DeepSeek-R1-0528: Advanced Reasoning Language Model

DeepSeek-R1-0528 is an advanced reasoning model developed by DeepSeek AI that significantly improves upon its predecessor through enhanced computational depth and inference capabilities. Released under the MIT license, it represents a major advancement in open-source reasoning AI.

Key Capabilities

• Deep Reasoning - Enhanced computational depth for complex problem-solving, using extended token chains to explore multiple solution paths
• Chain-of-Thought Processing - Extended thinking depth for complex mathematical and logical problems
• Function Calling - Enhanced support for tool use and API integration
• Reduced Hallucination - Lower error rates compared to previous versions through reinforcement learning optimization
• Commercial License - MIT license permits commercial use and modification

Benchmark Performance

Mathematics:

• AIME 2024: 91.4% accuracy
• AIME 2025: 87.5% accuracy
• HMMT 2025: 79.4% accuracy

Programming:

• Codeforces Division 1: 1930 rating
• LiveCodeBench: 73.3% accuracy

General Knowledge:

• MMLU-Pro: 85.0% (Exact Match)
• GPQA-Diamond: 81.0% accuracy

Use Cases

• Complex mathematical problem solving
• Advanced code generation and debugging
• Research and technical analysis
• Scientific reasoning and hypothesis testing
• Legal document analysis
• Financial modeling and forecasting
• Educational tutoring for advanced subjects
• Logical reasoning and proof generation

Training Approach

DeepSeek-R1-0528 employs reinforcement learning to incentivize reasoning capability, with optimization mechanisms during post-training that increase computational depth. This approach allows the model to explore multiple solution paths before generating final answers, leading to significant improvements in accuracy on challenging reasoning tasks.

The model demonstrates a 25% improvement in AIME 2025 performance compared to its predecessor, achieved through increased reasoning depth averaging 23K tokens per question versus 12K in the earlier version.

Architecture

The model uses a transformer-based architecture enhanced with reinforcement learning techniques specifically designed to improve reasoning capabilities. The training process optimizes for extended chain-of-thought processing, enabling the model to break down complex problems into manageable steps.

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