DeepSeek-Math
Last Updated on: Jan 13, 2026
DeepSeek-Math
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What is DeepSeek-Math?
DeepSeek Math (also called DeepSeekMath) is DeepSeek’s specialized, open-source, math-centric large language model. Built on DeepSeek‑Coder‑Base‑7B and further pre-trained on 500B tokens—including 120B from its own math-focused corpus—it excels at competition-level reasoning, achieving 51.7 % on the MATH benchmark and 64.2 % on GSM8K, rivaling models like GPT‑4 and Gemini‑Ultra—all without external toolkits or voting methods .
Who can use DeepSeek-Math & how?
  • Educators & Students: Generate step-by-step math solutions, proofs, and explanations suitable for tutoring or study aids.
  • Data Scientists & Researchers: Automatically solve and verify formal math problems and test theorem proofs.
  • Developers: Embed math reasoning into apps, tutoring platforms, or automated proof tools.
  • AI Practitioners: Fine-tune or build on top of base, instruct, or RL versions for advanced reasoning tasks.
  • Open-Source Enthusiasts: Download and use all three variants under MIT license via Hugging Face.

How to Use DeepSeek Math?
  • Access the Models: Choose from Base, Instruct, or RL checkpoints (`deepseek-ai/deepseek-math-7b-instruct` on Hugging Face).
  • Send Math Prompts: Use chain-of-thought prompts (e.g., “Please reason step by step…”) in English or Chinese for best results.
  • Enable Tool Use: Base is standalone; RL versions can call tools or Python interpreters when enabled.
  • Retrieve Structured Outputs: Receive detailed reasoning chains and final boxed answers or formal proofs.
  • Deploy Freely: Fully MIT-licensed for use in commercial and personal applications.
What's so unique or special about DeepSeek-Math?
  • Nearly Closed-Model Accuracy: First open model approaching GPT-4 and Gemini-Ultra on MATH and GSM8K.
  • Standalone Math Reasoning: Achieves state-of-the-art without external tools or voting mechanisms.
  • Richly Stepped Explanations: Designed to deliver pedagogical chain-of-thought, ideal for education or analysis tools.
  • Multilingual Support: Trained on math tokens from CC and multilingual web, showing strong English and Chinese performance.
  • Three Variants for Flexibility: Includes Base, Instruct, and RL versions for different deployment needs.
Things We Like
  • High benchmark performance without tool reliance
  • Clear, educational chain-of-thought responses
  • Open-source and MIT-licensed for all uses
  • Multiple model styles offer flexibility
  • Trained on massive, specialized math data
Things We Don't Like
  • Still a large 7B model—requires GPU resources
  • Not as general-purpose—tuned specifically for math
  • RL variant may be slower or token-heavy on complex proofs
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FAQs

A math-specialized 7B LLM that excels on benchmarks like MATH (51.7%) and GSM8K (~64%) without relying on tools.
Built on DeepSeek‑Coder‑Base‑7B, then pre-trained on 500B tokens—including 120B math tokens—and refined via chain-of-thought and RL.
Three: Base (pure pre-training), Instruct (SFT), and RL (reinforcement-learned chain reasoning).
Load via Hugging Face; use chain-of-thought prompts, generate step-by-step solutions, and optionally integrate tool use.
Yes—MIT-licensed, including math-specific and RL training checkpoints.

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