Quick answer
A reasoning model is an LLM that works through a problem step by step — using extra inference-time compute — before giving its final answer.
That deliberation raises accuracy on hard, multi-step tasks like math, science, and software engineering, at the cost of higher latency and more output tokens.
How reasoning models work
Instead of answering in a single forward pass, a reasoning model generates an internal chain of thought first, then summarizes a final response. The longer it is allowed to “think”, the better it tends to do on difficult problems — a pattern often called inference-time or test-time scaling.
Many current flagships expose this as a control: a reasoning effort level, a thinking budget, or a separate “thinking” variant. A growing number are hybrid, answering instantly for easy prompts and escalating to deliberation only when needed.
The trade-off
- Better at: competition math, graduate-level science, long-horizon coding and agentic tasks, anything with dependent steps.
- Costs more: higher latency and more billed output tokens, since the thinking itself is generated text.
- Overkill for: short factual lookups, classification, autocomplete, and high-volume low-stakes calls.
Benchmarks like GPQA Diamond and AIME are the usual evidence a model's reasoning is strong.
Reasoning-oriented models in the catalog
Recent models whose release framing or benchmarks emphasize reasoning:
| Model | Lab | GPQA | AIME | Context | Released |
|---|---|---|---|---|---|
| GPT-6.1 Terra | OpenAI | - | - | 1.1M | Oct 26, 2026 |
| Mercury 3 | Inception | - | - | 1M | Oct 19, 2026 |
| DeepSeek-V4.1-Flash-1017 | DeepSeek | - | - | 1.0M | Oct 17, 2026 |
| Nemotron 3.5 Bolt 74B-A9B | NVIDIA | - | - | 262K | Oct 15, 2026 |
| Nemotron 3.5 Super VL | NVIDIA | - | - | 1M | Oct 15, 2026 |
| Muse Spark Open | Meta | - | - | 1.0M | Oct 15, 2026 |
| LFM2.5-24B-A2B | Liquid | - | - | 131K | Oct 13, 2026 |
| Muse Wonder | Meta | - | - | 1.0M | Oct 12, 2026 |
Browse the live reasoning model catalog for the full, filterable list.
Frequently asked questions
What is a reasoning model?
A reasoning model is an LLM trained to spend additional inference-time compute — an internal chain of thought — before producing a final answer. This trades latency and token cost for higher accuracy on multi-step problems like math, science, and code.
How is it different from a normal chat model?
A standard chat model responds in roughly one pass, optimized for speed and fluency. A reasoning model deliberates first, often with a tunable 'effort' or 'thinking' level. Many recent models are hybrid: they can answer quickly or switch into a thinking mode for harder questions.
When should I not use a reasoning model?
For short, latency-sensitive, or high-volume tasks — autocomplete, classification, simple Q&A — the extra thinking adds cost and delay without improving the result. Reserve reasoning modes for problems where a wrong answer is expensive or the task has several dependent steps.
Where to go next
See the frontier model leaderboard, learn how benchmarks are scored, or read about open-weight access.