Context Window

Compare

Anthropic Claude vs GPT-5.3-Codex

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.

Anthropic

Model

Anthropic Claude

Context window

100K

100,000 tokens · ~75K words

Model page
Openai

Model

GPT-5.3-Codex

Image inputTool calling

Context window

400K

400,000 tokens · ~300K 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.

Anthropic Claude100K
GPT-5.3-Codex400K

GPT-5.3-Codex has about 4× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-5.3-Codex has 300% more context capacity (400K vs 100K 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 100K).

  • 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.

SpecAnthropic ClaudeGPT-5.3-Codex
Context window100,000 tokens (100K)400,000 tokens (400K)
Max output tokens8,191 tokens (8K)128,000 tokens (128K)
Speed tierBalancedBalanced
VisionNoYes
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APIYesNo
Release dateN/AFeb 2026

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.

ProviderAnthropic Claude inAnthropic Claude outGPT-5.3-Codex inGPT-5.3-Codex out
Aws Bedrock$8.00/M$24.00/M——

Frequently asked questions

GPT-5.3-Codex has a larger context window: 400K tokens vs 100K. 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