How do reasoning models stack up against standard LLMs? This benchmark compares 148 reasoning models against 156 standard models on composite score, pricing, and capabilities - helping you decide when chain-of-thought thinking is worth the trade-off.
Reasoning models from 26 providers. Score difference: +19 points average for reasoning models.
| # | Model | Score |
|---|---|---|
| 1 | GPT-5.3 ChatOpenAI | 85 |
| 2 | GPT-5.1 ChatOpenAI | 85 |
| 3 | GPT-5.2 ChatOpenAI | 83 |
| 4 | Qwen3 VL 32B InstructAlibaba | 81 |
| 5 | Qwen3 VL 8B InstructAlibaba | 81 |
| 6 | Qwen3 VL 30B A3B InstructAlibaba | 81 |
| 7 | GPT-4.1 NanoOpenAI | 81 |
| 8 | Qwen3 Coder PlusAlibaba | 78 |
| 9 | Qwen3 Coder FlashAlibaba | 78 |
| 10 | Nova Premier 1.0Amazon | 78 |
Chain-of-thought (CoT) prompting enables AI models to break down complex problems into intermediate steps before producing a final answer. Models like OpenAI o1 and DeepSeek R1 internalize this process, generating hidden reasoning traces that dramatically improve accuracy on math, logic, and multi-step tasks compared to direct answering.
Reasoning models shine on tasks that require multiple logical steps: mathematical proofs, complex coding challenges, scientific analysis, strategic planning, and any problem where standard models tend to hallucinate or skip steps. For simple Q&A or creative writing, standard models are often faster and equally effective.
Reasoning models consume more tokens and take longer to respond because they generate internal thinking traces. This trade-off is worthwhile when correctness matters more than latency - for example in code generation, financial analysis, or exam-style problems. For real-time chat, standard models remain the better choice.
The reasoning model landscape is evolving rapidly. OpenAI's o1 and o3 series led the way, followed by DeepSeek R1 bringing open-source reasoning. Google, Anthropic, and other providers have since introduced their own reasoning-capable models, driving down costs and expanding access to chain-of-thought capabilities.
AI reasoning benchmarks test a model's ability to solve complex problems requiring logical thinking, mathematical reasoning, scientific analysis, and multi-step problem solving - tasks that go beyond simple pattern matching.
DeepSeek R1, OpenAI o3, and Claude with extended thinking lead on reasoning benchmarks. These models use chain-of-thought processing to break down complex problems into steps, achieving significantly higher accuracy.
Key reasoning benchmarks include GPQA Diamond (graduate-level science), MATH-500 (mathematical reasoning), AIME (competition math), ARC Challenge (science questions), and GSM8K (grade-school math). Each tests different aspects of reasoning ability.