Context Window

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Codellama 34b Instruct vs MiMo-V2.5

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 34b Instruct

Context window

4K

4,096 tokens · ~3K words

Model page
Xiaomi

Model

MiMo-V2.5

Image inputTool calling

Context window

1.0M

1,048,576 tokens · ~786K 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 34b Instruct4K
MiMo-V2.51.0M

MiMo-V2.5 has about 256× the context window of the other in this pair.

Token calculator →Memory savings →

MiMo-V2.5 has 25500% more context capacity (1048K vs 4K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use MiMo-V2.5. Its 1048K context fits entire documents without chunking (vs 4K).

  • Long output (reports, code files)

    Use MiMo-V2.5. Its 131K 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.

SpecCodellama 34b InstructMiMo-V2.5
Context window4,096 tokens (4K)1,048,576 tokens (1048K)
Max output tokens4,096 tokens (4K)131,072 tokens (131K)
Speed tierBalancedBalanced
VisionNoYes
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APINoNo
Release dateN/AApr 2026

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 34b Instruct inCodellama 34b Instruct outMiMo-V2.5 inMiMo-V2.5 out
Anyscale$1.00/M$1.00/M——

Frequently asked questions

MiMo-V2.5 has a larger context window: 1048K 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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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