Context Window

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Llama4 Maverick 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.

Meta

Model

Llama4 Maverick

Tool calling

Context window

128K

128,000 tokens · ~96K 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.

Llama4 Maverick128K
Mistral Mixtral 8x7b Instruct32K

Llama4 Maverick has about 4× the context window of the other in this pair.

Token calculator →Memory savings →

Llama4 Maverick has 300% more context capacity (128K vs 32K tokens). Llama4 Maverick is 59% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama4 Maverick. Its 128K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Llama4 Maverick. Input tokens are 59% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Llama4 Maverick. Its 16K 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.

SpecLlama4 MaverickMistral Mixtral 8x7b Instruct
Context window128,000 tokens (128K)32,000 tokens (32K)
Max output tokens16,384 tokens (16K)8,191 tokens (8K)
Speed tierBalancedFast
VisionNoNo
Function callingYesNo
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.

ProviderLlama4 Maverick inLlama4 Maverick outMistral Mixtral 8x7b Instruct inMistral Mixtral 8x7b Instruct out
Aws Bedrock——$0.590/M$0.910/M
Snowflake$0.240/M$0.970/M——

Frequently asked questions

Llama4 Maverick has a larger context window: 128K 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