Context Window

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Mistral Large 4 vs Moonshot V1 32k Vision Preview

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

Mistral Large 4

Image inputTool calling

Context window

524K

524,288 tokens · ~393K words

Model page
Moonshot

Model

Moonshot V1 32k Vision Preview

Image inputTool calling

Context window

33K

32,768 tokens · ~25K 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.

Mistral Large 4524K
Moonshot V1 32k Vision Preview33K

Mistral Large 4 has about 16× the context window of the other in this pair.

Token calculator →Memory savings →

Mistral Large 4 has 1500% more context capacity (524K vs 32K tokens). Mistral Large 4 is 31% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mistral Large 4. Its 524K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Mistral Large 4. Input tokens are 31% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecMistral Large 4Moonshot V1 32k Vision Preview
Context window524,288 tokens (524K)32,768 tokens (32K)
Max output tokensN/A32,768 tokens (32K)
Speed tierDeepBalanced
VisionYesYes
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateN/AN/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.

ProviderMistral Large 4 inMistral Large 4 outMoonshot V1 32k Vision Preview inMoonshot V1 32k Vision Preview out
Mistral$0.680/M$2.09/M——
Moonshot——$1.00/M$3.00/M

Frequently asked questions

Mistral Large 4 has a larger context window: 524K 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