Context Window

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Codestral 2 001 vs Command R7B (12-2024)

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 2 001

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Cohere

Model

Command R7B (12-2024)

Tool calling

Context window

128K

128,000 tokens · ~96K 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 2 001128K
Command R7B (12-2024)128K

Same context window size for both models.

Token calculator →Memory savings →

Codestral 2 001 and Command R7B (12-2024) have identical context windows (128K tokens). Command R7B (12-2024) is 50% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Command R7B (12-2024). Input tokens are 50% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Codestral 2 001. Its 128K 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 2 001Command R7B (12-2024)
Context window128,000 tokens (128K)128,000 tokens (128K)
Max output tokens128,000 tokens (128K)4,096 tokens (4K)
Speed tierBalancedFast
VisionNoNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/ADec 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.

ProviderCodestral 2 001 inCodestral 2 001 outCommand R7B (12-2024) inCommand R7B (12-2024) out
Cohere——$0.150/M$0.037/M
Google Vertex$0.300/M$0.900/M——

Frequently asked questions

Command R7B (12-2024) has a larger context window: 128K tokens vs 128K. 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