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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}")
-
AI Playground
Test all API models in the sandbox environment before you integrate.
We provide more than 300 models to integrate into your app.
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.
💡 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.
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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