Reasoning Models

Reviewed by Proof · Updated

Definition

A reasoning model is a language model trained to work through a problem in intermediate steps before it gives its final answer.

Key takeaways

  • Reasoning models spend extra tokens "thinking" before they answer.
  • They tend to do better on math, logic, planning and hard coding tasks.
  • They're usually slower and use more output tokens per answer.
  • Some APIs return the thinking in a separate field, some hide it, and some mix it into the reply.
  • For simple tasks, a fast non-reasoning model is often the better choice.

How they differ

A standard chat model starts writing its answer right away. A reasoning model first produces a chain of intermediate steps, then the answer. The steps let it catch mistakes and break a big problem into smaller ones.

That extra work has costs:

  • Latency. The first word of the final answer can take much longer. See Latency.
  • Tokens. The reasoning usually counts as output tokens, so answers cost more. See Input vs. Output Tokens.
  • Settings. Some reasoning models ignore or limit Temperature and similar options.

Working with them

  • Leave a generous max_tokens. If thinking uses up the budget, the answer can be cut short.
  • Ask for the result you want, not a long list of steps. The model plans on its own.
  • Some OpenAI-compatible tools send a reasoning_effort setting. Whether a given model or provider honors it varies.
  • If a client shows a reasoning field, don't feed it back to users as the final answer.

On luv13

luv13 lists its models at https://api.luv13.ai/v1/models, but that list doesn't say which ones are reasoning models. See the luv13 model list, Listing Models and Request Parameters for what luv13 supports. For a prompting approach that works on any model, see Chain-of-Thought Prompting.

Example

A hard multi-step question with plenty of room for the answer:

curl https://api.luv13.ai/v1/chat/completions \
  -H "Authorization: Bearer $LUV13_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "luv13/glm-5.3-flash",
    "messages": [{"role": "user", "content": "A train leaves at 2:40 p.m. and the trip takes 3 hours 35 minutes. What time does it arrive?"}],
    "max_tokens": 2000
  }'