DeepSeek quietly open sources Prover-V2, a math-focused, 671B-parameter AI model using mixture-of-experts, on Hugging Face, one day after Alibaba released Qwen3
DeepSeek had already established a public model-release pattern with its 671B-parameter open-source DeepSeek-V3, while Alibaba had put an open reasoning model, QwQ-32B, on the same distribution channels. Prover-V2 extends that contest into a more specialized math-model category.
The timing, immediately after Qwen3 and ahead of the anticipated DeepSeek-R2, makes the release significant less as a standalone launch than as another rapid iteration in Chinese open-model competition.
First-order effects
Developers and researchers can now obtain and evaluate Prover-V2 through Hugging Face, adding a large mixture-of-experts option aimed at mathematical tasks to the openly available model pool.
DeepSeek immediately counters Alibaba's Qwen3 release with a specialized model release of its own, keeping attention on its open-source roadmap before R2.
Second-order effects
Alibaba and other open-model vendors face stronger pressure to differentiate on reasoning performance, specialization, deployment efficiency, or release cadence rather than merely offering model access.
Organizations evaluating open models gain another candidate for math-heavy workloads, increasing the value of disciplined task-specific benchmarking rather than treating a general-purpose release as a default choice.
Third-order effects
If this release cadence persists, open-weight AI competition may fragment into purpose-built models, with math and reasoning becoming distinct procurement categories alongside general-purpose systems.
Repeated public releases by Chinese vendors could strengthen buyer leverage where organizations can test multiple accessible model families, although practical adoption will still depend on evaluation and deployment constraints.
The trend: The story is part of a shift from isolated flagship-model launches toward rapid, open distribution of specialized models that compete on both capability focus and availability.
We just released DeepSeek-Prover V2. - Solves nearly 90% of miniF2F problems - Significantly improves the SoTA performance on the PutnamBench - Achieves a non-trivial pass rate on AIME 24 & 25 problems in their formal version Github: https://github.com/... [image]
🐳 New: DeepSeek Prover v2, also available for free It's a 671B parameter model. Not much is known about it yet, but it's likely an upgrade from DeepSeek-Prover-V1.5, which leveraged “Proof Assistant Feedback for Reinforcement Learning.” Try it out and let us know what you [image]
New DeepSeek model, live now - no official description yet. We've made a few Lean 4 proofs with it so far. Two providers on openrouter, @rayon_labs chutes and @novita_labs, were extremely fast to host it!
What I really like with DeepSeek: they are uncompromisingly following on the plan to build artificial intelligence™, even if it means releasing a giant model for five people (among them, only two who have the GPUs).
Alpha users on @sfcompute can inference @deepseek_ai v2 Prover 671B on 24 H100s IB at market rate since a few minutes after downloaded, and cache compiled. @vllm_project project, multi-node LWS ready. $0.44/gpu/hr right now per hour, but market rates apply. [image]
DeepSeek Unveils Advanced Model for Mathematical Theorem Proving #DeepSeek has released a new open-source model, DeepSeek-Prover-V2-671B, focusing on mathematical theorem proving tasks. The model is built on a mixture of experts (MoE) architecture and utilizes the Lean 4 [image]