Context Window

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Gov Anthropic Claude Sonnet 4 5 20250929 vs o1 (batch)

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

Gov Anthropic Claude Sonnet 4 5 20250929

Image inputTool calling

Context window

200K

200,000 tokens · ~150K words

Model page
Openai

Model

o1 (batch)

Image inputTool calling

Context window

200K

200,000 tokens · ~150K 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.

Gov Anthropic Claude Sonnet 4 5 20250929200K
o1 (batch)200K

Same context window size for both models.

Token calculator →Memory savings →

Gov Anthropic Claude Sonnet 4 5 20250929 and o1 (batch) have identical context windows (200K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long output (reports, code files)

    Use o1 (batch). Its 100K 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.

SpecGov Anthropic Claude Sonnet 4 5 20250929o1 (batch)
Context window200,000 tokens (200K)200,000 tokens (200K)
Max output tokens64,000 tokens (64K)100,000 tokens (100K)
Speed tierBalancedDeep
VisionYesYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APIYesYes
Release dateN/ADec 2024

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.

ProviderGov Anthropic Claude Sonnet 4 5 20250929 inGov Anthropic Claude Sonnet 4 5 20250929 outo1 (batch) ino1 (batch) out
Aws Bedrock$3.60/M$18.00/M——

Frequently asked questions

o1 (batch) has a larger context window: 200K tokens vs 200K. 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.

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