Best LLM for Reasoning in 2026
Ranked by GPQA Diamond, AIME math score, and cost per token. Covers scientific analysis, advanced mathematics, multi-step planning, and hard algorithmic problems. Live pricing from 0+ models across 33+ providers.
Top picks at a glance
o3
OpenAI
Highest GPQA Diamond and AIME scores. Best for frontier math, scientific analysis, and the hardest multi-step problems.
o4-mini
OpenAI
90–95% of o3 quality at 9× lower cost. The default choice for production reasoning workloads.
Claude 3.7 Sonnet
Anthropic
Configurable thinking budget. Use standard mode for speed, extended thinking for hard problems — one model, two modes.
DeepSeek R1
DeepSeek
Open-weights reasoning model competitive with o1 on math and science. Self-hostable or available via inference providers.
Best model by use case
Advanced mathematics (AIME, MATH)
o3 or o4-mini (high effort)
Reasoning models dominate math benchmarks. o3 achieves near-human performance on AIME 2024. For most math tasks, o4-mini at high effort is sufficient at 9× lower cost.
Scientific analysis & research
o3 or Gemini 2.5 Pro (thinking)
GPQA Diamond scores above 80 indicate genuine scientific reasoning capability. Both models handle multi-step hypothesis evaluation and literature synthesis.
Complex multi-step planning
Claude 3.7 Sonnet (extended thinking)
Configurable thinking budget lets you tune depth vs. cost. Strong on structured planning, requirement analysis, and decision trees with many interdependencies.
Legal & financial document analysis
o4-mini or Claude 3.7 Sonnet
Reasoning models reduce hallucination on complex documents requiring cross-reference and inference. Extended context (128K–200K) handles long contracts and filings.
Hard algorithmic problems
o3 or o4-mini
Competitive programming (Codeforces, LeetCode Hard) and algorithm design benefit from extended reasoning. o3 achieves expert-level performance on competitive programming benchmarks.
High-volume reasoning at scale
o4-mini or DeepSeek R1
At scale, cost matters. o4-mini ($1.10/M input) and DeepSeek R1 ($0.55/M via inference providers) deliver strong reasoning at a fraction of o3's $10/M input cost.
Live reasoning model comparison — 0 models
| Model | GPQA | Input/1M |
|---|
How to choose the best reasoning LLM
Match the benchmark to your task. GPQA Diamond measures scientific reasoning — biology, chemistry, physics at graduate level. AIME measures mathematical reasoning. If your task is scientific analysis or research synthesis, GPQA is the right signal. If it is mathematical problem-solving, AIME and MATH benchmarks are more relevant. Neither benchmark predicts performance on general business tasks.
Reasoning models are not always better. On tasks where standard models already produce correct results — summarization, classification, simple Q&A — reasoning models add latency and cost without quality improvement. The extended thinking process is only valuable when the task genuinely requires multi-step inference that a standard model fails on.
Thinking budget controls the cost-quality trade-off. Models like Claude 3.7 Sonnet and Gemini 2.5 Pro let you configure how many tokens the model uses for internal reasoning. A low thinking budget (1,000–5,000 tokens) reduces cost and latency while still applying some chain-of-thought. A high budget (32,000+ tokens) maximizes quality for the hardest problems at significantly higher cost.
Always benchmark with your actual prompts. Benchmark scores are averages across standardized test sets. Your specific task may perform very differently. Run 50–100 representative examples through o4-mini before deciding whether o3's quality premium is justified for your use case.
Reasoning benchmarks explained
| Benchmark | Human expert | Best model (2026) |
|---|---|---|
| GPQA Diamond | ~65% | o3: ~88% |
| AIME 2024 | ~15% | o3: ~92% |
| MATH-500 | ~40% | o3: ~97% |
| MMLU | ~89% | o3: ~96% |
| ARC-Challenge | ~83% | Multiple: ~98% |