Context Window

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Codellama 34b 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

Context window

16K

16,384 tokens · ~12K 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 34b16K
GPT-3.5 Turbo 16k16K

GPT-3.5 Turbo 16k has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-3.5 Turbo 16k has 0% more context capacity (16K vs 16K tokens). Codellama 34b is 88% 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 16K).

  • RAG / high-volume retrieval

    Use Codellama 34b. Input tokens are 88% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Codellama 34b. 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 34bGPT-3.5 Turbo 16k
Context window16,384 tokens (16K)16,385 tokens (16K)
Max output tokens16,384 tokens (16K)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 inCodellama 34b outGPT-3.5 Turbo 16k inGPT-3.5 Turbo 16k out
Openai——$3.00/M$4.00/M
Openrouter——$3.00/M$4.00/M
Perplexity$0.350/M$1.40/M——
Together Ai————

Frequently asked questions

GPT-3.5 Turbo 16k has a larger context window: 16K tokens vs 16K. 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
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model