Context Window

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Codellama 70b Instruct vs Mixtral 8x22B 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

Codellama 70b Instruct

Context window

4K

4,096 tokens · ~3K words

Model page
Mistral

Model

Mixtral 8x22B Instruct

Tool calling

Context window

66K

65,536 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.

Codellama 70b Instruct4K
Mixtral 8x22B Instruct66K

Mixtral 8x22B Instruct has about 16× the context window of the other in this pair.

Token calculator →Memory savings →

Mixtral 8x22B Instruct has 1500% more context capacity (65K vs 4K tokens). Mixtral 8x22B Instruct is 35% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mixtral 8x22B Instruct. Its 65K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Mixtral 8x22B Instruct. Input tokens are 35% cheaper — critical when sending large retrieved contexts.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecCodellama 70b InstructMixtral 8x22B Instruct
Context window4,096 tokens (4K)65,536 tokens (65K)
Max output tokens4,096 tokens (4K)N/A
Speed tierDeepBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoYes
Batch APINoNo
Release dateN/AApr 2024

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.

ProviderCodellama 70b Instruct inCodellama 70b Instruct outMixtral 8x22B Instruct inMixtral 8x22B Instruct out
Anyscale$1.00/M$1.00/M——
Fireworks——$1.20/M$1.20/M
Openrouter——$0.650/M$0.650/M

Frequently asked questions

Mixtral 8x22B Instruct has a larger context window: 65K tokens vs 4K. 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