Context Window

Compare

Codestral 2508 vs Mistral Large Latest

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

Codestral 2508

Tool calling

Context window

256K

256,000 tokens · ~192K words

Model page
Mistral

Model

Mistral Large Latest

Tool calling

Context window

32K

32,000 tokens · ~24K 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.

Codestral 2508256K
Mistral Large Latest32K

Codestral 2508 has about 8× the context window of the other in this pair.

Token calculator →Memory savings →

Codestral 2508 has 700% more context capacity (256K vs 32K tokens). Codestral 2508 is 40% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Codestral 2508. Its 256K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Codestral 2508. Input tokens are 40% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecCodestral 2508Mistral Large Latest
Context window256,000 tokens (256K)32,000 tokens (32K)
Max output tokens256,000 tokens (256K)N/A
Speed tierBalancedDeep
VisionNoNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingYesNo
Batch APINoNo
Release dateAug 2025N/A

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderCodestral 2508 inCodestral 2508 outMistral Large Latest inMistral Large Latest out
Azure——$8.00/M$24.00/M
Google Vertex——$2.00/M$6.00/M
Mistral$0.300/M$0.900/M$0.500/M$1.50/M

Frequently asked questions

Codestral 2508 has a larger context window: 256K tokens vs 32K. 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.
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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.

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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