Context Window

Compare

Codestral Latest vs Meta Llama3 8b 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

Codestral Latest

Context window

32K

32,000 tokens · ~24K words

Model page
Meta

Model

Meta Llama3 8b Instruct

Context window

8K

8,192 tokens · ~6K 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.

Codestral Latest32K
Meta Llama3 8b Instruct8K

Codestral Latest has about 3.9× the context window of the other in this pair.

Token calculator →Memory savings →

Codestral Latest has 290% more context capacity (32K vs 8K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Codestral Latest. Its 32K context fits entire documents without chunking (vs 8K).

  • Long output (reports, code files)

    Use Meta Llama3 8b Instruct. Its 8K 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.

SpecCodestral LatestMeta Llama3 8b Instruct
Context window32,000 tokens (32K)8,192 tokens (8K)
Max output tokens8,191 tokens (8K)8,192 tokens (8K)
Speed tierBalancedFast
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.

ProviderCodestral Latest inCodestral Latest outMeta Llama3 8b Instruct inMeta Llama3 8b Instruct out
Aws Bedrock——$0.360/M$0.720/M
Google Vertex$0.200/M$0.600/M——
Mistral————

Frequently asked questions

Codestral Latest has a larger context window: 32K tokens vs 8K. 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.
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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.

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