What it is: What is a Reasoning Model? — AI Glossary — everything you need to know
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A reasoning model is an AI that spends extended time “thinking” through a problem step by step before giving a final answer — trading latency for dramatically improved accuracy on complex math, logic, coding, and multi-step tasks. OpenAI’s o1 (released September 2024) was the first widely deployed reasoning model, followed by o3, DeepSeek-R1, and Claude’s extended thinking mode. These models represent a new category beyond standard LLMs, capable of solving problems that previously required human expert reasoning.
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What Makes a Reasoning Model Different
Standard LLMs produce tokens at a relatively fixed rate — they don’t “think harder” for difficult questions. A reasoning model uses test-time compute — spending more computation on harder problems, similar to how a human expert takes longer on difficult problems.
The core mechanism is extended chain-of-thought reasoning, trained via reinforcement learning. The model generates a long internal “thinking” trace before its final answer — exploring multiple approaches, catching its own errors, and revising its reasoning. This thinking often runs for thousands of tokens before the model produces its response.
Key differences from standard models:
- Slower to respond: 10-60+ seconds for hard problems (vs. 2-5 seconds for standard LLMs)
- More expensive per query: Reasoning tokens cost extra — o1 was initially 6x the price of GPT-4o
- Dramatically more accurate on hard tasks: o1 scored 83% on competition math vs. GPT-4’s 13%
- Better at following constraints: Extended reasoning allows checking whether outputs satisfy all requirements
Major Reasoning Models (2025)
The reasoning model landscape has expanded rapidly:
- OpenAI o1 / o3 / o4-mini: OpenAI’s flagship reasoning models. o3 represents a major capability jump; o4-mini offers reasoning at lower cost.
- DeepSeek-R1: Open-source reasoning model from China that matches o1 on benchmarks at a fraction of the API cost. Available for self-hosting.
- Claude Extended Thinking: Anthropic’s reasoning mode for Claude 3.5 Sonnet and later models — configurable “thinking budget” in tokens.
- Google Gemini 2.0 Flash Thinking: Fast reasoning variant with visible thinking traces.
- Qwen QwQ: Open-source reasoning model from Alibaba’s Qwen team, strong on math.
When to Use a Reasoning Model
Reasoning models are not always better — they’re overkill for simple tasks and expensive compared to standard models. Use them when:
- Solving math problems, proofs, or quantitative analysis
- Complex multi-step coding tasks (debugging hard issues, algorithm design)
- Logic puzzles, constraint satisfaction, or planning problems
- Legal or financial analysis requiring careful step-by-step reasoning
- Scientific problems requiring hypothesis testing across multiple approaches
For writing, summarization, translation, or general conversation, standard models like GPT-4o or Claude 3.5 Sonnet are faster and cheaper with comparable quality. The trend is toward routing: sending hard reasoning tasks to reasoning models and routine tasks to standard models. Chain-of-thought prompting on standard models is a lighter-weight alternative that captures some reasoning benefit without the full cost.
Key Takeaways
- Reasoning models use extended chain-of-thought thinking (trained via RL) to solve hard problems more accurately.
- They trade latency and cost for dramatically improved accuracy on math, logic, and complex coding.
- Major models: OpenAI o1/o3, DeepSeek-R1 (open-source), Claude Extended Thinking, Gemini Thinking.
- Use reasoning models for hard analytical tasks; use standard models for routine tasks to manage cost.
- Test-time compute scaling (more thinking = better answers) is a new axis of capability alongside model size.
Frequently Asked Questions
Is a reasoning model just doing chain-of-thought prompting?
It’s related but more fundamental. Reasoning models are specifically trained (via reinforcement learning on outcome correctness) to generate extended reasoning traces. Standard models doing chain-of-thought prompting benefit from similar effects, but reasoning models are optimized for it at a much deeper level — they’ve learned when and how to reason from scratch versus following a prompted pattern.
Why is DeepSeek-R1 significant?
DeepSeek-R1 matched o1’s performance on key benchmarks while being open-source (you can download and run the weights), and offered API access at ~3% of OpenAI’s o1 pricing. It demonstrated that reasoning model capabilities weren’t proprietary to OpenAI and democratized access significantly.
What are thinking tokens and do they cost money?
Thinking tokens are the internal reasoning trace the model generates before its final response. With OpenAI and Anthropic APIs, these are billed at the same or similar rate as regular output tokens — so a harder problem that requires 2,000 thinking tokens costs more than a simple question with 200 thinking tokens.
Can reasoning models be wrong even with extended thinking?
Yes. Reasoning models are better, not perfect. They can still make calculation errors, have incorrect premises, or confidently arrive at wrong conclusions through plausible-seeming reasoning chains. For high-stakes applications, always verify reasoning model outputs.
Is extended thinking visible in Claude?
When Claude Extended Thinking is enabled, the API returns a “thinking” block with the model’s internal reasoning visible to developers (though it can be hidden from end users). This transparency is useful for debugging and understanding how the model arrived at its answer.
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This article draws on official documentation, product pages, and industry reporting. Specific sources are linked inline throughout the text.
Last reviewed: April 2026
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