Context Window

Compare

Claude 3 7 Sonnet vs Claude 4 Opus

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 7 Sonnet

Context window

200K

200,000 tokens · ~150K words

Model page
Anthropic

Model

Claude 4 Opus

Tool 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.

Claude 3 7 Sonnet200K
Claude 4 Opus200K

Same context window size for both models.

Token calculator →Memory savings →

Claude 3 7 Sonnet and Claude 4 Opus have identical context windows (200K tokens). Claude 3 7 Sonnet is 81% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Claude 3 7 Sonnet. Input tokens are 81% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Claude 4 Opus. Its 200K 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 3 7 SonnetClaude 4 Opus
Context window200,000 tokens (200K)200,000 tokens (200K)
Max output tokens64,000 tokens (64K)200,000 tokens (200K)
Speed tierBalancedDeep
VisionNoNo
Function callingNoYes
Extended thinkingYesNo
Prompt cachingYesNo
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 7 Sonnet inClaude 3 7 Sonnet outClaude 4 Opus inClaude 4 Opus out
Deepinfra——$16.50/M$82.50/M
Gradient$3.00/M$15.00/M——
Openrouter$3.00/M$15.00/M——
Replicate$3.00/M$15.00/M——

Frequently asked questions

Claude 4 Opus 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.

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.

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