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Codellama 70b Instruct vs Gemma 3n 4B (free)

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 70b Instruct

Context window

4K

4,096 tokens · ~3K words

Model page
Google

Model

Gemma 3n 4B (free)

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.

Codellama 70b Instruct4K
Gemma 3n 4B (free)8K

Gemma 3n 4B (free) has about 2× the context window of the other in this pair.

Gemma 3n 4B (free) has 100% more context capacity (8K vs 4K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gemma 3n 4B (free). Its 8K context fits entire documents without chunking (vs 4K).

  • Long output (reports, code files)

    Use Codellama 70b Instruct. Its 4K 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 70b InstructGemma 3n 4B (free)
Context window4,096 tokens (4K)8,192 tokens (8K)
Max output tokens4,096 tokens (4K)2,048 tokens (2K)
Speed tierDeepBalanced
VisionNoNo
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/AMay 2025

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 70b Instruct inCodellama 70b Instruct outGemma 3n 4B (free) inGemma 3n 4B (free) out
Anyscale$1.00/M$1.00/M

Frequently asked questions

Gemma 3n 4B (free) has a larger context window: 8K 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.

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