Context Window

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Ministral 3b vs Mistral Mistral Large 3 675b

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

Ministral 3b

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Mistral

Model

Mistral Mistral Large 3 675b

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.

Ministral 3b128K
Mistral Mistral Large 3 675b128K

Same context window size for both models.

Token calculator →Memory savings →

Ministral 3b and Mistral Mistral Large 3 675b have identical context windows (128K tokens). Ministral 3b is 92% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Ministral 3b. Input tokens are 92% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Mistral Mistral Large 3 675b. 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.

SpecMinistral 3bMistral Mistral Large 3 675b
Context window128,000 tokens (128K)128,000 tokens (128K)
Max output tokens4,096 tokens (4K)8,192 tokens (8K)
Speed tierFastDeep
VisionNoNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoNo
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.

ProviderMinistral 3b inMinistral 3b outMistral Mistral Large 3 675b inMistral Mistral Large 3 675b out
Aws Bedrock——$0.500/M$1.50/M
Azure$0.040/M$0.040/M——

Frequently asked questions

Mistral Mistral Large 3 675b has a larger context window: 128K 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