DeepSeek-R1
https://github.com/deepseek-ai/deepseek-r1- Category
- AI Tools
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- No. 507Tools index
- Pricing
- Open Source
- Type
- TOOL
- Builder
- deepseek-ai
- GitHub
- 92.0k stars
- Latest release
- v1.0.0
- Date
About
Open-source reasoning model from DeepSeek trained via reinforcement learning, with chain-of-thought reasoning competitive with closed frontier models.
What it does
DeepSeek-R1 is a family for solving math, coding, and general reasoning problems. Its training pipeline combines cold-start examples, two reinforcement-learning stages, and two supervised fine-tuning stages. Six smaller variants transfer reasoning patterns from the large model into dense Qwen and Llama bases.
Why it's ranked here
The case is strong but specific. Published evaluations show excellent math and coding results, including 97.3 on MATH-500 and 65.9 on LiveCodeBench. The project also offers checkpoints from 1.5B to 70B, an OpenAI-compatible API, and an MIT license. However, several benchmark rivals still lead on factual accuracy, instruction following, and software repair.
What's good
You can choose between the large mixture-of-experts model and six smaller distilled checkpoints. The size range makes the research useful beyond teams able to host the 671B-parameter model. Access is flexible through downloadable checkpoints, a hosted chat interface, or an OpenAI-compatible API. The documented evaluations cover English, Chinese, math, code, and general reasoning.
Tradeoffs
The main model is operationally heavy: it has 671B total parameters, with 37B activated, and local setup guidance points to another repository. The documentation says the distilled models modify their base configurations and tokenizers, so generic defaults may be wrong. Results also vary by task. DeepSeek-R1 trails comparison models on instruction following, simple factual questions, and the reported software-repair benchmark.
How to use it well
It suits researchers and engineers testing difficult math, code, or multi-step reasoning. Start with the hosted API for evaluation, then select a distilled checkpoint when local operation matters. Follow the supplied model settings, especially for altered tokenizers and configurations. It does not cover turnkey full-model deployment by itself, because those instructions live with the underlying base architecture.
Technical notes+
README.md documents DeepSeek-R1 and DeepSeek-R1-Zero as 671B-parameter mixture-of-experts models with 37B activated parameters and 128K context, then links six distilled Hugging Face checkpoints based on Qwen2.5 and Llama3 families. It exposes hosted chat and an OpenAI-compatible API, while redirecting full-model local execution details to the DeepSeek-V3 repository. LICENSE contains the MIT license. .github/workflows/stale.yml defines a scheduled Ubuntu GitHub Actions job using actions/stale@v9; it exempts pinned and security issues and does not mark pull requests stale.
Observed
- License
- MIT
- Interfaces
- Hosted chat website and OpenAI-compatible API
- Checkpoint distribution
- DeepSeek-R1, DeepSeek-R1-Zero, and six distilled models are linked through Hugging Face
- Distilled model sizes
- 1.5B, 7B, 8B, 14B, 32B, and 70B parameters
- Full model architecture
- Mixture of experts with 671B total parameters and 37B activated parameters
- Context length
- 128K for DeepSeek-R1 and DeepSeek-R1-Zero
Read from README.md, LICENSE, .github/workflows/stale.yml.
What it can do
Generate step-by-step reasoning for complex problems
Complex question or problem statement → Chain-of-thought reasoning process with detailed steps
Answer questions using logical reasoning
Natural language questions → Reasoned answers with explanation of thought process
Solve mathematical problems with detailed work
Mathematical equations or word problems → Step-by-step mathematical solutions
Analyze and break down complex scenarios
Complex situations or scenarios → Structured analysis with reasoning chains
Generate code solutions with logical explanations
Programming problems or requirements → Code with reasoning about implementation choices
Perform logical inference and deduction
Premises and logical statements → Logical conclusions with reasoning steps
Intel on DeepSeek-R1
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