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Codegemma 2b vs Gemma2 9b It

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.

Google

Model

Codegemma 2b

Context window

8K

8,192 tokens · ~6K words

Model page
Google

Model

Gemma2 9b It

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.

Codegemma 2b8K
Gemma2 9b It8K

Same context window size for both models.

Codegemma 2b and Gemma2 9b It have identical context windows (8K tokens). Codegemma 2b is 50% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Codegemma 2b. Input tokens are 50% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecCodegemma 2bGemma2 9b It
Context window8,192 tokens (8K)8,192 tokens (8K)
Max output tokens8,192 tokens (8K)8,192 tokens (8K)
Speed tierBalancedBalanced
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.

ProviderCodegemma 2b inCodegemma 2b outGemma2 9b It inGemma2 9b It out
Fireworks$0.100/M$0.100/M$0.200/M$0.200/M

Frequently asked questions

Gemma2 9b It has a larger context window: 8K 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