Context Window

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Codellama 34b Instruct vs Phi 2 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
Microsoft

Model

Phi 2 3b

Context window

2K

2,048 tokens · ~2K 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
Phi 2 3b2K

Codellama 34b Instruct has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Codellama 34b Instruct has 100% more context capacity (4K vs 2K tokens). Phi 2 3b is 90% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Codellama 34b Instruct. Its 4K context fits entire documents without chunking (vs 2K).

  • RAG / high-volume retrieval

    Use Phi 2 3b. Input tokens are 90% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Codellama 34b 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 34b InstructPhi 2 3b
Context window4,096 tokens (4K)2,048 tokens (2K)
Max output tokens4,096 tokens (4K)2,048 tokens (2K)
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 outPhi 2 3b inPhi 2 3b out
Anyscale$1.00/M$1.00/M——
Fireworks——$0.100/M$0.100/M

Frequently asked questions

Codellama 34b Instruct has a larger context window: 4K tokens vs 2K. 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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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
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Current turn

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