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Claude Opus 4.7 vs Sonar Deep Research

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 4.7

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K words

Model page
Perplexity

Model

Sonar Deep Research

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.

Claude Opus 4.71M
Sonar Deep Research128K

Claude Opus 4.7 has about 7.8× the context window of the other in this pair.

Claude Opus 4.7 has 681% more context capacity (1000K vs 128K tokens). Sonar Deep Research is 60% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Claude Opus 4.7. Its 1000K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Sonar Deep Research. Input tokens are 60% 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 Opus 4.7Sonar Deep Research
Context window1,000,000 tokens (1000K)128,000 tokens (128K)
Max output tokens128,000 tokens (128K)N/A
Speed tierDeepBalanced
VisionYesNo
Function callingYesNo
Extended thinkingYesYes
Prompt cachingYesNo
Batch APIYesNo
Release dateApr 2026Mar 2025

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 4.7 inClaude Opus 4.7 outSonar Deep Research inSonar Deep Research out
Anthropic$5.00/M$25.00/M
Aws Bedrock$5.00/M$25.00/M
Azure$5.00/M$25.00/M
Google Vertex$5.00/M$25.00/M
Openrouter$5.00/M$25.00/M
Perplexity$2.00/M$8.00/M

Frequently asked questions

Claude Opus 4.7 has a larger context window: 1000K 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.

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