Context Window

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Codellama 70b vs Gpt 35 Turbo 0125

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

Context window

16K

16,384 tokens · ~12K words

Model page
Openai

Model

Gpt 35 Turbo 0125

Tool calling

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 70b16K
Gpt 35 Turbo 012516K

Same context window size for both models.

Token calculator →Memory savings →

Codellama 70b and Gpt 35 Turbo 0125 have identical context windows (16K tokens). Gpt 35 Turbo 0125 is 28% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Gpt 35 Turbo 0125. Input tokens are 28% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Codellama 70b. 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 70bGpt 35 Turbo 0125
Context window16,384 tokens (16K)16,384 tokens (16K)
Max output tokens16,384 tokens (16K)4,096 tokens (4K)
Speed tierDeepBalanced
VisionNoNo
Function callingNoYes
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 70b inCodellama 70b outGpt 35 Turbo 0125 inGpt 35 Turbo 0125 out
Azure——$0.500/M$1.50/M
Perplexity$0.700/M$2.80/M——

Frequently asked questions

Gpt 35 Turbo 0125 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.

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.

Calculate your savingsLearn about Mem0

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