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Command R7B (12-2024) vs Kimi Latest 8k

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.

Cohere

Model

Command R7B (12-2024)

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Moonshot

Model

Kimi Latest 8k

Image inputTool calling

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.

Command R7B (12-2024)128K
Kimi Latest 8k8K

Command R7B (12-2024) has about 15.6× the context window of the other in this pair.

Command R7B (12-2024) has 1462% more context capacity (128K vs 8K tokens). Command R7B (12-2024) is 25% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Command R7B (12-2024). Its 128K context fits entire documents without chunking (vs 8K).

  • RAG / high-volume retrieval

    Use Command R7B (12-2024). Input tokens are 25% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Kimi Latest 8k. 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.

SpecCommand R7B (12-2024)Kimi Latest 8k
Context window128,000 tokens (128K)8,192 tokens (8K)
Max output tokens4,096 tokens (4K)8,192 tokens (8K)
Speed tierFastBalanced
VisionNoYes
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoYes
Batch APINoNo
Release dateDec 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.

ProviderCommand R7B (12-2024) inCommand R7B (12-2024) outKimi Latest 8k inKimi Latest 8k out
Cohere$0.150/M$0.037/M
Moonshot$0.200/M$2.00/M

Frequently asked questions

Command R7B (12-2024) has a larger context window: 128K 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.
Get better results.

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.

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