Open-source AI with 128K-token context, excelling in formal theorem proving and mathematical reasoning.
DeepSeek Prover V2 Model Description
DeepSeek Prover V2, developed by DeepSeek, is an open-source large language model tailored for formal theorem proving in Lean 4. Built on DeepSeek-V3, it excels in mathematical reasoning, decomposing complex problems into subgoals for precise proof construction. With a 671-billion-parameter architecture, it’s ideal for advanced mathematical and logical tasks, accessible via Hugging Face and DeepSeek’s API platform.
Technical Specifications
Performance Benchmarks
DeepSeek Prover V2 is a 671-billion-parameter model (37 billion active per token) using a Mixture-of-Experts (MoE) architecture, initialized with a recursive theorem-proving pipeline powered by DeepSeek-V3. It employs Multi-head Latent Attention (MLA) and DeepSeekMoE for efficient inference, with cold-start data synthesis and reinforcement learning for enhanced reasoning.
Context Window: 32K tokens (7B model), extendable to 128K for 671B model.
Benchmarks:
MiniF2F-test: 88.9% pass ratio, outperforming all open-source models.
PutnamBench: Solves 49/658 problems, leading neural theorem proving.
ProverBench (325 problems, including AIME 24/25): State-of-the-art results.
Cost for 1,000 tokens: $0.00077 (input) + $0.0024 (output) = $0.00317 total
Performance Metrics
DeepSeek Prover V2 metrics
Key Capabilities
DeepSeek Prover V2 specializes in formal theorem proving, integrating informal and formal reasoning via a recursive proof search pipeline. It decomposes complex mathematical problems into manageable subgoals, synthesizing proofs with step-by-step chain-of-thought reasoning.
Formal Theorem Proving: Generates and verifies Lean 4 proofs, achieving 88.9% on MiniF2F-test, surpassing all competitors.
Chain-of-Thought Reasoning: Combines DeepSeek-V3’s reasoning with formal proofs for cohesive outputs.
Scalable Inference: MoE architecture with 37B active parameters ensures efficient computation on large-scale tasks.
Multilingual Support: Handles mathematical notation and problem statements in multiple languages.
Tool Integration: Supports Lean 4 proof assistant for automated verification and proof construction.
API Features: Offers structured outputs, reinforcement learning feedback, and OpenAI-compatible API endpoints.
Optimal Use Cases
DeepSeek Prover V2 is designed for scenarios requiring rigorous mathematical and logical reasoning:
Mathematical Research: Formalizing proofs for number theory, algebra, and geometry in Lean 4.
Educational Tools: Assisting students with competition-level math problems (e.g., AIME, Putnam).
Automated Theorem Proving: Developing and verifying formal proofs for academic and industrial applications.
Scientific Analysis: Supporting logical reasoning in fields like theoretical physics and computer science.
AI-Driven Logic Systems: Building reasoning engines for automated proof assistants.
Comparison with Other Models
DeepSeek Prover V2 excels in formal theorem proving, outperforming general-purpose models in specialized math tasks:
vs. Qwen3-235B-A22B: Matches AIME 2025 performance but surpasses in formal proving (MiniF2F: 88.9% vs. ~80%), though slower (35 vs. 40.1 tokens/second).
vs. Gemini 2.5 Flash: Far superior in theorem proving (MiniF2F: 88.9% vs. ~60%) but lacks multimodality and has higher latency (1.2s vs. 0.8s).
vs. DeepSeek-R1: Stronger in formal proving (MiniF2F: 88.9% vs. ~75%) but less versatile for general reasoning tasks.
vs. Claude 3.7 Sonnet: Outperforms in neural theorem proving (PutnamBench: 49/658 vs. ~40/658), with lower costs ($0.00317 vs. ~$0.015 per 1K tokens).
Code Samples
Limitations
Limited to text-based mathematical reasoning; no vision or multimodal capabilities.
High latency (1.2s TTFT) for real-time applications.
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