Introduction to AI Reasoning at Mistral

Course redesigned on November 2, 2026. This course was originally built around the Magistral models, which were fully retired on July 31, 2026. At Mistral, reasoning is now handled via the reasoning_effort parameter available on general-purpose models: Mistral Small 4, Mistral Large 4, and Mistral Medium 3.5 (deprecated, retirement scheduled for April 30, 2027). All twelve lessons have been rewritten based on the official documentation: Reasoning · Models.

Why reasoning changes the game

When you ask a complex question to a standard language model, it generates its response token by token without a genuine reflection phase. For rephrasing or factual queries, the result is often correct. However, when faced with multi-step problems or demanding logical reasoning, performance degrades significantly: the model commits to a direction from the first few words and has no room to correct itself.

Reasoning changes this dynamic. Mistral’s documentation presents it as the next evolution of chain of thought: the model is trained to freely generate a chain of reflection before delivering its final answer. It explores the problem more deeply and, by spending more compute at inference time, improves its solution. This is known as Test Time Computation: investing compute during inference, not just during training.

The cost and benefit of reflection

The principle is simple: the more a model thinks before answering, the better its response on complex tasks. Concretely, it generates thinking traces: hypotheses, intermediate calculations, verifications, and backtracking. These extra tokens cost time and budget. In an example from the official documentation, a small age riddle posed to Mistral Small 4 with high reasoning effort consumes 77 input tokens and 1,114 output tokens for a final answer of just a few lines: the bulk of the cost is the thinking itself.

Reasoning excels in complex use cases, starting with mathematics and coding, but it applies to many other situations: analysis, planning, and decision-making.

Consider a classic problem to measure the difference. A father has three children; the product of their ages is 36, the sum of their ages is the neighbor’s house number, and the oldest plays the piano. What are their ages? Without reasoning, a model guesses and rarely gets it right. With reasoning, it enumerates the decompositions of 36 into three factors, calculates the sum of each, notices that two of them share the same sum (1, 6, 6 and 2, 2, 9 both equal 13: this is the ambiguity implied by the prompt), and then uses the clue about the “oldest” child to resolve it: 2, 2, and 9 years old.

The structure of a response with reasoning

When reasoning is active, the response is no longer a simple string. The message.content field becomes a list of chunks: first a thinking chunk of type thinking, which itself contains a list of text chunks; then a chunk of type text, which holds the final answer. Here is the simplified form returned by the API:

{
  "role": "assistant",
  "content": [
    {
      "type": "thinking",
      "thinking": [
        {"type": "text", "text": "... thinking traces ..."}
      ]
    },
    {"type": "text", "text": "... final answer ..."}
  ]
}

This separation is valuable: it lets you show or hide the thinking process depending on your application. An educational tutor might display the traces; a consumer-facing assistant would show only the conclusion. Lesson 3 details how to read these chunks, including in streaming mode.

Reasoning at Mistral in November 2026

Mistral initially offered a dedicated range, Magistral (Small and Medium, versions 25.06, 25.07, and 25.09), where reasoning was always active. This range is now fully retired: reasoning has become a capability of general-purpose models, configured per request with the reasoning_effort parameter.

Mistral Small 4 is described in its official sheet as a hybrid model that combines instruction, reasoning, and code in a single model. It is the natural starting point for this course. Mistral Large 4 offers a three-level scale and a one-million-token context window; it remains in Public Preview, which calls for caution in production. Lesson 5 compares these models in detail.

When to use reasoning

Reasoning isn’t necessary everywhere, and enabling it systematically is like paying an insurance premium for risk-free trips. It adds real value for mathematics, logic, debugging, comparative analysis, planning, and multi-step problems. It is superfluous for simple translation, rephrasing, direct factual questions, or short creative writing.

The whole challenge lies in balancing quality against token consumption. The course proceeds in three stages: configuring and reading reasoning (Lessons 1 to 4), choosing the model, prompting it effectively, and migrating from Magistral (Lessons 5 to 8), then applying it to mathematics, code, and decision-making (Lessons 9 to 12).

Key takeaways

• Test Time Computation invests compute at inference to improve complex answers
• With reasoning, message.content becomes a list: a thinking chunk (the reflection) followed by a text chunk (the answer)
• The Magistral range was retired on July 31, 2026: reasoning is now configured with reasoning_effort
• Mistral Small 4 (mistral-small-2603) is the starting point; Mistral Large 4 (mistral-large-4-0) targets the most demanding cases
• Reasoning is justified for math, code, and decision-making, not for simple tasks