Best AI Models for Reasoning
Frontier chain-of-thought models evaluated across AIME Olympiad math, GPQA Diamond science, and Humanity's Last Exam.
Top Frontier Reasoning Models
Ranked by verified competition math (AIME) and PhD-level science (GPQA) scores.
OpenAI o3-mini
Next-generation cost-efficient reasoning model optimized for STEM, competitive math, and coding.
200k tokens
Not Disclosed
$1.10 / $4.40 / MTok
OpenAI o1
Flagship deep reasoning model trained with reinforcement learning for frontier science, math, and coding.
200k tokens
Not Disclosed
$15.00 / $60.00 / MTok
Claude 3.7 Sonnet
Anthropic's first hybrid reasoning frontier model with dynamic thinking budget control and state-of-the-art coding capabilities.
200k tokens
Not Disclosed
$3.00 / $15.00 / MTok
DeepSeek-R1
Open-weights frontier reasoning model trained via large-scale reinforcement learning without supervised cold start.
64k tokens
671B (37B active)
$0.55 / $2.19 / MTok
Gemini 2.0 Pro (Experimental)
Google's flagship intelligence model engineered for complex coding, mathematical proofs, and 2M token context.
2M tokens
Not Disclosed
$0.00 / $0.00 / MTok
Reasoning FAQ
What defines a 'Reasoning AI Model'?
Reasoning models (like OpenAI o1, o3-mini, DeepSeek-R1, and Claude 3.7 Sonnet) use test-time compute to generate extended chain-of-thought tokens, verifying steps, correcting intermediate mistakes, and exploring multiple logical paths before outputting the final answer.
Is DeepSeek-R1 comparable to OpenAI o1 on math?
Yes. On official AIME 2024 evaluations, DeepSeek-R1 achieves 79.8% Pass@1 and 97.3% on MATH-500, matching OpenAI o1 (83.3%) while releasing open weights under the MIT license.
When should I use reasoning models instead of standard LLMs?
Reasoning models are ideal for complex multi-step problems: mathematical theorem proving, multi-file code debugging, biochemical modeling, and legal logic synthesis. For simple summarization or high-speed chatbots, standard models offer lower latency and cost.
Data Accuracy & Verification Notice
AI model specifications, pricing records, and benchmark metrics published on this platform are compiled directly from authoritative sources (official provider documentation, research papers, and verified evaluation harnesses). Benchmark results reflect specific test harnesses and prompting methodologies; scores are not directly comparable across differing evaluation setups.