Context Window

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MiniMax M2-her vs Mistral Small

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.

Minimax

Model

MiniMax M2-her

Context window

66K

65,536 tokens · ~49K words

Model page
Mistral

Model

Mistral Small

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.

MiniMax M2-her66K
Mistral Small32K

MiniMax M2-her has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

MiniMax M2-her has 104% more context capacity (65K vs 32K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use MiniMax M2-her. Its 65K context fits entire documents without chunking (vs 32K).

  • Long output (reports, code files)

    Use Mistral Small. Its 8K 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.

SpecMiniMax M2-herMistral Small
Context window65,536 tokens (65K)32,000 tokens (32K)
Max output tokens2,048 tokens (2K)8,191 tokens (8K)
Speed tierFastBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingYesNo
Batch APINoNo
Release dateJan 2026N/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.

ProviderMiniMax M2-her inMiniMax M2-her outMistral Small inMistral Small out
Azure——$1.00/M$3.00/M
Mistral——$0.100/M$0.300/M

Frequently asked questions

MiniMax M2-her has a larger context window: 65K 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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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