Context Window

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Codestral Latest vs GPT-5.1-Codex-Mini

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 Latest

Context window

32K

32,000 tokens · ~24K words

Model page
Openai

Model

GPT-5.1-Codex-Mini

Image inputTool calling

Context window

400K

400,000 tokens · ~300K 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 Latest32K
GPT-5.1-Codex-Mini400K

GPT-5.1-Codex-Mini has about 12.5× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-5.1-Codex-Mini has 1150% more context capacity (400K vs 32K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-5.1-Codex-Mini. Its 400K context fits entire documents without chunking (vs 32K).

  • Long output (reports, code files)

    Use GPT-5.1-Codex-Mini. Its 100K max output lets you generate complete artifacts in one request.

Full specs

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

SpecCodestral LatestGPT-5.1-Codex-Mini
Context window32,000 tokens (32K)400,000 tokens (400K)
Max output tokens8,191 tokens (8K)100,000 tokens (100K)
Speed tierBalancedFast
VisionNoYes
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APINoNo
Release dateN/ANov 2025

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 Latest inCodestral Latest outGPT-5.1-Codex-Mini inGPT-5.1-Codex-Mini out
Google Vertex$0.200/M$0.600/M——
Mistral————

Frequently asked questions

GPT-5.1-Codex-Mini has a larger context window: 400K 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.

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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