Context Window

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Codellama 34b Instruct vs GPT-3.5 Turbo 16k

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
Openai

Model

GPT-3.5 Turbo 16k

Tool calling

Context window

16K

16,385 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
GPT-3.5 Turbo 16k16K

GPT-3.5 Turbo 16k has about 4× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-3.5 Turbo 16k has 300% more context capacity (16K vs 4K tokens). Codellama 34b Instruct is 66% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-3.5 Turbo 16k. Its 16K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Codellama 34b Instruct. Input tokens are 66% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecCodellama 34b InstructGPT-3.5 Turbo 16k
Context window4,096 tokens (4K)16,385 tokens (16K)
Max output tokens4,096 tokens (4K)4,096 tokens (4K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoYes
Release dateN/AAug 2023

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 outGPT-3.5 Turbo 16k inGPT-3.5 Turbo 16k out
Anyscale$1.00/M$1.00/M——
Openai——$3.00/M$4.00/M
Openrouter——$3.00/M$4.00/M

Frequently asked questions

GPT-3.5 Turbo 16k 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