DeepSeek delivers frontier class intelligence - open-source, affordable, and relentlessly advancing. From code to science, the future of AI is here.
From general-purpose chat to specialized code and math reasoning — DeepSeek's open model family covers every intelligent task.
The most powerful DeepSeek yet — 1.6 trillion parameter MoE model with GPT-5 class intelligence. Features DeepSeek Sparse Attention, Gold Medal on IMO & IOI 2025, and agentic tool-use. Outperforms GPT-4o on most benchmarks at 1/6 the API cost.
671B parameter MoE model with only 37B active per query. Introduces DeepSeek Sparse Attention (DSA) for 70% reduction in long-context inference costs. Powers deepseek-chat and deepseek-reasoner API endpoints.
Trained entirely via reinforcement learning — no supervised fine-tuning. Develops chain-of-thought reasoning organically. Achieves 97.3% on MATH-500 and top results on competition math, science, and logic problems.
Purpose-built for software engineering. 82.6% on HumanEval, outperforming GPT-4o. Understands full repositories, not just snippets. Supports 340+ programming languages with strong performance on Python, Java, C++, and more.
Multimodal model that understands images, charts, documents, and visual content. Process screenshots, diagrams, and photos alongside text for richer, context-aware responses.
Specialized in mathematical reasoning and theorem proving. Trained on extensive math datasets including competition problems (AMC, AIME, Olympiad), academic papers, and proof verification corpora.
Access DeepSeek through a browser, download the app, integrate via API, or self-host the open-source weights on your own hardware.
DeepSeek powers applications across every domain — from scientific research to enterprise automation and creative work.
Generate, debug, refactor and review code in 340+ languages. Full repository understanding, not just snippets. Ideal for dev automation, boilerplate, and complex algorithms.
Literature review, hypothesis generation, experiment design, and data analysis. DeepSeek-R1 handles multi-step scientific reasoning including chemistry, biology, and physics.
Solve competition math (AMC, AIME, Olympiad), university-level problems, theorem proving, and quantitative finance. Achieved IMO 2025 Gold Medal.
Extract, summarize and analyze PDFs, reports, legal documents and contracts. 128K context window processes entire books in a single prompt.
Build intelligent agents that process emails, fill forms, route tickets and automate workflows. Tool-use and structured output for seamless integration.
Write, translate and localize content in 30+ languages with native-level quality. Strongest Chinese-language AI available — outperforms GPT-4o and Claude on Chinese benchmarks.
Personalized step-by-step explanations in math, science, coding and languages. Adaptive to student level with patient, structured explanations and practice problems.
Brainstorm, draft, edit and polish articles, stories, marketing copy, scripts and more. Maintain long-context narrative consistency across book-length projects.
Interpret datasets, write analysis scripts, generate charts (via code), perform statistical analysis and summarize insights. Works with CSV, SQL, and pandas workflows.
DeepSeek's architectural innovations set it apart from every other frontier model.
671B total parameters, only 37B active per token. Dramatically reduces compute cost while maintaining frontier-level intelligence. Efficient without compromise.
Novel training objective that predicts multiple future tokens simultaneously — improving reasoning over long contexts and boosting sample efficiency by 3×.
All model weights, technical reports, and training recipes published under MIT License. Self-host, fine-tune, and build commercial products freely — no restrictions.
Novel attention mechanism that compresses the KV cache into latent vectors, reducing memory footprint up to 90% while preserving full model expressiveness.
First frontier model trained end-to-end in FP8 precision. Achieves 60% memory reduction and 30% faster training throughput compared to BF16 baselines.
DeepSeek Sparse Attention reduces long-context inference complexity from O(L²) to O(kL), cutting inference costs by 70% for long documents and codebases.
DeepSeek-R1 trained via pure RL — no supervised fine-tuning at all. Emergent chain-of-thought reasoning, self-verification, and backtracking develop naturally.
Native function calling, structured JSON output, and multi-turn agent workflows. DeepSeek-V4 introduces large-scale agentic task synthesis for real-world automation.
Drop-in replacement for OpenAI's API. Change 2 lines of code — base URL and key — to migrate any existing ChatGPT integration to DeepSeek instantly.
From zero to intelligent AI integration in minutes — whether you're a casual user or enterprise developer.
Use the free web chat, download the iOS/Android app, or sign up for an API key at platform.deepseek.com for developer access.
For general questions use deepseek-chat (V3.2). For complex math and reasoning, enable DeepThink mode or use deepseek-reasoner (R1). For code, use DeepSeek-Coder V2.
Be specific. Include context, constraints, and desired output format. DeepSeek excels at structured tasks — ask for JSON, tables, step-by-step solutions, or code with explanations.
Toggle DeepThink (R1) mode for math, coding challenges, or multi-step reasoning. The model will show its chain-of-thought before the final answer.
Install pip install openai. Set base_url="https://api.deepseek.com" and your API key. Use models deepseek-chat or deepseek-reasoner. That's it — fully OpenAI compatible.
Use consistent system prompts to maximize cache hit rates (saves 90% on input costs). Cache hits cost $0.028/1M vs $0.28/1M for misses — a 10× difference.
def is_prime(n): if n < 2: return False for i in range(2, int(n**0.5)+1): if n % i == 0: return False return TrueO(√n) time — much faster than checking all divisors up to n.DeepSeek uses pay-per-token pricing with no monthly subscription required. The web chat is completely free.
Full access to DeepSeek's chat interface with no account required. Unlimited conversations with the flagship model.
Free iOS and Android app with all web features plus voice input, widgets, and offline conversation management.
New API accounts receive $5 in free credits — enough to process ~18M input tokens. No credit card required to start.
| Model | Context | Input (Cache Hit) | Input (Cache Miss) | Output | Max Output |
|---|---|---|---|---|---|
| deepseek-chat (V3.2) | 128K | $0.028/1M | $0.28/1M | $0.42/1M | 8K |
| deepseek-reasoner (R1/V3.2) | 128K | $0.028/1M | $0.28/1M | $0.42/1M | 64K (CoT) |
| deepseek-v4-pro | 128K | $0.145/1M | $1.74/1M | $3.48/1M | 65K |
| deepseek-v4-flash | 128K | $0.02/1M | $0.20/1M | $0.60/1M | 65K |
Run smaller distilled models (1.5B–70B) locally using Ollama, LM Studio, or llama.cpp. Requires 8–48GB VRAM depending on model size.
Self-host the full 671B (V3) or 1.6T (V4) model. Requires multi-GPU server infrastructure (8×A100 for V3). Ideal for enterprises processing millions of requests.
Access DeepSeek models via AWS Bedrock, Azure AI, Google Vertex AI, Together AI, Fireworks, and Groq for enterprise compliance and SLAs.
Rigorous academic benchmarks comparing DeepSeek models against GPT-4o, Claude, and Gemini across coding, math, and reasoning.
● BENCHMARK SCORES (DeepSeek V3.2 vs Competitors)
● FULL MODEL COMPARISON TABLE
| Feature | DeepSeek | GPT-4o | Claude | Gemini |
|---|---|---|---|---|
| Input Price /1M | $0.28 | $2.50 | $3.00 | $1.25 |
| Output Price /1M | $0.42 | $10.00 | $15.00 | $5.00 |
| Context Window | 128K | 128K | 200K | 1M |
| Open Source | ✓ MIT | ✗ | ✗ | ✗ |
| Self-Hostable | ✓ Yes | ✗ No | ✗ No | ✗ No |
| HumanEval Score | 90.2% | 87.1% | 85% | 82% |
| MATH-500 | 97.3% | 76.6% | 71.1% | 80% |
| Chain-of-Thought | ✓ Native | ✓ o-series | ✓ Extended | ✓ Deep Think |
| API Compatible | OpenAI API | OpenAI API | Anthropic | Google AI |
| Free Web Access | ✓ Unlimited | ✓ Limited | ✓ Limited | ✓ Limited |
DeepSeek is exceptional — but like all AI systems, it has real strengths to leverage and limitations to be aware of.
Up to 95% cheaper than GPT-5. $0.28/1M input tokens vs $15+ for Claude Opus. Best cost-per-intelligence ratio available.
R1 achieved 97.3% on MATH-500, IMO 2025 Gold Medal. Coder V2 scores 82.6% on HumanEval, beating GPT-4o.
Full MIT license — self-host, fine-tune, build commercial products freely. No access restrictions or waiting lists.
Outperforms GPT-4o and Claude on Chinese SimpleQA. Ideal for bilingual applications and Chinese-language content.
Change 2 lines of code to migrate from ChatGPT. Zero rewriting of existing integrations.
DeepSeek is a Chinese company. Conversations may be stored on servers in China. Not suitable for sensitive government or regulated enterprise data without self-hosting.
The model avoids discussing certain sensitive political topics related to China. Content filters may restrict some responses that other models answer freely.
DeepSeek-R1's chain-of-thought is powerful but slow — each reasoning response can take 15–60 seconds. Not ideal for latency-sensitive production apps.
High demand can cause rate limiting or slow responses on the free tier. Enterprises should use API credits or cloud provider deployments for reliability.
Vision support (DeepSeek-VL) is not as mature as GPT-4V or Gemini Vision. No native audio input or image generation.
Yes — chat.deepseek.com and the mobile apps are completely free with no usage limits for the chat interface. New API accounts also receive $5 in free credits. For high-volume API usage, you pay per token — starting at just $0.028/1M tokens with cache hits. There is no mandatory subscription plan.
A token is the smallest unit of text a model processes — roughly 0.75 words (or ~4 characters) in English. "Hello, world!" is about 4 tokens. The API charges separately for input tokens (what you send) and output tokens (what the model generates). With DeepSeek-V3.2, input tokens with cache hits cost $0.028/1M, cache misses $0.28/1M, and output tokens $0.42/1M. A typical 500-word conversation costs well under $0.01. DeepSeek also automatically caches repeated prompts (like system messages), which saves 90% on repeated inputs.
DeepThink (powered by DeepSeek-R1) enables chain-of-thought reasoning where the model explicitly "thinks through" a problem before answering. You can see its internal reasoning steps. It's significantly better at complex math, logic puzzles, and multi-step problems — but takes longer (15–60 seconds per response). Toggle it on in the chat interface with the "DeepThink" button, or use the deepseek-reasoner model via API. For simple questions, standard mode is faster and cheaper.
For non-sensitive business use (marketing copy, code review, public data analysis), the cloud API is fine. However, DeepSeek is a Chinese company — data processed through the public API may be stored on servers subject to Chinese law. For sensitive data (healthcare, finance, legal documents), we recommend either self-hosting the open-source model weights on your own infrastructure, or using DeepSeek via compliant cloud providers (AWS Bedrock, Azure AI) that offer HIPAA/SOC2 SLAs and data residency guarantees.
DeepSeek's API is fully OpenAI-compatible. Update just two lines: set base_url="https://api.deepseek.com" and your DeepSeek api_key. Change the model name to deepseek-chat (standard) or deepseek-reasoner (reasoning). All your existing code for streaming, function calling, and structured outputs works without any other changes. Most users migrate in under 5 minutes.
Yes! DeepSeek is fully open source under MIT License. For local use, the easiest method is Ollama — run ollama pull deepseek-v2 to download a quantized version. Smaller models (1.5B–7B) run on 8GB VRAM. The 67B version requires 40GB VRAM. For the full 671B V3 model, you'll need a multi-GPU server (8×A100 or equivalent). LM Studio and llama.cpp are also supported.
DeepSeek-V3 (and V3.2) is the general-purpose flagship model — optimized for speed, versatility, and breadth. It handles chat, code, writing, and analysis efficiently. DeepSeek-R1 is a reasoning-specialized model trained via reinforcement learning to develop explicit chain-of-thought. R1 dramatically outperforms V3 on math, logic, and complex reasoning tasks, but is slower and more verbose. For everyday tasks, use V3. For hard math, science, or puzzles, use R1 (DeepThink mode).
Yes. DeepSeek-V3.2 and V4 support OpenAI-compatible function calling (tool use). You can define functions in the same JSON schema format as OpenAI. DeepSeek-V4 adds a large-scale agentic task synthesis pipeline that significantly improves tool-use accuracy and generalization for building AI agents and automation workflows. Structured output (JSON mode) is also supported.
DeepSeek was founded in 2023 as a subsidiary of High-Flyer Capital Management, a prominent Chinese quantitative hedge fund. The company is headquartered in Hangzhou, China. It became globally prominent in January 2025 when DeepSeek-R1 matched GPT-4 performance while reportedly costing only ~$5.5M to train — compared to GPT-4's $100M+. The team publishes all research openly and releases model weights under MIT License.
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