Context Window

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Gpt 35 Turbo 1106 vs Llama V3p2 11b Vision

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.

Openai

Model

Gpt 35 Turbo 1106

Tool calling

Context window

16K

16,384 tokens · ~12K words

Model page
Meta

Model

Llama V3p2 11b Vision

Image input

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.

Gpt 35 Turbo 110616K
Llama V3p2 11b Vision16K

Same context window size for both models.

Token calculator →Memory savings →

Gpt 35 Turbo 1106 and Llama V3p2 11b Vision have identical context windows (16K tokens). Llama V3p2 11b Vision is 80% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Llama V3p2 11b Vision. Input tokens are 80% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Llama V3p2 11b Vision. 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.

SpecGpt 35 Turbo 1106Llama V3p2 11b Vision
Context window16,384 tokens (16K)16,384 tokens (16K)
Max output tokens4,096 tokens (4K)16,384 tokens (16K)
Speed tierBalancedFast
VisionNoYes
Function callingYesNo
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.

ProviderGpt 35 Turbo 1106 inGpt 35 Turbo 1106 outLlama V3p2 11b Vision inLlama V3p2 11b Vision out
Azure$1.00/M$2.00/M——
Fireworks——$0.200/M$0.200/M

Frequently asked questions

Llama V3p2 11b Vision 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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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.

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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