Claude 4 series introduced “hybrid reasoning,” where the model can either respond instantly or engage in step-by-step deliberation (“extended thinking”) that the user can optionally view as a summary. Claude is known for common-sense reasoning and contextual understanding, but it also errs on the side of caution – it may refuse ambiguous requests to stay safe. Overall, Claude is praised for its coherent logic and empathic style, but it may occasionally avoid complex controversial reasoning to remain aligned.

• DeepSeek: DeepSeek’s R1 model was built specifically for deep reasoning. It was trained purely via reinforcement learning to develop its own problem-solving strategies, rather than mimicking human answers. As a result, DeepSeek-R1 exhibits surprisingly “human-like” reasoning abilities in fields like mathematics, coding, and logic puzzles. It can tackle challenging tasks (like solving math Olympiad problems) by breaking them down step-by-step. DeepSeek often shows its reasoning process to the user – its chat interface initially gained attention for a “thinking” mode that displays the model’s chain-of-thought as it works through a query. This transparency impressed users and even spurred rivals to offer similar features. Thanks to a Mixture-of-Experts architecture (671B total parameters, with ~37B used per token), DeepSeek can allocate specialized “experts” to different parts of a problem, achieving strong reasoning without extreme latency. In practice, DeepSeek is often very precise and fact-oriented, cross-checking information to minimize errors. Its reasoning prowess is comparable to OpenAI and Anthropic’s best – in one scientific study, the reasoning-optimized versions of ChatGPT, Claude, and DeepSeek all significantly outperformed their base versions, with DeepSeek-R1 and OpenAI o-series leading in speed and accuracy for tough scientific problems.

Coding Ability and Technical Skills

All three models excel at coding tasks, but with some differences in benchmarks and tooling:

• ChatGPT: With GPT-4, ChatGPT became famous for coding help. GPT-4 could solve ~67% of problems on HumanEval (a Python coding benchmark) in 2023 – a huge leap over earlier models. OpenAI has continued to improve this: GPT-4.5 and o-series models push the state-of-the-art on coding benchmarks. For example, OpenAI o3 achieved new SOTA on Codeforces (competitive programming) and SWE-Bench (a software engineering benchmark), even completing complex multi-step coding tasks without specialized fine-tuning. ChatGPT can now not only write code, but also execute code within the chat (via the Advanced Data Analysis tool, formerly Code Interpreter) and debug it. In fact, o3 and o4-mini can spawn a Python interpreter to verify solutions, which led to near-perfect scores on challenging math coding tests (like 99.5% on AIME 2025 with code assistance). Developers find ChatGPT’s coding abilities extremely powerful, though the model sometimes produces verbose code or needs guidance on specific library uses. Overall, GPT-4/GPT-4.5 and o3 are among the top performers in coding benchmarks, known for high success in writing correct, well-structured code.

• Claude: Claude has made rapid progress in coding. Claude 2 (2023) scored 71.2% on HumanEval, up from 56% in Claude 1.3 – approaching GPT-4’s level. By 2025, Claude Opus 4 and Claude Sonnet 4 are explicitly described as “state of the art coding models,” even powering products like the new GitHub Copilot agent. Claude Opus 4 can handle “days-long” coding tasks and multi-file projects, aided by its massive context (easily handling 100K+ tokens of codebase). It also offers Claude Code, a mode or tool to run code in the background and let the model iteratively refine solutions. In coding benchmarks, Anthropic reports Claude 4 leads on SWE-bench and performs robustly on Codeforces and other code challenges. In practice, developers praise Claude for writing clean, coherent code and following instructions closely (e.g. it will adhere to style or avoid modifying code not asked to change). Its coding “instincts” improved with the Sonnet and Opus series, and it is reliable at debugging and explaining code as well. One caveat: earlier Claude versions were a bit slower to output code than ChatGPT, but Claude 3.5/4 now feature faster outputs and even a 64K token output limit, enabling generation of very large code files in one go.

• DeepSeek: DeepSeek models are also highly capable in programming. DeepSeek V3 and R1 were trained on a vast code corpus (the training set spans 14.8 trillion tokens across domains). They demonstrate “exceptional coding abilities”, with DeepSeek-R1 performing “superior code generation across multiple languages” and complex software engineering tasks. On the Codex HumanEval, DeepSeek’s performance is comparable to the top closed models. While exact public numbers are scarce, DeepSeek claims its models “outperform other open-source models” and are on par with leading closed models on coding benchmarks. Notably, DeepSeek’s MoE architecture can specialize for coding: certain expert modules handle programming queries efficiently, which allows DeepSeek to be both large and efficient in coding tasks. Users report that DeepSeek’s code solutions are often very accurate and to-the-point – it tends to stick to factual, terse explanations (less storytelling) and produce working code with fewer hallucinated libraries. In head-to-head comparisons, some developers rank DeepSeek R1 at least equal to OpenAI’s models for coding, with one noting R1 was virtually tied with OpenAI’s o3 model on technical problem-solving (though o3 was slightly faster). DeepSeek can be run locally as well, which means developers can use it for coding without sending code to an external API – a plus for sensitive projects.

Performance on Knowledge and Benchmark Tests

Beyond coding, these models have been evaluated on many academic and professional benchmarks. The table below summarizes key benchmark results and capabilities (as of August 2025):

Benchmark / Test	ChatGPT (OpenAI)	Claude (Anthropic)	DeepSeek
MMLU (Univ. knowledge)	~86% accuracy (GPT-4 on MMLU). O-series further improved reasoning-heavy categories.	~78–82% (Claude 2 scored ~78.5%; Claude 4 likely low-80s). Continual gains, but slightly behind GPT-4 on obscure domains.	~80–85% (estimated). DeepSeek claims SOTA-level MMLU; its R1 excelled in many domains (54/57 categories vs GPT-4). Very strong on multilingual and STEM topics.
Mathematical Reasoning	Solves complex math (AIME) problems with tools – near 100% with code assistance. Without tools, GPT-4/O3 can still outperform most models on GSM8K (grade-school math ~90%+).	Claude 2 scored 88% on GSM8K math (grade-school math). Claude excels in step-by-step solutions and scored top-500 in US Math Olympiad qualifier. 100K context helps for long problems.	DeepSeek-R1 won gold on math – extraordinary at AIME and even hard MATH datasets. It naturally does multi-step proofs. Users note DeepSeek often outperforms others in complex math without needing external tools.
Knowledge QA (e.g. Trivia)	GPT-4 has broad knowledge up to 2021; scores high on ARC and open trivia. With browsing enabled, ChatGPT can retrieve up-to-date info on the fly.	Claude is trained on slightly more recent data (Claude 2 had knowledge of newer 2023 info). It’s strong in general knowledge and very good at interpreting context or documents given in prompt.	DeepSeek V3 was trained on 14.8T tokens including diverse web data. It provides very fact-checked answers, often citing or cross-verifying internally. It has multilingual knowledge (Chinese and English) and tends to minimize “I don’t know” responses via its retrieval-like approach.
Professional Exams	GPT-4 was famous for passing the Bar (estimated ~85% on multiple-choice, top 10% of test-takers) and high on medical exams. O-series likely maintains top-tier exam performance.	Claude 2: 76.5% on Bar exam multiple-choice (up from 73% in Claude 1.3). ~90th percentile on GRE Verbal/Writing; ~50th percentile Quant. Good at logical reasoning tasks (e.g. it exceeds PhD-level accuracy on some science QA).	No public data for Bar/GRE, but DeepSeek aims at these too. DeepSeek-R1 was benchmarked on graduate-level physics/chemistry questions and exceeded human PhD accuracy on a tough science QA benchmark (GPQA). Likely competitive with GPT-4 on many professional knowledge tests.
Multi-turn Reasoning (HellaSwag, BBH)	GPT-4 and newer models perform near or above human level on narrative reasoning and “common sense” benchmarks (e.g. HellaSwag ~95%+). OpenAI’s focus on chain-of-thought improved these even more.	Claude is very strong in commonsense reasoning and ethical judgement. It was tuned on HHH (Helpful, Honest, Harmless) evaluations, which improves its consistency in multi-turn logic puzzles. Likely on par with GPT-4 in many cases.	DeepSeek’s hybrid approach (MoE + RL) yields excellent results in tricky reasoning benchmarks. It significantly outperformed non-reasoning models in a research study on complex tasks. DeepSeek’s accuracy on tricky multi-turn tasks is comparable to OpenAI’s o-series, with R1 often winning on pure reasoning speed.

Key Takeaway: All three are top-tier general AI models. ChatGPT (GPT-4/o3) still holds the edge on many academic benchmarks (notably highest MMLU score), but Claude 4 and DeepSeek R1 are close behind, sometimes even surpassing in specialized areas (e.g. Claude 4 in long-form writing, DeepSeek in certain creative or non-English tasks). Notably, DeepSeek’s emergence has proven that open, low-cost models can match the incumbents – it “upended AI” by achieving GPT-4-level performance at a fraction of the training cost.

Pricing Tiers and Access Options

Each service offers a mix of free access and paid plans, but their models differ in availability:

• ChatGPT: OpenAI provides a Free tier of ChatGPT accessible to anyone with an account. Free users get unlimited chats with the older GPT-3.5 model. The free service is quite usable but excludes the most advanced models and features – responses can be slower and may be unavailable at peak times. For full power, OpenAI sells ChatGPT Plus at $20/month. Plus grants access to GPT-4 (now GPT-4o) and priority fast responses. Plus users can switch between GPT-3.5 and GPT-4 models, use Advanced Data Analysis (run code on files), enable Browsing and a library of third-party Plugins, as well as voice input and image understanding on supported platforms. Essentially, $20 unlocks ChatGPT’s best model and beta features. OpenAI also offers higher-cost plans: ChatGPT Pro (around $200/month) for extremely heavy users, and ChatGPT Enterprise for organizations (enterprise plans include unlimited GPT-4 access, data encryption, and admin controls). The API access to OpenAI models is billed separately on a pay-as-you-go basis (for example, GPT-4 API usage is around $0.06 per 1K tokens output). Overall, ChatGPT’s pricing is straightforward: free for basic use, $20 for premium personal use, and higher tiers for businesses.

• Claude: Anthropic’s Claude AI has become more accessible in 2025. Claude offers a Free tier as well – anyone can chat with Claude on the web or the Claude mobile apps (iOS/Android) at no cost. The free tier allows substantial usage with the Claude base model, including generating code, analyzing text or images, and even web search within Claude’s interface. Paid plans are available for more intense use. The Claude Pro plan is priced similarly to ChatGPT: $20/month (or $17/month if paid annually). Claude Pro includes “everything in Free, plus” higher usage limits, priority access, and some advanced features: e.g. direct terminal access to Claude Code (for coding workflows), unlimited “Projects” to organize chats/documents, integration with Google Workspace (to let Claude read your email, calendar, docs if you permit), and an “extended thinking” mode for complex tasks. For power users, Anthropic also has a Claude Max plan (a higher tier for individuals) and Team/Enterprise plans – these unlock the most powerful Claude Opus 4 model and larger usage quotas. For instance, Claude Opus 4 (the 200k-context model) is available to Pro, Max, and enterprise users, but very extensive use of Opus might require the Max or enterprise plan due to its higher compute cost. Anthropic’s API is also pay-as-you-go (Opus 4 API pricing is $15 per million input tokens, $75 per million output) – roughly $0.075 per 1K output tokens, comparable to or slightly higher than OpenAI’s GPT-4 pricing. In summary, Claude’s free tier is generous (particularly for casual use and evaluation), and its $20 Pro tier closely matches ChatGPT Plus in cost/features, while offering unique perks like tool integrations and huge context windows.

• DeepSeek: DeepSeek’s model is open-source and freemium. It does not have a conventional subscription plan. Instead, DeepSeek offers free access in two ways: (1) via a web demo and chatbot that anyone can try, and (2) via the open-source model weights that can be downloaded (under MIT license) for self-hosting. The official DeepSeek web demo (DeepSeek V3 model) can be used without signup, allowing users to test its capabilities interactively. For more sustained or advanced usage, DeepSeek uses a pay-per-use model (like an API) where you purchase credits. The rates are exceptionally low: about $0.27 per 1M input tokens and $1.10 per 1M output tokens for the standard DeepSeek-V3 Chat model. Even the more advanced “DeepSeek Reasoner” (R1 with extended reasoning) is only ~$2.19 per 1M output tokens. To put this in perspective, generating the same amount of text with GPT-4 would cost an order of magnitude more (OpenAI’s rate is ~$60 per 1M output tokens). There are no monthly fees or minimums – one simply pays for what they use. Because the model weights are open, technically power users can run DeepSeek locally for free, given sufficient hardware. This makes DeepSeek extremely appealing for developers on a budget or those needing to process massive volumes of text: e.g. analyzing millions of words might cost ~$10 with DeepSeek vs $100+ with GPT-4. DeepSeek’s open model also means no hard usage caps or waiting lists – if the public API is busy or filtered, one can deploy it on their own servers for full control. In summary, DeepSeek is the most cost-effective: free to try and dramatically cheaper at scale, albeit the user must handle more of the deployment (or trust a smaller new provider).

Integration and Ecosystem

The ecosystem around each model affects how users can interact with them and integrate them into workflows:

• ChatGPT Integrations: ChatGPT has a rich and growing ecosystem. On the user side, OpenAI offers an official ChatGPT web interface and mobile apps (both iOS and Android) for conversational use. In 2023, OpenAI introduced ChatGPT Plugins, enabling ChatGPT to plug into external services (travel booking, databases, math solvers, etc.). There’s also a built-in web browsing mode (via Bing) that ChatGPT Plus users can enable, so the AI can fetch up-to-date information online. For developers, ChatGPT is accessible via the OpenAI API (with endpoints for Chat Completions). This API has been widely integrated into other products: for instance, Microsoft’s Bing Chat and Windows Copilot are powered by OpenAI’s GPT-4; Microsoft 365 Copilot (in Office apps) also uses OpenAI models. There are plugins/extensions for IDEs like VS Code (e.g. third-party ChatGPT assistants) and a plethora of community integrations (Slack bots, browser extensions, etc.). In Sept 2024, OpenAI launched ChatGPT Enterprise, which lets companies integrate ChatGPT with guaranteed privacy (no training on their data) and connect ChatGPT to internal data sources (e.g. a company knowledge base). Overall, ChatGPT is deeply integrated from web browsers to Office to coding environments, thanks in part to OpenAI’s partnership with Microsoft and a robust plugin ecosystem.

• Claude Integrations: Anthropic initially made Claude available via API and limited partnerships (it was integrated into tools like Notion AI and DuckDuckGo’s search assistant early on). By 2025, Claude has its own Claude.ai platform where users can chat with Claude on the web or via the Claude app. Claude can accept document uploads and large text for analysis, which is great for summarizing or searching within long files. Notably, Claude has a 100K+ token context, meaning it can ingest hundreds of pages of text in one go – developers use this for tasks like analyzing large PDFs or even books. Integration-wise, Slack launched a Claude-powered assistant (Slack GPT offers Claude for certain functions), and Zoom has used Claude for some of its AI summaries. Claude is also offered as an option on AWS’s Bedrock and Google Cloud Vertex AI platforms, making it easy for enterprises to deploy. Another unique aspect: Claude Pro users can connect Claude to their Google Workspace – allowing it to read one’s emails or calendar if permitted, and perform smart email drafting or scheduling assistance. Claude also introduced “extensions” for desktop – likely plugins to use Claude in other desktop applications (the details are emerging). In summary, Claude is becoming well-integrated in productivity tools and enterprise AI ecosystems, though it isn’t as ubiquitous as ChatGPT’s presence (due to the latter’s Microsoft backing).

• DeepSeek Integrations: DeepSeek, being open-source, relies on community and third-party integrations. DeepSeek provides a web chat interface (chat.deepseek.com) – which gained massive popularity in early 2025, even becoming the #1 downloaded free app on iOS in the U.S. in January. This spike was due to users flocking to try R1’s capabilities and likely the appeal of a free, unrestricted chatbot. The official DeepSeek app/platform allowed features like code execution and file uploads similar to its competitors, although sign-up required a Chinese phone number for the official channel (the HuggingFace demo did not). On the development side, DeepSeek offers an OpenAI-compatible API – meaning developers can swap OpenAI’s endpoint for DeepSeek’s with minimal code changes. This ease of integration, plus the low cost, led many to experiment with DeepSeek in their own apps by 2025. Chinese tech companies have also integrated DeepSeek models – by Feb 2025 dozens of Chinese firms announced they were embedding DeepSeek into their products. Because anyone can self-host it, DeepSeek has also been integrated into self-hosted chatbots, developer tools, and even browser extensions by enthusiasts. For instance, one could run DeepSeek on a local server and use it as a private coding assistant in an IDE or as a chatbot on a personal website. However, as a newer entrant, DeepSeek’s official integrations (with big platforms) are fewer – it’s not natively in Office or Slack – but the open nature means it can be adapted to many situations if one is technically inclined. Additionally, DeepSeek’s multilingual support (strong English and Chinese, etc.) makes it attractive for integration in non-English markets or bilingual applications.

User Experience and Interface Features

Each model’s user experience has its own flavor and features:

• ChatGPT UX: ChatGPT’s interface is known for its simplicity and versatility. It presents a clean chat window where users converse in a thread of messages. Key features include conversation history (past chats are saved, allowing users to revisit and continue them) and the ability to edit messages or prompt ChatGPT to regenerate responses if unsatisfactory. With the introduction of plugins and browsing, ChatGPT’s interface added a plugin selector – users can choose tools like WolframAlpha, web browser, or others, and the model will transparently use them. In mid-2025, OpenAI also added a “Thinking” mode toggle for some users: free users can select a “Fast” (default) or “Think” mode powered by the o4-mini reasoning model, which spends more time to produce an answer. This essentially lets users trade speed for better reasoning, enhancing the experience for complex queries. Another aspect of ChatGPT’s UX is the Multimodal capability – on certain platforms, ChatGPT can accept images as input (to describe or analyze them) and even voice input/output (OpenAI has been testing a voice conversation mode). The official mobile apps further streamline voice conversations and have features like syncing chat history across devices. Overall, ChatGPT provides a very engaging, “chatty” experience with a friendly tone. It is often described as verbose and explanatory, which is good for thorough answers but sometimes requires nudging to be more concise. Users have fine-grained control via system messages or instructions to adjust the style.

• Claude UX: Using Claude feels like chatting with a thoughtful, polite assistant. Claude’s tone is empathetic and upbeat – Anthropic tuned it to be helpful and harmless, so it often thanks users for questions and apologizes if it can’t comply. Many find Claude’s style “warm” and human-like. In terms of interface, Claude’s web app allows very large inputs (you can paste or upload long documents for analysis). Claude will happily summarize or answer questions about an entire PDF or even a short book, within one session – something unique due to its context size. Claude also supports file attachments and will output formatted content (it’s good at producing well-structured essays, letters, or even JSON/CSV data if asked). A notable Claude feature is “Projects” – users can organize chats and documents into project folders, which is useful for keeping longer workflows or multiple files organized. Claude’s “extended thinking” mode can show a summary of its internal reasoning when tackling a complex query, giving a window into why it gave a certain answer. In terms of responsiveness, Claude 2+ models are quite fast for most queries, though at times slightly slower than ChatGPT (especially if using the full 100k context or extended reasoning, one might wait a bit longer). Claude is less likely to ramble off-topic – if anything, it might under-answer (providing a concise response and awaiting clarification). Importantly, Claude’s UI also now includes web search integration, similar to ChatGPT’s browsing: it can fetch info from the web when you enable that setting. In summary, Claude offers a user-friendly, respectful chat experience, excellent for sensitive or lengthy discussions. Users often mention they feel “comfortable” asking Claude anything because it handles delicate topics with care and refuses in a gentler manner if it must.

• DeepSeek UX: DeepSeek’s user experience was initially geared toward tech-savvy users but has evolved. The DeepSeek chatbot interface (both web and mobile) displays the model’s step-by-step reasoning by default – as the model “thinks,” you might see a running log of its analysis before the final answer. This is a compelling feature for those who want transparency or to debug the AI’s logic. Some users loved “reading through the process of it thinking”, which felt like peering into the AI’s mind. DeepSeek’s style of response tends to be precise and factual. It usually directly answers the question and then possibly cites source-like details or calculation steps. Compared to ChatGPT or Claude, DeepSeek is a bit less conversational/empathetic by default – it can come across more like a knowledgeable analyst than a chatty friend. However, it can certainly do creative writing or casual tone if prompted; it’s just oriented toward accuracy and conciseness (some described it as less “verbose or engaging” but highly to-the-point). The interface has the usual copy/share features, and since it’s open, many community clients exist (with variations in UI). One downside: the official DeepSeek chat required a Chinese phone SMS to register, which was a hurdle for some international users (though the Hugging Face demo was open). This has been partially mitigated by third-party UIs and the open API. DeepSeek also supports multimodal inputs to an extent: its V2 introduced some multimodal capacity (text+image), though its core use is text-based Q&A. In user testing, DeepSeek was especially praised for multi-language output – e.g. producing very natural Russian prose or Chinese text on par with native speakers. The flipside of fewer filters is that DeepSeek might produce content others refuse (which can be good for harmless creative freedom, but users must exercise their own judgment ethically). Overall, the DeepSeek UX is powerful for power-users: it gives insight into reasoning and doesn’t hand-hold as much, which some developers and researchers appreciate. Casual users may find it slightly “dry” or less humorous than ChatGPT, but they gain accuracy.

Training Data Transparency and Safety Alignment

The three providers have different philosophies on transparency and alignment:

• OpenAI (ChatGPT): OpenAI has been somewhat opaque about training data for its flagship models. In the GPT-4 technical report, OpenAI notably declined to disclose the model’s size or the specific training dataset details, citing competitive and safety concerns. We do know GPT-4 was trained on a massive mix of internet text (up to 2021), code, and human demonstrations, costing over $100 million in compute. OpenAI employs Reinforcement Learning from Human Feedback (RLHF) heavily to align ChatGPT – meaning human reviewers graded the AI’s answers and the model was tuned to favor those. This yields a generally polite and norm-following AI. Safety-wise, OpenAI uses both automated red-teaming and manual red-teaming. GPT-4’s system card describes how they tested for misuse and put guardrails. However, some in the AI community criticize OpenAI for being a “closed book” – not open-sourcing models or revealing much about the content of the training data (which likely includes Common Crawl, Wikipedia, books, etc.). On the positive side, OpenAI has a strict usage policy and the model refuses requests for disallowed content pretty consistently. They continuously update ChatGPT to reduce bias and toxicity. In June 2025, for example, they updated ChatGPT’s voice mode and improved its refusal consistency. Transparency: OpenAI provides high-level info (like categories of data and broad safety approaches) but not specifics. Alignment: ChatGPT is heavily aligned to avoid harmful content – sometimes to a fault, as it might be overly cautious or give sanitized answers (the “PR-talk” style some users noted). OpenAI is researching ways to let the model follow user preferences more flexibly without breaking safety guidelines in the future.

• Anthropic (Claude): Anthropic has been relatively more open about its methods, if not its data. They published research on “Constitutional AI”, explaining how Claude is aligned by an AI-written constitution of principles (e.g. from rights documents and philosophy) rather than only relying on human feedback. They have also released model cards for Claude that list evaluation results and safety metrics. While the exact training data of Claude isn’t fully public, Anthropic has indicated it’s trained on a large web crawl and other text, and Claude 2 had more recent data (up to early 2023, including some newer libraries for coding). They tend to share parameter counts of older models (Claude 1 was ~52B parameters, for example) and broad info like context length and that they use reinforcement learning and red-teaming. Anthropic also emphasizes “HHH alignment” – making Claude Helpful, Honest, Harmless. They conduct rigorous tests: internal evaluation showed Claude 2 was 2x better at giving harmless responses compared to Claude 1.3. They also regularly publish about mitigating biases and not following harmful instructions. In practice, Claude is less likely to output offensive content – it has strong filters but tries to do so in a friendly manner (e.g. explaining why it cannot comply). Anthropic’s Transparency initiatives include an Economic Inequality Index (to monitor bias in economic content) and participation in academic benchmarking of biases. They haven’t open-sourced Claude, but they allow academic access in some cases. Overall, Anthropic is seen as safety-conscious and somewhat more transparent than OpenAI about their alignment techniques (if not the full dataset). They also allow users to peek into Claude’s reasoning in “thinking mode,” which is a form of transparency at the interaction level.

• DeepSeek: DeepSeek stands out for open-sourcing its models. DeepSeek-R1 and V3 were released under an MIT License with “open weight” disclosure. This means the exact model parameters are available to the public (with some usage terms), a bold move for such advanced models. DeepSeek has published a technical paper detailing its MoE architecture and training approach, including the fact that it trained on 14.8 trillion tokens at a cost of only ~$6 million. They credit techniques like mixture-of-experts and algorithmic efficiency for this cost reduction. The company has been transparent about challenges too – for instance, they trained during U.S. chip export restrictions by using less-powerful GPUs and fewer of them. DeepSeek discloses a lot of metrics (e.g. how R1 performs on certain math or coding benchmarks, how many GPU hours it used) and openly shares model weights for community evaluation. In terms of training data transparency, they haven’t published the exact data sources list, but it’s implied to include large English and Chinese corpora and coding data. Given the open model, independent researchers can analyze the model to infer data characteristics. Regarding safety and alignment, DeepSeek’s approach is interesting: R1 was trained via RL to develop reasoning naturally, and it was released with the expectation that the community will help find issues. The model is relatively aligned (it does refuse some harmful requests and has a basic moderation layer in the official demo), but it is not as heavily filtered as ChatGPT or Claude. DeepSeek encourages ethical use but essentially gives users the freedom (and responsibility) that comes with an open model. They did incorporate some alignment – likely a mix of RLHF and their own red-teaming – but details are sparse. Notably, because DeepSeek is based in China, it presumably complies with Chinese content regulations (the official version likely filters politically sensitive content against Chinese laws). However, the open-source model itself can be fine-tuned or used by anyone, which introduces both opportunity (custom alignment by users) and risk (people could misuse it since it’s not locked down). In summary, DeepSeek is highly transparent about architecture and performance, open with weights, but somewhat less restrictive on outputs, placing more onus on the user’s discretion.

Known Limitations, Strengths, and Weaknesses

Finally, it’s important to note each model’s strengths and weaknesses as observed up to 2025:

• ChatGPT (GPT-4/o3) Strengths: Unparalleled general knowledge and vast training – it can handle almost any topic with authority. Excellent reasoning especially with the new o-series (tackles logic puzzles, complex math, legal reasoning, etc.). Highly articulate – gives well-structured, often eloquent responses. Great at coding, debugging, and now uses tools automatically to find answers or calculate when needed. Very reliable in following user instructions (especially GPT-4 and later, which seldom misunderstands prompts). Widely integrated and continuously improving through updates.

  ChatGPT Limitations: It sometimes hallucinates with confidence (especially on obscure queries, it may fabricate a plausible-sounding answer). While reduced in GPT-4, hallucination isn’t fully gone. It can be verbose or repetitive, often giving longer answers than needed. It also has a knowledge cutoff (mid-2021 by default, unless browsing is enabled), so it can be outdated on current events. The safety filters, while important, mean ChatGPT will refuse certain requests or produce sanitized answers – it might avoid edgy humor or discussions that Claude or DeepSeek might handle with nuance. In some niche domains (e.g. very specialized technical fields), GPT-4’s training data might have gaps or outdated info. Lastly, the 8K/32K context limit of GPT-4 is smaller than Claude’s 100K or DeepSeek’s 128K, meaning ChatGPT can handle less text in one go than its competitors (OpenAI is working to expand this). As one user pointedly described, GPT-4 (especially with default settings) can lean toward “PR-talk language” – careful and diplomatic, but sometimes lacking directness or creative flair.

• Claude Strengths: Extremely lengthy context – Claude can remember and analyze huge amounts of text (entire manuals, books, or codebases) in one prompt. This makes it fantastic for summarizing long documents or doing in-depth analyses without losing track. Human-like and safe – Claude’s conversational style is often the most pleasant and “human” in feel; it’s great for brainstorming, counseling tone, or any application needing a friendly voice. It’s highly compliant with instructions: Claude follows the user’s ask closely and will admit uncertainties rather than make things up aggressively. It’s the most “ethical” AI in approach, rarely giving unsafe outputs (useful for enterprise settings or sensitive contexts). Claude is also fast in its latest iteration (Claude 4) and affordable via its free tier for many tasks. Its coding ability is top-notch, nearly rivaling ChatGPT, and it’s particularly good at explaining code or algorithms in a digestible way (benefiting education use-cases).

  Claude Limitations: Claude’s caution can be a double-edged sword. It may refuse queries that could be answered safely but touch on complicated areas – for example, detailed medical or legal advice might make Claude respond with a safe-completion or a disclaimer, more so than ChatGPT. It prioritizes not being offensive, which occasionally means it won’t delve into dark humor or certain creative writings even if the user requests. Users have noted Claude sometimes “plays it safe” to the point of being a bit bland in responses on controversial topics. Another limitation is that it sometimes ends answers abruptly or yields shorter answers than expected, especially if it thinks being concise is safest. Compared to ChatGPT, it has been slightly slower in output generation historically (though Claude 4 closed the gap significantly). On some very technical benchmarks (e.g. hardcore academic exams), Claude still trails GPT-4 by a small margin. And while Claude’s training data is more up-to-date than GPT-4’s, it’s still not real-time (it doesn’t know 2024–2025 info unless given via its web search). In coding, Claude can struggle if asked to produce extremely long code without errors – it benefits from the user overseeing and sometimes has minor logical bugs in code (which it can fix if told to self-check). In summary, Claude might avoid complexity rather than risk a wrong or off-tone answer, which is safe but can be a limitation for power users.

• DeepSeek Strengths: Accuracy and factual precision are DeepSeek’s hallmark. It was designed to retrieve and cross-verify information, leading to far fewer hallucinations in many factual Q&A scenarios. Users found that if ChatGPT would sometimes bluff an answer, DeepSeek would either find the correct info or explicitly say it’s not sure, which builds trust. DeepSeek is also the most customizable – since it’s open, one can fine-tune it on domain-specific data, or modify it to have a certain persona or constraints, something not possible with closed models. It shines in specialized tasks: for example, one user noted it “absolutely blows everything else out of the water” for writing in Russian about classical music. Such niche prowess likely comes from its massive token count and expert mixture (some experts may specialize in certain languages or styles). Cost is a huge strength: DeepSeek’s affordability means it can be used to process huge data (like indexing a large document repository with AI) without breaking the bank. Technically, it supports a 128K context like Claude, so it handles long inputs well. And its reasoning mode is state-of-the-art, solving very hard problems – great for research or advanced use. Finally, DeepSeek’s community-driven nature means fast iterations and improvements; bugs can be fixed by community patches, and there’s a sense of openness in its progress.

  DeepSeek Limitations: As a newer entrant, DeepSeek’s models might feel less polished in conversation. It lacks the years of fine-tuning on human conversational datasets that ChatGPT had. So, it can come across as less “chatty” or witty by default. It doesn’t volunteer extra clarifications or creative flourishes unless prompted – some might find it a bit dry. Also, while DeepSeek tries to follow instructions, its alignment isn’t as thoroughly tested – it might produce content that ChatGPT/Claude would filter (potentially offensive or biased outputs if provoked), so the user has to exercise caution. On the technical side, running a 671B-parameter MoE model locally is not trivial – you need significant hardware (though MoE means you don’t need to load all 671B at once, it’s still heavy). So, the open-source freedom comes with a complexity cost for some users. Another limitation: English-centric tasks – DeepSeek is bilingual (Chinese/English) at its core, and it’s excellent in those, but for less common languages or niche western pop culture, it may be less tuned than GPT-4 which saw more English internet content. Some early users noted DeepSeek’s style could be overly formal or it might overuse certain phrases – minor quirks that open models often have until fine-tuned. Lastly, because DeepSeek is causing a shake-up in the industry, it might not yet have the same level of trust or track record in enterprise settings; some companies might be hesitant to rely on it until it’s proven over time (and given the privacy of data, since it’s from a new lab). But these limitations are gradually shrinking as DeepSeek rapidly iterates.

Qualitative Feedback from Users and Developers

User and developer opinions provide a qualitative layer to this comparison:

• Many everyday users find ChatGPT (especially GPT-4) to be the “go-to” assistant for a broad range of tasks because of its balanced skills and the sheer convenience of its interface. People praise its creativity and how it can handle everything from writing poems to explaining quantum physics. However, power users on forums have observed that ChatGPT’s answers can feel formulaic or overly safe in some cases. For instance, one Reddit user remarked that recent GPT-4-based models were “repetitive slop with a lot of words and little substance” when it came to creative writing, preferring DeepSeek’s more direct style. Developers still generally consider GPT-4 the gold standard for tough coding bugs or logic puzzles, but they note that the gap has narrowed considerably in 2025.

• Claude has earned a devoted following among those who value conversational quality. Users often say interacting with Claude “feels like talking to an enthusiastic, knowledgeable colleague”. Its empathy in answers (for example, it apologizes and shows concern in a human-like way) makes it popular for tasks like mental health support or personal advice – roles where tone matters. Some writers prefer Claude for long-form content generation, as it tends to produce coherent and structured text when asked for stories or essays, with fewer breaks in coherence due to its large context window. On the flip side, some developers and AI enthusiasts find Claude’s cautious nature a bit frustrating. They joke that Claude can be “too polite for its own good,” sometimes declining to provide code that scrapes a website (for ethical concerns) or adding moral disclaimers unnecessarily. Overall though, sentiments are that Claude 2/Claude 4 is a top-tier model that “finally gives OpenAI real competition,” especially after Anthropic opened it up for free trials. People also appreciate Claude’s reliability – it has had fewer outages and is accessible even when ChatGPT might be at capacity.

• DeepSeek sparked a lot of excitement in early 2025. Many developers were astonished by its performance given its independent origin and open model. It has been described as a “game-changer” that showed the world you don’t need a trillion-parameter closed model to compete at the highest level. Users from multilingual backgrounds celebrate DeepSeek’s abilities in languages like Russian, Chinese, etc. One user on Reddit said “DeepSeek R1 – the most natural sounding text of them all… absolutely blows everything else for [my use cases]”. This kind of feedback suggests DeepSeek often feels less constrained and more direct, which certain tasks benefit from. Developers also love the freedom – they can run local instances, fine-tune it, and not worry about API limits. However, with great power comes caution: some discussions note that because it’s open, one must ensure to put their own safety filters if deploying publicly. There were also initial concerns about Chinese government influence (given the company’s base in China), but so far DeepSeek hasn’t shown obvious bias beyond perhaps being cautious about Chinese political queries in the official version. The community reaction can be summed up as: ChatGPT is no longer the undisputed king – Claude and especially DeepSeek have carved out significant niches. It’s “hard to declare a clear winner” now among these top models, as one detailed comparison put it. Each has domains where it shines – ChatGPT for all-around use and integration, Claude for aligned conversational intelligence, and DeepSeek for accuracy and openness.

In conclusion, as of August 2025, ChatGPT, Claude, and DeepSeek represent the cutting-edge of large language models, each with unique advantages. ChatGPT (with GPT-4/GPT-o series) remains a powerful generalist with top-tier reasoning and a mature ecosystem. Claude has become the civil, long-winded sage that enterprises trust for safe deployment and massive context tasks. DeepSeek has emerged as the disruptor, delivering comparable intelligence with an open, cost-efficient approach that is reshaping expectations. The competition has clearly benefited users – we now have multiple excellent AI assistants to choose from. The best choice “depends on what you need”: for lively and interactive chats, many pick ChatGPT; for empathetic, secure advice, Claude may be ideal; for ultra-precise answers and control, DeepSeek is compelling. Rather than a single winner, we are seeing these models complement each other’s strengths in the AI landscape. And with upcoming iterations (Claude 5? OpenAI GPT-5? DeepSeek R2?), this race is only accelerating – a win for AI progress and users alike.

____________

FOLLOW US FOR MORE.

DATA STUDIOS

datastudios.org