Context Window

Compare

GPT-5.1-Codex-Max vs Mistral Large 2411

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.

Openai

Model

GPT-5.1-Codex-Max

Image inputTool calling

Context window

400K

400,000 tokens · ~300K words

Model page
Mistral

Model

Mistral Large 2411

Tool calling

Context window

128K

128,000 tokens · ~96K 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.

GPT-5.1-Codex-Max400K
Mistral Large 2411128K

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

Token calculator →Memory savings →

GPT-5.1-Codex-Max has 212% more context capacity (400K vs 128K tokens). GPT-5.1-Codex-Max is 37% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-5.1-Codex-Max. Its 400K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use GPT-5.1-Codex-Max. Input tokens are 37% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGPT-5.1-Codex-MaxMistral Large 2411
Context window400,000 tokens (400K)128,000 tokens (128K)
Max output tokens128,000 tokens (128K)128,000 tokens (128K)
Speed tierBalancedDeep
VisionYesNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesYes
Batch APINoNo
Release dateDec 2025Nov 2024

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.

ProviderGPT-5.1-Codex-Max inGPT-5.1-Codex-Max outMistral Large 2411 inMistral Large 2411 out
Google Vertex——$2.00/M$6.00/M
Mistral——$2.00/M$6.00/M
Openrouter$1.25/M$10.00/M——

Frequently asked questions

GPT-5.1-Codex-Max 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.

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.

Calculate your savingsLearn about Mem0

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