Context Window

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GPT-5.3-Codex vs Meta Llama3 2 11b Instruct

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-5.3-Codex

Image inputTool calling

Context window

400K

400,000 tokens · ~300K words

Model page
Meta

Model

Meta Llama3 2 11b Instruct

Image inputTool calling

Context window

128K

128,000 tokens · ~96K 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-5.3-Codex400K
Meta Llama3 2 11b Instruct128K

GPT-5.3-Codex has about 3.1× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-5.3-Codex has 212% more context capacity (400K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-5.3-Codex. Its 400K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use GPT-5.3-Codex. Its 128K 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-5.3-CodexMeta Llama3 2 11b Instruct
Context window400,000 tokens (400K)128,000 tokens (128K)
Max output tokens128,000 tokens (128K)4,096 tokens (4K)
Speed tierBalancedFast
VisionYesYes
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateFeb 2026N/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-5.3-Codex inGPT-5.3-Codex outMeta Llama3 2 11b Instruct inMeta Llama3 2 11b Instruct out
Aws Bedrock——$0.350/M$0.350/M

Frequently asked questions

GPT-5.3-Codex has a larger context window: 400K tokens vs 128K. 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