# DeepSeek-Math-V2

> 685B open-weight self-verifying math prover that scores IMO/CMO gold and 118/120 on Putnam 2024.

- Maker: DeepSeek
- Line: DeepSeek Math
- Canonical page: https://ai-tldr.dev/models/deepseek-math-v2/
- Updated: 2026-06-18

## In short

DeepSeek-Math-V2 is a specialized mathematical-reasoning model from DeepSeek, released on 27 November 2025 as the current flagship of the DeepSeek Math line. It is a 685-billion-parameter Mixture-of-Experts model built on the DeepSeek-V3.2-Exp-Base checkpoint, and it is published as open weights on Hugging Face under the Apache 2.0 license. It has a 128K-token context window. You can use it through Hugging Face (open weights).

## Facts

| Fact | Value | Source |
| --- | --- | --- |
| Context window | 128K | [1] |
| Licence | Apache-2.0 · open weights | [1] |
| Released | 27 November 2025 | [2] |
| Parameters | 685B total (MoE) | [1] |

## Sources

1. [DeepSeek-Math-V2 model card (Hugging Face)](https://huggingface.co/deepseek-ai/DeepSeek-Math-V2)
2. [DeepSeek-Math-V2 GitHub repository](https://github.com/deepseek-ai/DeepSeek-Math-V2)

## Overview

DeepSeek-Math-V2 is a specialized mathematical-reasoning model from DeepSeek, released on 27 November 2025 as the current flagship of the DeepSeek Math line. It is a 685-billion-parameter Mixture-of-Experts model built on the DeepSeek-V3.2-Exp-Base checkpoint, and it is published as open weights on Hugging Face under the Apache 2.0 license. Unlike a general chat model, DeepSeek-Math-V2 is tuned for writing rigorous natural-language proofs rather than just producing a final numeric answer.

The model's defining idea is self-verification. DeepSeek trains an LLM-based verifier with reinforcement learning (GRPO) that scores a candidate proof on rigor and completeness, plus a meta-verifier that checks the verifier's own critiques so it does not hallucinate flaws. The proof generator is then rewarded for producing solutions whose self-assessed quality matches the verifier's judgment, and it iteratively repairs its own proofs within a 128K-token context before finalizing an answer. This 'verify-first' pipeline is what lets DeepSeek-Math-V2 catch and fix its own mistakes the way a human mathematician re-checks a proof.

With scaled test-time compute and large verification budgets, DeepSeek-Math-V2 reaches gold-medal-level performance on IMO 2025 and CMO 2024 and scores 118 of 120 on Putnam 2024 — above the best human score that year. On the IMO-ProofBench evaluation it outperforms Google DeepMind's DeepThink IMO Gold on the Basic subset and stays competitive on the Advanced subset, making it one of the strongest openly released math-reasoning models to date. Because it is open-weight, researchers can download and run it via the DeepSeek-V3.2-Exp inference stack.

## Benchmarks

- Putnam 2024: 118 ([source](https://huggingface.co/deepseek-ai/DeepSeek-Math-V2))
- IMO 2025 (problems solved): 5 ([source](https://simonwillison.net/2025/Nov/27/deepseek-math-v2/))

## Strengths

- Open Apache-2.0 weights at frontier scale (685B MoE), free for research and commercial use and fully self-hostable
- Self-verifying proof pipeline: a GRPO-trained verifier plus meta-verifier let the model critique and repair its own proofs before answering
- Gold-medal-level results on elite math olympiads (IMO 2025, CMO 2024) and a near-perfect 118/120 on Putnam 2024
- Writes rigorous natural-language proofs, not just final answers — built for theorem-proving rather than arithmetic
- Beats DeepMind's DeepThink IMO Gold on the IMO-ProofBench Basic subset among compared systems

## Best for

- Automated theorem proving and verification of multi-step mathematical proofs
- Research on self-verification, verifier-reward training, and test-time-compute scaling for reasoning
- Generating and grading olympiad-style and competition math solutions
- Self-hosted math-reasoning deployments where open Apache-2.0 weights are required
- Building proof-checking or math-tutoring tools on top of an open-weight reasoning model

## How to access

- [Hugging Face (open weights)](https://huggingface.co/deepseek-ai/DeepSeek-Math-V2) — model id `deepseek-ai/DeepSeek-Math-V2`

## DeepSeek Math versions

- DeepSeek-Math-V2 (27 November 2025) — this page
- [DeepSeekMath](https://ai-tldr.dev/models/deepseek-math/) (April 2024)

## FAQ

### Is DeepSeek-Math-V2 open source?

The weights are published openly on Hugging Face under the Apache 2.0 license, so you can download, run, fine-tune, and use DeepSeek-Math-V2 for research and commercial purposes, including self-hosted deployment. It is run with the DeepSeek-V3.2-Exp inference stack rather than a hosted DeepSeek API endpoint.

### How big is DeepSeek-Math-V2?

It is a 685-billion-parameter Mixture-of-Experts model built on the DeepSeek-V3.2-Exp-Base checkpoint, and it works within a 128K-token context. It is text-only and specialized for mathematical proof writing rather than general chat.

### What makes DeepSeek-Math-V2 different from other reasoning models?

It is trained to self-verify. DeepSeek trains an LLM verifier (with GRPO) and a meta-verifier that score proofs on rigor and completeness, and the proof generator is rewarded for producing solutions that hold up to that verification — iteratively repairing its own proofs before finalizing an answer.

### How well does DeepSeek-Math-V2 perform on math competitions?

With scaled test-time compute it reaches gold-medal-level performance on IMO 2025 and CMO 2024 and scores 118 of 120 on Putnam 2024, above the top human score that year. On IMO-ProofBench it outperforms DeepMind's DeepThink IMO Gold on the Basic subset and stays competitive on the Advanced subset.

## Links

- [DeepSeek-Math-V2 model card (Hugging Face)](https://huggingface.co/deepseek-ai/DeepSeek-Math-V2)
- [DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning (arXiv)](https://arxiv.org/abs/2511.22570)
- [DeepSeek-Math-V2 GitHub repository](https://github.com/deepseek-ai/DeepSeek-Math-V2)
- [MarkTechPost — DeepSeekMath-V2 release coverage](https://www.marktechpost.com/2025/11/28/deepseek-ai-releases-deepseekmath-v2-the-open-weights-maths-model-that-scored-118-120-on-putnam-2024/)
- [Simon Willison — DeepSeek-Math-V2 notes](https://simonwillison.net/2025/Nov/27/deepseek-math-v2/)

Source: AI/TLDR LLM registry, https://ai-tldr.dev/models/deepseek-math-v2/