Loading lesson…
Codestral 25 is Mistral's dedicated coding model. Small, fast, and cheap enough to run as an inline autocomplete.
Codestral 25 supports fill-in-the-middle (FIM) out of the box and is priced to run on every keystroke of a paying developer. That is a different class of tool than a chat assistant.
| Feature | Codestral 25 | Claude Sonnet 4.6 |
|---|---|---|
| FIM support | Native | Workaround |
| Latency per completion | <500ms | 1-2s |
| Cost per M tokens | Very low | Moderate |
| Best fit | Inline completion | Chat + agent |
resp = client.fim.complete( model="codestral-latest", prompt="def parse_csv(path):\n ", suffix="\n return rows", )FIM endpoint takes a prefix and suffix; the model fills the gap.Codestral 25 excels at completions; it underperforms chat-tier models on multi-step refactors and natural-language explanations. Use it for inline suggestions and route chat to Sonnet or GPT-5.
8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-modelx-codestral-25-builders
What is the main idea of "Mistral Codestral 25 — code-specific model"?
Which concept is most central to "Mistral Codestral 25 — code-specific model"?
Which use of AI fits this topic best?
What should a careful learner remember about "Self-host is realistic"?
You want to use AI after this lesson. What is the safest next step?
How should AI output about Codestral 25 be treated?
Name one way to verify an AI answer about Codestral 25.
Which action would help you apply "Mistral Codestral 25 — code-specific model" responsibly?