Context Window

Compare

Claude 3 Opus 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 3 Opus

Context window

200K

200,000 tokens · ~150K 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 3 Opus200K
Claude Sonnet 4 61M

Claude Sonnet 4 6 has about 5× the context window of the other in this pair.

Token calculator →Memory savings →

Claude Sonnet 4 6 has 400% more context capacity (1000K vs 200K tokens). Claude Sonnet 4 6 is 80% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Claude Sonnet 4 6. Its 1000K context fits entire documents without chunking (vs 200K).

  • RAG / high-volume retrieval

    Use Claude Sonnet 4 6. Input tokens are 80% cheaper — critical when sending large retrieved contexts.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecClaude 3 OpusClaude Sonnet 4 6
Context window200,000 tokens (200K)1,000,000 tokens (1000K)
Max output tokensN/A64,000 tokens (64K)
Speed tierDeepBalanced
VisionNoYes
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
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 3 Opus inClaude 3 Opus 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$15.00/M$75.00/M$3.00/M$15.00/M
Gradient$15.00/M$75.00/M——
Openrouter——$3.00/M$15.00/M

Frequently asked questions

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