Katteb

Mistral Medium 3.5 Model to write, debug and explain code

Mistral Chat and reasoning models Upgrade to use mistralai/mistral-medium-3-5

Mistral Medium 3.5 is a dense 128B European model tuned for coding, reasoning and multimodal prompts. It is suited for engineers and analysts who need precise code, long-context documents, or reliable tool calls in agent setups.

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What it can do

  • Reads images and screenshots
  • Searches the live web and cites sources
  • Reads PDFs and documents
  • Thinks step by step on hard questions
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What people use it for

Produce and review code snippets

Generate complete functions, unit tests and small modules from a short spec, then ask for line-by-line explanations and targeted fixes. Review and refactor code with concrete diffs you can paste back into your editor.

Multi-file debugging with context

Feed project files or stack traces and get prioritized error causes and step-by-step reproduction instructions. The model holds long context so it can track cross-file state across the session.

Long-form technical summaries and documents

Summarize design notes, convert research into structured sections, or extract exact tables and JSON from long documents or mixed text and images. Ideal for reports that must preserve structure across many pages.

Why it is worth it

Questions people ask

When should I pick Mistral Medium 3.5 over a smaller model?

Choose it when you need dense reasoning, long-context views of code or documents, or multimodal inputs. For very small, cheap inference tasks a smaller model may be cheaper.

Can this model call tools, browse the web, or read files inside Katteb?

Yes. In Katteb the model can be used with web, files and vision capabilities to perform tool-enabled sessions and read uploaded files. Plan for iterative prompts and review outputs before running code or automation.

How is Katteb billed for using this model?

Katteb charges per message in credits. Mistral Medium 3.5 costs 18 credits per message on Katteb; check your account credits and session usage before large-scale runs.

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