Context Window

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Devstral Small 2507 vs Ministral 3b

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
Mistral

Model

Ministral 3b

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.

Devstral Small 2507128K
Ministral 3b128K

Same context window size for both models.

Token calculator →Memory savings →

Devstral Small 2507 and Ministral 3b have identical context windows (128K tokens). Ministral 3b is 60% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Ministral 3b. Input tokens are 60% cheaper — critical when sending large retrieved contexts.

  • 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 2507Ministral 3b
Context window128,000 tokens (128K)128,000 tokens (128K)
Max output tokens128,000 tokens (128K)4,096 tokens (4K)
Speed tierBalancedFast
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.

ProviderDevstral Small 2507 inDevstral Small 2507 outMinistral 3b inMinistral 3b out
Azure——$0.040/M$0.040/M
Mistral$0.100/M$0.300/M——

Frequently asked questions

Ministral 3b 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