How do reasoning models stack up against standard LLMs? This benchmark compares 197 reasoning models against 149 standard models on composite score, pricing, and capabilities - helping you decide when chain-of-thought thinking is worth the trade-off.
Reasoning vs Standard - Head-to-Head
Reasoning Models
197
Models
92
Top Score
63
Avg Score
$12.56
Avg $/1M Out
Standard Models
149
Models
87
Top Score
51
Avg Score
$4.06
Avg $/1M Out
Reasoning models from 35 providers. Score difference: +12 points average for reasoning models.
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.
When Reasoning Helps
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.
Speed vs Accuracy
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.
Emerging Reasoning Models
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.
AI Reasoning Benchmark - Which LLM Thinks Best? | LM Market Cap