ChatGPT vs DeepSeek: Full Overview, Report and Comparison

INDEX

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1 Model Architecture and Training

ChatGPT (OpenAI GPT-4)

It is based on OpenAI’s GPT-4, a large proprietary Transformer model. OpenAI has not disclosed exact parameters, but reports suggest GPT-4 is on the order of ~1 trillion parameters. It is a dense transformer (no sparsity) trained on a broad mix of web, code, and document data, and aligned via extensive fine-tuning and reinforcement learning (OpenAI spent “6 months aligning” GPT-4). GPT-4 is multimodal (accepts text and image) and uses chain-of-thought reasoning. All details of its training corpus and procedure remain proprietary (likely Internet+books code up to ~2022).

DeepSeek (DeepSeek-V3/R1)

It is an open-source Chinese AI model series. Its main model DeepSeek-V3 is a sparse Mixture-of-Experts Transformer with 671 billion parameters total (but only ~37B active per token). It uses novel Multi-Head Latent Attention (MLA) to reduce memory, and mixed-precision training (FP8) for efficiency. DeepSeek-V3 was pretrained on 14.8 trillion tokens (mixed English/Chinese data) over ~2.66M GPU-hours – an order of magnitude less compute than GPT-4’s rumored training cost. A specialized DeepSeek-R1 model (7B “small” up to 671B “full”) is fine-tuned for reasoning: it distills chain-of-thought from V3 and adds reinforcement learning to improve logic. In summary, DeepSeek is open (code MIT-licensed) but the model weights carry a custom license with use restrictions, whereas ChatGPT/GPT-4 is closed-source (no public weights). Table 1 summarizes key architecture points:

Aspect	ChatGPT (GPT-4)	DeepSeek (V3/R1)
Architecture	Dense Transformer (closed)	Sparse MoE Transformer (671B total, 37B active)
Parameters	~1T (unverified)	671B (37B active)
Context Window	~8K tokens (GPT-4 standard)	64K tokens
Training Data	Undisclosed (likely diverse web/code up to 2022)	14.8T tokens of text/code (diverse)
Precision/Compute	High FP16/BF16; unknown total compute/cost	Mixed FP8 on 2048 H800 GPUs (2.66M GPU-hrs, $5.3M)
Fine-Tuning	Extensive RLHF by OpenAI; ChatGPT “Assistant” style	Supervised fine-tuning + RLHF (R1 distillation)

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2 Performance Benchmarks

On standard LLM benchmarks, ChatGPT (GPT-4) and DeepSeek show similar high performance on many tasks, with ChatGPT generally leading in coding and multilingual contexts and DeepSeek excelling in math and Chinese. For example, MMLU (multi-subject knowledge) few-shot accuracy is roughly ~88–89% for GPT-4 and 88.5% for DeepSeek-V3. On coding (HumanEval), GPT-4 achieves about 90% pass@1, whereas DeepSeek’s chat model scores ~82.6%. For grade-school math (GSM8K/MATH-500), DeepSeek-V3 reports ~89.3% accuracy, comparable to GPT-4 (reported ~89–92% by other sources). Notably, DeepSeek outperforms GPT-4 on some reasoning/math sets: e.g. on MATH-500 it reports 90.2% vs GPT-4o’s ~74.6%. On Chinese benchmarks, DeepSeek is much stronger (e.g. C-Eval: 86.5% vs GPT-4’s 76.0%). Table 2 summarizes key benchmark comparisons:

Benchmark	ChatGPT (GPT-4)	DeepSeek-V3 (Chat)
MMLU (multi-subject)	~88–89%	88.5%
HumanEval (code)	90.2% (pass@1)	82.6% (pass@1)
GSM8K (math)	~89% (est.)	89.3%
MATH-500 (math)	~75–96% (varies)	90.2%
C-Eval (Chinese)	76.0%	86.5%

Overall, GPT-4 yields very high scores on general knowledge and code benchmarks, while DeepSeek matches or exceeds GPT-4 on math and Chinese tests. Both far outperform older models (e.g. GPT-3.5) on these tasks. DeepSeek’s developers emphasize its state-of-art math/code performance (especially for an open model); independent tests (Writesonic) likewise found DeepSeek R1 nearly on par with ChatGPT for math and code.

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3 Programming and Reasoning Capabilities

ChatGPT (GPT-4)

GPT-4 is a strong generalist and excels at programming tasks and complex reasoning. It can generate and debug code in many languages (it underlies GitHub Copilot) with ~90% accuracy, and its chain-of-thought training yields robust reasoning. OpenAI provides a Code Interpreter tool in ChatGPT for advanced tasks. ChatGPT is widely used for programming help and logical problem solving in practice.

DeepSeek

It has dedicated code models (“DeepSeek Coder”) ranging from 1B to 33B parameters, pretrained on 2 trillion code tokens. These models achieve state-of-the-art open-source results: e.g. a 33B model outperforms Meta’s CodeLlama-34B by 7–11% on HumanEval/MBPP, and the instruct-tuned variant matches GPT-3.5-turbo on coding tasks. Its general chat model (V3) also shows strong coding ability (65% base→82.6% chat pass@1). For reasoning, DeepSeek-R1 is explicitly designed: it distills chain-of-thought from V3 and adds reinforcement learning. In practice, DeepSeek-R1 has demonstrated creative use (e.g. writing prompts, exam reasoning) and, combined with Anthropic’s Claude Sonnet, can form powerful coding assistants. In sum, both systems are very capable in programming and logical reasoning, with DeepSeek offering specialized open models and ChatGPT offering a mature integrated service.

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4 Multilingual Support and Capabilities

GPT-4 (ChatGPT) is explicitly multilingual: OpenAI notes it supports 26 languages at launch (including English, Chinese, Spanish, French, German, etc.) and performs well across them (though English scores are usually highest). GPT-4’s language abilities are strong, and it is widely used globally. DeepSeek is primarily bilingual (English and Chinese). It is trained on substantial Chinese data and scores very highly on Chinese benchmarks; e.g. its performance on Chinese language tasks (CEval, CLUE, etc.) surpasses GPT-4’s. However, community reports indicate that smaller DeepSeek models struggle with languages beyond English/Chinese (e.g. poorer Korean translations). In effect, DeepSeek’s strength is Chinese/English, while ChatGPT is broad-spectrum. Table 3 highlights this contrast:

• ChatGPT (GPT-4): Multilingual by design (26+ languages); consistent quality across major languages.

• DeepSeek: Bilingual focus (English + Chinese). Excels on Chinese tests (86.5% CEval vs 76.0% for GPT-4), decent English performance, but less robust on other languages.

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5 Mathematical and Logical Problem-Solving

Both models are strong at math and logic, but DeepSeek’s recent architecture optimizations give it an edge on certain benchmarks. GPT-4 (especially with tools) can solve arithmetic and algebra problems very well (reported ~90% on grade-school math). DeepSeek-V3 reports ~89–90% accuracy on math benchmarks (GSM8K, MATH-500), indicating state-of-art performance even among GPT-4-class models. In fact, DeepSeek outperformed GPT-4o on the MATH-500 test (90.2% vs 74.6%), suggesting superior raw math proficiency in its base. For logical reasoning, both use chain-of-thought: GPT-4 was trained to “refuse to go outside guardrails” and answer step-by-step, while DeepSeek explicitly distills such reasoning from its R1 model into V3. In practice, users report DeepSeek solving puzzles and logical problems comparably to ChatGPT. Overall, neither system has a clear weakness in math/logic, but DeepSeek’s benchmarks imply a slight edge on higher-grade math tasks, whereas GPT-4 is extremely reliable with its advanced prompting tools.

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6 Fine-Tuning, Customization, and Developer Support

• ChatGPT/GPT-4: OpenAI provides a full developer platform. As of mid-2024, GPT-4 can be fine-tuned: developers can customize GPT-4o with their data ($25 per million training tokens, with free token allowances). Inference for fine-tuned GPT-4 costs $3.75/$15 per million tokens. The OpenAI API (also Azure) supports models like gpt-3.5-turbo and GPT-4 via REST or libraries. GPT-3.5 has long been fine-tunable; GPT-4 fine-tuning was recently introduced and is expanding. OpenAI’s documentation and SDKs are extensive, and there is a large ecosystem (StackOverflow, GitHub, LangChain, etc.) around ChatGPT. ChatGPT itself supports plugins and advanced features for developers.

• DeepSeek: Being open-source, DeepSeek is fully customizable. Anyone can download the model weights (via HuggingFace) and fine-tune them on new data. DeepSeek also offers a hosted API with an OpenAI-compatible interface. Its API supports advanced options like multi-round chat, function calling, JSON output, and a specialized “reasoner” mode (R1) for chain-of-thought answers. Official docs and guides (deepseek.com) and a developer platform exist. Community support includes a GitHub (tens of thousands of stars), Discord, WeChat, Twitter channels. In short, DeepSeek is highly open to developers (free commercial use, open models) while ChatGPT provides well-supported but closed cloud APIs.

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7 Training Data Transparency and Recency

Neither model fully discloses its training corpus. OpenAI has been vague about GPT-4’s data (presumably a broad web/text/code scrape up to ~2021–2022), aside from saying it includes books, websites, and code. DeepSeek’s team similarly has not released details of its dataset sources, only reporting aggregate size (14.8T tokens). Both models undergo SFT and RLHF/LLM alignment after pretraining. Notably, DeepSeek was trained very recently (R1 launched January 2025), so its knowledge may extend into late 2024. GPT-4’s knowledge cutoff appears to be around 2021 (earlier, though GPT-4o may have later updates). Critics have raised concerns about data legality: DeepSeek’s license review notes potential issues of “unclear legality of training data”, and OpenAI has faced questions about scraped data from the web. In summary: OpenAI’s data sources and cut-off are undisclosed (likely somewhat dated), while DeepSeek reports scale but not exact sources; data transparency is limited for both.

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8 Real-World Applications and Use Cases

Both models serve broad AI assistant roles, but with different emphasis. ChatGPT (GPT-4) is deployed globally across many domains: it powers chatbots, virtual assistants, coding helpers, content generators, and more. For example, OpenAI reports 80% of Fortune 500 companies use ChatGPT for tasks like drafting communications, coding assistance, data analysis, and creative work. It underlies products like GitHub Copilot (code), Microsoft Bing Chat (search assistant), customer service bots, educational tools, and enterprise platforms. Typical use cases include writing/editing text, brainstorming ideas, tutoring/education, software development help, and automating business processes. ChatGPT’s ChatGPT Enterprise tier is explicitly marketed for secure corporate use (unlimited GPT-4, SOC 2 compliance).

DeepSeek has so far been adopted primarily in China. Its consumer app (a free AI chatbot) became the #1 free download on the Apple App Store shortly after release (Jan 2025). Chinese companies across industries are rushing to integrate DeepSeek: Wired reports 20+ automakers (and a bus maker) adding DeepSeek chat to vehicles, medical/pharma firms using it for clinical research, and banks/insurers using it for customer support and strategy. In effect, DeepSeek-R1 is being pitched for logical decision-making, research assistance, coding, and domain-specific tasks. In everyday terms, DeepSeek is promoted as a general-purpose AI assistant in apps, with strengths in math and Chinese language tasks.

Summary: ChatGPT/GPT-4 is a mature global platform (free and commercial tiers) used for writing, coding, search, business Q&A, etc. DeepSeek (so far) is a rising Chinese platform used in chatbots, coding tools, and enterprise apps, especially where Chinese language or low cost is important.

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9 Safety, Ethics, and Moderation

ChatGPT (GPT-4) undergoes extensive alignment to improve safety. OpenAI has instituted content filters (a Moderation API) and system-level rules so GPT-4 “refuses to go outside guardrails”. GPT-4 was trained with adversarial safety testing to reduce hallucinations and harmful outputs. Nonetheless, it still can err or reflect biases, so OpenAI provides ongoing updates. OpenAI explicitly prohibits disallowed content and trains GPT-4 to reject such queries.

DeepSeek operates under Chinese legal constraints. Its models are censored for sensitive topics: for example, DeepSeek will not discuss Tiananmen Square. The company has reported limiting output on taboo subjects. However, independent analysts warn that DeepSeek’s guardrails and transparency may be weaker than OpenAI’s. DeepSeek’s terms indicate it collects user data (raising privacy concerns) and its custom license forbids certain uses (no military use, disallowed content). Some security researchers have shown DeepSeek’s filters can be bypassed. Additionally, DeepSeek has faced scrutiny from regulators: e.g. Italy blocked its app over data privacy worries and Australia banned it on government devices. In short, ChatGPT provides built-in moderation and clear usage policies (with ongoing research to improve them), whereas DeepSeek must comply with Chinese censorship (avoiding politically sensitive queries) but has been criticized for opaque privacy/ethical safeguards.

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10 Commercial Availability, Deployment, and Licensing

ChatGPT (OpenAI)

Available only as a cloud service. OpenAI’s GPT-4 and ChatGPT are proprietary; users cannot self-host or download the model. Access is via OpenAI’s API (also on Azure) or the ChatGPT web/app. OpenAI offers consumer plans (free GPT-3.5 access, ChatGPT Plus $20/month for GPT-4) and enterprise plans (ChatGPT Enterprise with advanced features). Licensing is closed: you cannot redistribute or embed GPT-4 yourself.

DeepSeek

Open and flexible. DeepSeek’s source code is MIT-licensed and model weights are publicly released under a custom license. In practice, DeepSeek models can be self-hosted on private servers or used via DeepSeek’s own API. The DeepSeek-V3 model (“deepseek-chat”) and the reasoning model (“deepseek-reasoner”) are offered on their platform. Unlike ChatGPT, organizations can download and run DeepSeek locally if desired. However, the DeepSeek license imposes use restrictions (no military use, no disallowed content), meaning it’s not fully open-source by strict definitions. The deepseek.ai site provides documentation and integrations (e.g. developers can use OpenAI-compatible SDKs to call DeepSeek’s service).

	ChatGPT (GPT-4)	DeepSeek (V3/R1)
Availability	Cloud/API only (OpenAI/Azure)	Open-source: local deploy or cloud API
Licensing	Proprietary (no model release)	MIT code; custom model license (use-restricted)
Deployment	SaaS (ChatGPT app, Azure)	Self-host or DeepSeek Cloud
Enterprise Offer	ChatGPT Enterprise (SOC2, privacy)	Not yet a formal enterprise tier; third-party support limited
Updates/Versions	Controlled by OpenAI	Community-driven (new releases via GitHub/HuggingFace)

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11 Pricing Models

ChatGPT/GPT-4

ChatGPT Plus subscription (for end-users) is $20/month, granting GPT-4 access with higher throughput and priority. On the API side, OpenAI charges by token: GPT-4 (8K context) costs $30 per 1M prompt tokens and $60 per 1M completion tokens (i.e. $0.03/$0.06 per 1K tokens). For comparison, GPT-3.5-turbo is ~$2 per 1M tokens. Newer GPT-4 variants (32K) cost more. Note OpenAI often uses precise “prompt” vs “completion” terminology, and offers volume discounts for enterprise.

DeepSeek

DeepSeek’s API is far cheaper per token. The “deepseek-chat” model (V3) has these standard prices: $0.27 per 1M input tokens (cache miss) and $1.10 per 1M output tokens. If the input is cached, it’s only $0.07 per 1M tokens. DeepSeek also offers 50% “off-peak” discounts on these rates. The reasoning model (R1) costs about double: $0.55/$2.19 per 1M (input/output) at peak. In practical terms, GPT-4’s $30/$60 per M vs DeepSeek’s ~$0.27/$1.10 per M means GPT-4 is roughly 50–100× more expensive per token. The table below contrasts per-token prices:

Model	Input Tokens (per million)	Output Tokens (per million)
GPT-4 (API)	$30.00 (i.e. $0.03/1K)	$60.00 (i.e. $0.06/1K)
ChatGPT Plus	$20/month (unlimited usage tier)	–
DeepSeek Chat (V3)	$0.27 (cache-miss) ($0.07 hit)	$1.10
DeepSeek Reasoner (R1)	$0.55 (miss) / $0.14 (hit)	$2.19

So... ChatGPT/GPT-4 is premium-priced (subscription + usage fees) while DeepSeek is budget-friendly (and even free to run locally).

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12 Community, Documentation, and Ecosystem

ChatGPT (OpenAI)

A very large and mature ecosystem surrounds ChatGPT. OpenAI provides extensive official documentation, tutorials, and community forums. There is a rich ecosystem of developer tools, libraries (e.g. OpenAI Python SDK), and frameworks (LangChain, etc.). Online communities (StackOverflow, Reddit, etc.) discuss ChatGPT/GPT-4 extensively. OpenAI also publishes research and has a developer forum. The ChatGPT product itself has millions of users worldwide and integrations (Microsoft Copilot, Bing, etc.) are well-documented.

DeepSeek

As a newcomer, DeepSeek’s ecosystem is smaller but rapidly growing. Its GitHub repositories (V3, R1, Coder) have already amassed tens of thousands of stars (indicative interest). Official documentation (deepseek.ai, Hugging Face repos) is available. DeepSeek runs a Discord channel, Twitter, and WeChat groups for support. Some third-party guides (e.g. DataCamp, blogs) cover usage. However, because DeepSeek originated in China, much of its community and docs are in Chinese, and global resources are just emerging. There are no mature plug-in ecosystems yet. In short, ChatGPT has an extensive, global developer ecosystem, whereas DeepSeek has a nascent but active open-source community (with official docs and community channels available).

So we can say that, overall, ChatGPT’s ecosystem is more established, but DeepSeek benefits from open-source momentum and rapid adoption buzz (especially in China). Next table summarizes some ecosystem aspects...

Aspect	ChatGPT (OpenAI)	DeepSeek
Documentation	Comprehensive (OpenAI docs, guides)	GitHub docs, API docs, blog articles
Community	Very large (global developers)	Growing (Discord, GitHub, Chinese forums)
Integrations/Plugins	Plugins & Partners (Microsoft, etc.)	Few official integrations yet
Open-Source Support	None (model closed)	Full (code and models open)
Third-Party Tools	Many (LangChain, etc.)	Early-stage (some libraries on GitHub)

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