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DeepSeek Prover V2

DeepSeek’s Prover V2, a 671B-parameter MoE model, specializes in Lean 4 theorem proving, achieving 88.9% on MiniF2F-test.

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Javascript

Python

                                        const { OpenAI } = require('openai');

const api = new OpenAI({
  baseURL: 'https://api.ai.cc/v1',
  apiKey: '',
});

const main = async () => {
  const result = await api.chat.completions.create({
    model: 'deepseek/deepseek-prover-v2',
    messages: [
      {
        role: 'system',
        content: 'You are an AI assistant who knows everything.',
      },
      {
        role: 'user',
        content: 'Tell me, why is the sky blue?'
      }
    ],
  });

  const message = result.choices[0].message.content;
  console.log(`Assistant: ${message}`);
};

main();

                                        import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.ai.cc/v1",
    api_key="",    
)

response = client.chat.completions.create(
    model="deepseek/deepseek-prover-v2",
    messages=[
        {
            "role": "system",
            "content": "You are an AI assistant who knows everything.",
        },
        {
            "role": "user",
            "content": "Tell me, why is the sky blue?"
        },
    ],
)

message = response.choices[0].message.content

print(f"Assistant: {message}")

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DeepSeek Prover V2

Product Detail

DeepSeek Prover V2, developed by DeepSeek, is an advanced open-source large language model specifically engineered for formal theorem proving in Lean 4. Built upon the robust DeepSeek-V3 architecture, this model excels in complex mathematical reasoning, adeptly breaking down intricate problems into manageable subgoals for precise proof construction. With a formidable 671-billion-parameter architecture, it stands as an ideal solution for advanced mathematical and logical tasks, readily accessible via Hugging Face and DeepSeek’s API platform.

🚀 Technical Specifications & Performance

DeepSeek Prover V2 is a monumental 671-billion-parameter model, leveraging a Mixture-of-Experts (MoE) architecture with 37 billion active parameters per token for unparalleled efficiency. Its foundation is a recursive theorem-proving pipeline powered by DeepSeek-V3, enhanced with Multi-head Latent Attention (MLA) and DeepSeekMoE for optimal inference. The model's reasoning capabilities are further boosted by cold-start data synthesis and sophisticated reinforcement learning techniques.

✓ Context Window: 32K tokens (for the 7B model), extendable to an impressive 128K tokens for the 671B model.
✓ Output Speed: Achieves 35 tokens/second with a low latency of 1.2s (Time To First Token - TTFT).
✓ API Pricing:
- — Input tokens: $0.553875 per million tokens
- — Output tokens: $2.414885 per million tokens

🌟 Performance Benchmarks

★ MiniF2F-test: Achieves an outstanding 88.9% pass ratio, outperforming all other open-source models.
★ PutnamBench: Successfully solves 49/658 problems, setting a new benchmark in neural theorem proving.
★ ProverBench: Delivers state-of-the-art results on 325 problems, including AIME 24/25.
★ AIME 2025: Demonstrates competitive performance with models like Qwen3-235B-A22B.

DeepSeek Prover V2 metrics illustrate its superior performance across various benchmarks.

💡 Key Capabilities of DeepSeek Prover V2

DeepSeek Prover V2 is expertly designed for formal theorem proving, seamlessly integrating informal and formal reasoning through a recursive proof search pipeline. It intelligently decomposes complex mathematical challenges into manageable subgoals, synthesizing proofs with detailed, step-by-step chain-of-thought reasoning.

✅ Formal Theorem Proving: Generates and verifies Lean 4 proofs, achieving a market-leading 88.9% on MiniF2F-test.
✅ Advanced Mathematical Reasoning: Capable of solving high-school competition-level problems (e.g., AIME 24/25) with precise subgoal decomposition.
✅ Chain-of-Thought Reasoning: Combines DeepSeek-V3’s reasoning prowess with formal proofs for cohesive and verifiable outputs.
✅ Scalable Inference: Its MoE architecture, with 37B active parameters, ensures efficient computation for large-scale tasks.
✅ Multilingual Support: Processes mathematical notation and problem statements across multiple languages.
✅ Tool Integration: Fully supports the Lean 4 proof assistant for automated verification and proof construction.
✅ Flexible API Features: Offers structured outputs, reinforcement learning feedback, and OpenAI-compatible API endpoints.

🎯 Optimal Use Cases

DeepSeek Prover V2 is purpose-built for scenarios demanding rigorous mathematical and logical reasoning:

➡️ Mathematical Research: Ideal for formalizing proofs in diverse areas such as number theory, algebra, and geometry within Lean 4.
➡️ Educational Tools: An invaluable assistant for students tackling competition-level math problems (e.g., AIME, Putnam).
➡️ Automated Theorem Proving: Developing and verifying formal proofs for critical academic and industrial applications.
➡️ Scientific Analysis: Enhancing logical reasoning in theoretical physics, computer science, and other scientific domains.
➡️ AI-Driven Logic Systems: A core component for building sophisticated reasoning engines for automated proof assistants.

⚖️ Comparison with Other Leading Models

DeepSeek Prover V2 shines in formal theorem proving, often surpassing general-purpose models in its specialized mathematical tasks:

→ vs. Qwen3-235B-A22B: Matches AIME 2025 performance but significantly surpasses in formal proving (MiniF2F: 88.9% vs. ~80%), though with a slightly slower output speed (35 vs. 40.1 tokens/second).
→ vs. Gemini 2.5 Flash: Demonstrates far superior theorem proving capabilities (MiniF2F: 88.9% vs. ~60%) but lacks multimodality and has higher latency (1.2s vs. 0.8s).
→ vs. DeepSeek-R1: Exhibits stronger formal proving performance (MiniF2F: 88.9% vs. ~75%) but is less versatile for general reasoning tasks.
→ vs. Claude 3.7 Sonnet: Outperforms in neural theorem proving (PutnamBench: 49/658 vs. ~40/658), while offering lower costs ($0.00317 vs. ~$0.015 per 1K tokens).

⚠️ Limitations

⚠ Limited to text-based mathematical reasoning; it does not possess vision or multimodal capabilities.
⚠ Presents higher latency (1.2s TTFT), which might be a consideration for real-time applications.
⚠ Optimal utilization requires expertise in Lean 4.
⚠ Operates under the Qwen License, which restricts commercial use, making it primarily research-focused.

🔌 API Integration

DeepSeek Prover V2 facilitates seamless integration via its AI/ML API. Comprehensive documentation for developers is available here.

# Example: Basic API call snippet (for illustration)

 import openai  client = openai.OpenAI(     api_key="YOUR_API_KEY",     base_url="https://api.deepseek.com/v1" )  response = client.chat.completions.create(     model="deepseek-prover-v2",     messages=[         {"role": "user", "content": "Prove that for any natural number n, n + 0 = n in Lean 4."}     ] ) print(response.choices[0].message.content)

❓ Frequently Asked Questions (FAQs)

Q: What is DeepSeek Prover V2 primarily designed for?

A: DeepSeek Prover V2 is an open-source large language model specialized in formal theorem proving in Lean 4, excelling in mathematical reasoning and proof construction.

Q: How does DeepSeek Prover V2 achieve its high performance in theorem proving?

A: It leverages a 671-billion-parameter Mixture-of-Experts (MoE) architecture, a recursive theorem-proving pipeline built on DeepSeek-V3, and enhanced reasoning through cold-start data synthesis and reinforcement learning.

Q: What are the key advantages of using DeepSeek Prover V2 compared to other models?

A: Its strengths include state-of-the-art performance on MiniF2F-test (88.9%) and PutnamBench (49/658), precise subgoal decomposition, scalable inference, and competitive API pricing for its specialized capabilities.

Q: Can DeepSeek Prover V2 be used for commercial applications?

A: Currently, its Qwen License restricts commercial use, making it primarily suitable for research and academic purposes.

Q: Is DeepSeek Prover V2 capable of handling multimodal inputs?

A: No, DeepSeek Prover V2 is limited to text-based mathematical reasoning and does not support vision or other multimodal inputs.

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