Context Window

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Codellama 34b Instruct vs Starcoder2 3b

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
Bigcode

Model

Starcoder2 3b

Context window

16K

16,384 tokens · ~12K 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
Starcoder2 3b16K

Starcoder2 3b has about 4× the context window of the other in this pair.

Token calculator →Memory savings →

Starcoder2 3b has 300% more context capacity (16K vs 4K tokens). Starcoder2 3b is 90% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Starcoder2 3b. Its 16K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Starcoder2 3b. Input tokens are 90% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Starcoder2 3b. Its 16K 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 InstructStarcoder2 3b
Context window4,096 tokens (4K)16,384 tokens (16K)
Max output tokens4,096 tokens (4K)16,384 tokens (16K)
Speed tierBalancedFast
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.

ProviderCodellama 34b Instruct inCodellama 34b Instruct outStarcoder2 3b inStarcoder2 3b out
Anyscale$1.00/M$1.00/M——
Fireworks——$0.100/M$0.100/M

Frequently asked questions

Starcoder2 3b has a larger context window: 16K 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