How do reasoning models stack up against standard LLMs? This benchmark compares 132 reasoning models against 165 standard models on composite score, pricing, and capabilities — helping you decide when chain-of-thought thinking is worth the trade-off.
Reasoning models from 23 providers. Score difference: +14 points average for reasoning models.
| # | Model | Score |
|---|---|---|
| 1 | o1OpenAI | 65 |
| 2 | GPT-5.3 ChatOpenAI | 62 |
| 3 | GPT-5.2 ChatOpenAI | 62 |
| 4 | GPT-5.1 ChatOpenAI | 61 |
| 5 | GPT-4.1OpenAI | 59 |
| 6 | GPT-4.1 MiniOpenAI | 58 |
| 7 | GPT-4.1 NanoOpenAI | 58 |
| 8 | GPT-5 ChatOpenAI | 57 |
| 9 | Nova Premier 1.0Amazon | 56 |
| 10 | Gemma 3 27B (free)Google | 56 |
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.