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Claude Opus 4 6 Default vs Claude Sonnet 4 6

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

Claude Opus 4 6 Default

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K words

Model page
Anthropic

Model

Claude Sonnet 4 6

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K 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.

Claude Opus 4 6 Default1M
Claude Sonnet 4 61M

Same context window size for both models.

Claude Opus 4 6 Default and Claude Sonnet 4 6 have identical context windows (1000K tokens). Claude Sonnet 4 6 is 40% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Claude Sonnet 4 6. Input tokens are 40% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Claude Opus 4 6 Default. 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.

SpecClaude Opus 4 6 DefaultClaude Sonnet 4 6
Context window1,000,000 tokens (1000K)1,000,000 tokens (1000K)
Max output tokens128,000 tokens (128K)64,000 tokens (64K)
Speed tierDeepBalanced
VisionYesYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APIYesYes
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.

ProviderClaude Opus 4 6 Default inClaude Opus 4 6 Default outClaude Sonnet 4 6 inClaude Sonnet 4 6 out
Anthropic$3.00/M$15.00/M
Aws Bedrock$3.00/M$15.00/M
Azure$3.00/M$15.00/M
Google Vertex$5.00/M$25.00/M$3.00/M$15.00/M
Openrouter$3.00/M$15.00/M

Frequently asked questions

Claude Sonnet 4 6 has a larger context window: 1000K tokens vs 1000K. 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.

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