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Devstral Medium vs KAT-Coder-Air V2.5

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Mistral

Model

Devstral Medium

Tool calling

Context window

131K

131,072 tokens · ~98K words

Model page
Kwaipilot

Model

KAT-Coder-Air V2.5

Tool calling

Context window

256K

256,000 tokens · ~192K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

Devstral Medium131K
KAT-Coder-Air V2.5256K

KAT-Coder-Air V2.5 has about 2× the context window of the other in this pair.

KAT-Coder-Air V2.5 has 95% more context capacity (256K vs 131K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use KAT-Coder-Air V2.5. Its 256K context fits entire documents without chunking (vs 131K).

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecDevstral MediumKAT-Coder-Air V2.5
Context window131,072 tokens (131K)256,000 tokens (256K)
Max output tokensN/A80,000 tokens (80K)
Speed tierBalancedBalanced
VisionNoNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingYesYes
Batch APINoNo
Release dateJul 2025Jul 2026

Frequently asked questions

KAT-Coder-Air V2.5 has a larger context window: 256K tokens vs 131K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model