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Model details

Deepseek Prover V2 671B

DeepSeek-Prover-V2-671B is a large-scale model specifically architected to address the rigorous demands of formal theorem proving in Lean 4. By focusing on the structural requirements of mathematical verification, the model is designed to decompose complex logical problems into manageable subgoals. This systematic approach allows it to navigate intricate proofs, making it a powerful tool for researchers and developers who require high-precision reasoning and reliable verification in formal language environments.

The model benefits from a specialized training lineage that utilizes a recursive theorem proving pipeline. During its development, the system employed a cold-start procedure where DeepSeek-V3 was prompted to break down problems, synthesizing these resolved subgoals into a structured chain-of-thought process. This method of expert cultivation ensures that the model is well-equipped to handle step-by-step logical deductions. By leveraging this sophisticated initialization data, the model demonstrates significant strengths in automated reasoning, positioning it as a forward-looking asset for advanced computational mathematics and formal verification tasks.

NovitaAIdeepseek/deepseek-prover-v2-671b

Quick Info

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Provider
NovitaAI
Model key
deepseek/deepseek-prover-v2-671b
Release date
Apr 30, 2025
Last updated
Apr 30, 2025
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.70
Output token cost
$2.50

Limits

Output tokens
160,000 tokens
Context window
160,000 tokens

Latest news about Deepseek Prover V2 671B

NovitaAI

Coverage

A third-party guide on DeepSeek-Prover-V2-671B confirms the model was released on April 30, 2025, as the next-generation automated theorem proving model in DeepSeek's open-weight lineup. It is built on the same 671 billion-parameter Mixture-of-Experts (MoE) architecture that powers DeepSeek-V3, with an estimated 37 bil Key reported specifications include a context length of approximately 128,000 tokens to accommodate lengthy proofs and complex reasoning chains, and a likely Multi-Head Latent Attention (MLA) mechanism inherited from DeepSeek-V2 that compresses the KV cache to reduce RAM and VRAM requirements. The guide notes the model

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