Context Window

Compare

Devstral Small 2507 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

Devstral Small 2507

Tool calling

Context window

128K

128,000 tokens · ~96K 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.

Devstral Small 2507128K
GPT-5.1-Codex-Mini400K

GPT-5.1-Codex-Mini has about 3.1× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-5.1-Codex-Mini has 212% more context capacity (400K vs 128K 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 128K).

  • Long output (reports, code files)

    Use Devstral Small 2507. Its 128K 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.

SpecDevstral Small 2507GPT-5.1-Codex-Mini
Context window128,000 tokens (128K)400,000 tokens (400K)
Max output tokens128,000 tokens (128K)100,000 tokens (100K)
Speed tierBalancedFast
VisionNoYes
Function callingYesYes
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.

ProviderDevstral Small 2507 inDevstral Small 2507 outGPT-5.1-Codex-Mini inGPT-5.1-Codex-Mini out
Mistral$0.100/M$0.300/M——

Frequently asked questions

GPT-5.1-Codex-Mini has a larger context window: 400K tokens vs 128K. 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