DeepSeek Math V2: Open Source Math Reasoner With Olympiad Level Proofs

DeepSeek Math V2

DeepSeek Math V2 is the clearest signal yet that open source models can compete at the very top of mathematical reasoning. It is a Mixture of Experts model at 685 billion parameters, trained specifically for proofs, derivations and structured reasoning instead of casual numerical answers.

On recent benchmarks, DeepSeek Math V2 reaches gold medal level performance on the International Mathematical Olympiad 2025 and China’s Mathematical Olympiad, and scores 118 out of 120 points on the 2024 Putnam exam. In other words, it performs at the level of the world’s best competition mathematicians on extremely hard problems.

Built around proofs, not just answers

Most models that “get good at math” do so by optimizing for the final answer. That works for contest formats where answers are short, but it breaks down for:

• Formal proofs
• Research style problems
• Tasks where you must show every step and justify each move

DeepSeek Math V2 is trained around a verifier generator architecture. The pipeline looks roughly like this:

1. A verifier model is trained to judge not only correctness, but rigor and faithfulness of proofs.
2. A meta verifier improves robustness by catching edge cases and subtle errors.
3. A generator model produces candidate proofs.
4. The verifier and meta verifier score those proofs, and their feedback is used as a reinforcement signal to train the generator.

During inference, the model can run multiple refinement cycles. It generates a proof, checks it with its verifier stack, then repairs and extends the proof until it passes internal quality thresholds or hits resource limits. This self verification loop dramatically reduces hand waving and false confidence.

Competition grade results

On published evaluations, DeepSeek Math V2 achieves:

• Gold level scores on IMO 2025 under official scoring
• Gold level performance on CMO 2024 style benchmarks
• A near perfect 118 out of 120 on Putnam 2024 when using scaled test time compute

Those numbers matter because they show two things at once:

• The model can handle long, multi step reasoning chains across algebra, geometry, combinatorics and number theory.
• The generated solutions include proofs judged correct under competition criteria, not just lucky final answers.

This pushes mathematical models closer to being partners in proof development, not just calculators with extra steps.

Open weights under Apache 2.0

DeepSeek has released Math V2 as open weights under the Apache 2.0 license, with checkpoints available on platforms like Hugging Face and code on GitHub.

That matters for several reasons:

• Researchers can inspect, finetune and extend the model for specific domains.
• Tool builders can integrate it into math tutors, grading systems and interactive theorem proving tools without locked licenses.
• Formal methods communities can explore connections between large language model proofs and existing proof assistants.

In short, an IMO gold level engine is now something you can download and run, not just a capability locked in proprietary APIs.

Practical use cases beyond contests

While the headline results come from competitions, DeepSeek Math V2 is interesting for broader applications where structured reasoning is key:

• Advanced tutoring systems for high school and university math, capable of walking through full solutions instead of giving final answers only
• Automated problem generation and solution checking for contest training platforms
• Research assistants that can draft candidate proofs, counterexamples or explorations for human mathematicians
• Bridges to formal proof systems, where LLM generated proofs can be translated, checked and refined in tools like Lean or Coq

Because the model is designed to verify its own reasoning, it is better suited for workflows where trust and explainability matter more than raw speed.

Designing systems around DeepSeek Math V2

If you are building AI products with serious math inside, DeepSeek Math V2 can play several roles:

• Core reasoning engine for systems that must solve or explain advanced problems
• Oracle used in ensemble setups where cheaper models handle routine queries and delegate hard problems to Math V2
• Verification layer that cross checks proofs generated by other models or humans

In all of these cases, the key design principle is simple: treat the model as a proof partner. Give it room to think, refine and verify instead of forcing it into single shot responses.

Why DeepSeek Math V2 matters for the AI ecosystem

The model sends a clear message: open source can reach frontier level in highly specialized domains, not just as a “good enough” copy of closed systems.

By combining:

• A huge Mixture of Experts backbone
• A carefully designed verifier first training loop
• Open weights and a permissive license

DeepSeek Math V2 opens up a new round of work on trustworthy, self checked reasoning systems. It shows that scaling test time compute plus explicit verification can push performance far beyond “chatbot that acts confident,” and into the territory of tools mathematicians and educators may actually rely on.