Context Window

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Mistral Mistral 7b Instruct vs Mistral Mixtral 8x7b Instruct

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 Mistral 7b Instruct

Context window

32K

32,000 tokens · ~24K words

Model page
Mistral

Model

Mistral Mixtral 8x7b Instruct

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.

Mistral Mistral 7b Instruct32K
Mistral Mixtral 8x7b Instruct32K

Same context window size for both models.

Token calculator →Memory savings →

Mistral Mistral 7b Instruct and Mistral Mixtral 8x7b Instruct have identical context windows (32K tokens). Mistral Mistral 7b Instruct is 66% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Mistral Mistral 7b Instruct. Input tokens are 66% 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 Mistral 7b InstructMistral Mixtral 8x7b Instruct
Context window32,000 tokens (32K)32,000 tokens (32K)
Max output tokens8,191 tokens (8K)8,191 tokens (8K)
Speed tierFastFast
VisionNoNo
Function callingNoNo
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.

ProviderMistral Mistral 7b Instruct inMistral Mistral 7b Instruct outMistral Mixtral 8x7b Instruct inMistral Mixtral 8x7b Instruct out
Aws Bedrock$0.200/M$0.260/M$0.590/M$0.910/M

Frequently asked questions

Mistral Mixtral 8x7b Instruct has a larger context window: 32K 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.

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