Context Window

Compare

Mistral Large vs Open Mixtral 8x22b

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

Tool calling

Context window

32K

32,000 tokens · ~24K words

Model page
Mistral

Model

Open Mixtral 8x22b

Tool calling

Context window

65K

65,336 tokens · ~49K 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 Large32K
Open Mixtral 8x22b65K

Open Mixtral 8x22b has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Open Mixtral 8x22b has 104% more context capacity (65K vs 32K tokens). Open Mixtral 8x22b is 33% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Open Mixtral 8x22b. Its 65K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Open Mixtral 8x22b. Input tokens are 33% 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 LargeOpen Mixtral 8x22b
Context window32,000 tokens (32K)65,336 tokens (65K)
Max output tokens8,191 tokens (8K)8,191 tokens (8K)
Speed tierDeepBalanced
VisionNoNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingYesNo
Batch APINoNo
Release dateFeb 2024N/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 inMistral Large outOpen Mixtral 8x22b inOpen Mixtral 8x22b out
Azure$4.00/M$12.00/M——
Ibm Watsonx$3.00/M$10.00/M——
Mistral——$2.00/M$6.00/M
Openrouter$8.00/M$24.00/M——
Snowflake————

Frequently asked questions

Open Mixtral 8x22b 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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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