Reasoning model benchmarks explained: GPQA, AIME, MATH, and coding scores
Reasoning models use chain-of-thought to solve complex problems. But which benchmarks actually measure their reasoning ability, and which are just marketing?
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What are reasoning models?
Reasoning models (like OpenAI's o3, DeepSeek's R1, and Qwen's Thinking variants) use chain-of-thought prompting to solve complex problems. Instead of answering immediately, they "think through" the problem step by step, which can significantly improve accuracy on difficult tasks.
However, not all benchmarks are created equal. Some measure genuine reasoning ability, while others test knowledge recall or pattern matching. Here's what you need to know.
Key reasoning benchmarks
GPQA Diamond
Graduate-level science questions
reasoningAIME 2024
American Invitational Mathematics Examination
mathMATH-500
Competition mathematics problems
mathLiveCodeBench
Real-time coding challenges
codingSWE-bench Verified
Real GitHub issue resolution
codingAider Polyglot
Multi-language code editing
codingReasoning model benchmark comparison
Here's how the top reasoning models compare across key benchmarks. Scores are accuracy percentages (higher is better).
| Model | GPQA | AIME | MATH | Coding |
|---|---|---|---|---|
| deepseek-r1 fireworks_ai | 71.5 | 79.8 | 97.3 | 65.9 |
| deepseek-r1-basic fireworks_ai | 71.5 | 79.8 | 97.3 | 65.9 |
| deepseek-r1-671b lambda_ai | 71.5 | 79.8 | 97.3 | 65.9 |
| us.deepseek.r1-v1:0 bedrock_converse | 71.5 | 79.8 | 97.3 | 65.9 |
| deepseek-r1-turbo novita | 71.5 | 79.8 | 97.3 | 65.9 |
| deepseek-r1-8b llamagate | 71.5 | 79.8 | 97.3 | 65.9 |
| deepseek-r1-7b-qwen llamagate | 71.5 | 79.8 | 97.3 | 65.9 |
| qwen3-next-80b-a3b-thinking dashscope | - | - | - | - |
How to interpret reasoning benchmarks
High scores mean different things
- GPQA Diamond: Genuine scientific reasoning at graduate level
- AIME: Mathematical problem-solving with multi-step reasoning
- MATH-500: Competition math requiring creative approaches
- LiveCodeBench: Real-world coding ability on fresh problems
- SWE-bench: Practical software engineering on actual GitHub issues
Common pitfalls
- Don't compare reasoning models to non-reasoning models on the same benchmarks
- Check if scores are from the same version/date (reasoning models improve rapidly)
- Look at multiple benchmarks - no single number tells the whole story
- Consider cost: reasoning tokens are expensive, so check price-per-correct-answer
When to use reasoning models
Reasoning models excel at:
- Complex mathematical proofs and calculations
- Multi-step coding problems and debugging
- Scientific analysis requiring deep understanding
- Strategic planning with many constraints
- Tasks where accuracy matters more than speed
They're less ideal for:
- Simple Q&A or factual lookups
- High-throughput applications where latency matters
- Tasks that don't benefit from step-by-step reasoning
- Budget-constrained applications (reasoning tokens cost more)
Compare reasoning models
Ready to compare reasoning models side by side? Use our tools:
Frequently asked questions
What makes a model a "reasoning" model?
Reasoning models use chain-of-thought prompting to solve problems step by step, rather than answering immediately. They typically have "o", "r1", or "thinking" in their names.
Are reasoning models always better?
No. Reasoning models excel at complex tasks but are slower, more expensive, and overkill for simple questions. Use them when accuracy on hard problems matters more than speed or cost.
How often do reasoning model benchmarks change?
Rapidly. Reasoning models are updated frequently, and benchmark scores can change significantly with each version. Always check the date of the benchmark results.