Best AI Models for Reasoning & Math in 2026
From GPQA Diamond to AIME competition problems — which AI models can actually reason? Live benchmark data reveals surprising results.
🧠 Key Findings
- • Extended thinking models dominate reasoning benchmarks — chain-of-thought is essential
- • DeepSeek R1 punches well above its price class in mathematical reasoning
- • GPQA Diamond (graduate-level science) is the best predictor of real-world reasoning ability
- • AIME scores have improved dramatically — top models now solve 60%+ of competition problems
- • For math-heavy applications, model choice matters more than for any other category
Reasoning and mathematical ability represent the frontier of AI capability. While most AI models can generate fluent text and functional code, genuinely solving novel math problems or reasoning through complex multi-step arguments remains challenging. The benchmarks in this analysis test exactly that: problems that require real understanding, not pattern matching.
We evaluate models on six reasoning benchmarks, each testing different aspects of intelligence: GPQA Diamond (graduate-level science requiring expert knowledge), HLE (hard logical reasoning), MATH 500 (competition mathematics), AIME (American Invitational Mathematics Examination), MMLU Pro (broad graduate-level knowledge), and the latest AIME 2025 problems that no model could have been trained on.
Reasoning Benchmark Rankings
Live data from Artificial Analysis. Higher is better for all benchmarks.
| Model | GPQA Diamond | HLE | MATH 500 | AIME | AIME 2025 | MMLU Pro | $/1M out |
|---|---|---|---|---|---|---|---|
| GPT-5.4 (xhigh) | 92.0% | 41.6% | — | — | — | — | $15.00 |
| Gemini 3 Pro Preview… | 90.8% | 37.2% | — | — | 95.7% | 89.8% | $12.00 |
| GPT-5.2 (xhigh) | 90.3% | 35.4% | — | — | 99.0% | 87.4% | $14.00 |
| Grok 4 | 87.7% | 23.9% | 99.0% | 94.3% | 92.7% | 86.6% | $15.00 |
| Claude Opus 4.6 (Non… | 84.0% | 18.6% | — | — | — | — | $25.00 |
| DeepSeek R1 0528 (Ma… | 81.3% | 14.9% | 98.3% | 89.3% | 76.0% | 84.9% | $5.40 |
| Claude Sonnet 4.6 (N… | 79.9% | 13.2% | — | — | — | — | $15.00 |
| DeepSeek V3.2 (Non-r… | 75.1% | 10.5% | — | — | 59.0% | 83.7% | $0.42 |
Understanding the Benchmarks
GPQA Diamond — The PhD-Level Test
GPQA (Graduate-level Google-Proof Question Answering) Diamond is one of the most challenging AI benchmarks. Questions are written by domain experts (PhDs) and are specifically designed to be "Google-proof" — you can't find the answer by searching the internet. They require genuine understanding and multi-step reasoning across physics, chemistry, biology, and other sciences.
GPQA Diamond scores above 60% indicate strong scientific reasoning ability. Human expert accuracy on these questions ranges from 65-85%, so models approaching or exceeding 70% are performing at near-expert level.
AIME — Math Competition Problems
The American Invitational Mathematics Examination (AIME) is a math competition for top high school students. Problems require creative mathematical thinking, not just computation. Until 2024, AI models struggled with AIME problems. In 2026, top models solve 60%+ of problems — a remarkable improvement.
We track both historical AIME problems and the latest AIME 2025 set. AIME 2025 scores are particularly meaningful because these problems were created after training cutoffs, making them a genuine test of mathematical reasoning rather than memorization.
HLE — Humanity's Last Exam
HLE (Humanity's Last Exam) is designed to be the hardest possible evaluation for AI systems. The questions span all domains of human knowledge and require deep reasoning. Current top models score well below 50%, making HLE the best benchmark for measuring the frontier of AI reasoning capability.
The Extended Thinking Revolution
The biggest breakthrough in AI reasoning has been extended thinking (also called chain-of-thought or "thinking" mode). Models like Claude Opus 4.6 and DeepSeek R1 can now spend additional compute time reasoning through problems before producing an answer — similar to how a human might think carefully before responding to a difficult question.
This matters enormously for reasoning tasks. A model in standard mode might score 50% on AIME, but the same model with extended thinking can score 70%+. The trade-off is latency and cost: extended thinking uses more tokens (the "thought" tokens) and takes longer. For time-sensitive applications, you might prefer a fast model without thinking. For accuracy-critical math or science, extended thinking is worth the wait.
When evaluating models for reasoning tasks, always check whether benchmark scores were obtained with or without extended thinking enabled. Our benchmark leaderboard shows both configurations where available.
Use Case Recommendations
📐 Math Tutoring & Problem Solving
Best: DeepSeek R1 (best math/$ ratio) or Claude Opus 4.6 (highest accuracy). Enable extended thinking for step-by-step solutions. Both models show their work clearly, making them ideal for educational contexts.
🔬 Scientific Research & Analysis
Best: Claude Opus 4.6 or GPT-5.4. For research that requires understanding complex scientific papers and making connections across fields, GPQA Diamond scores are the best predictor. Flagship models are worth the premium here.
📊 Data Analysis & Statistics
Best: Gemini 3 Pro (large context for big datasets) or Claude Sonnet 4.6 (fast + accurate). Statistical analysis benefits from both mathematical ability and code generation — models strong in both areas have an edge.
⚖️ Legal & Logical Reasoning
Best: GPT-5.4 or Claude Opus 4.6. Legal reasoning requires careful logic, attention to precedent, and nuanced interpretation — areas where flagship models significantly outperform mid-tier options.
Explore Full Reasoning Rankings
Interactive leaderboard with all reasoning benchmarks.