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Claude 4 Sonnet vs Nova Pro 1.0

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 4 Sonnet

Tool calling

Context window

200K

200,000 tokens · ~150K words

Model page
Amazon

Model

Nova Pro 1.0

Image inputTool calling

Context window

300K

300,000 tokens · ~225K 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 4 Sonnet200K
Nova Pro 1.0300K

Nova Pro 1.0 has about 1.5× the context window of the other in this pair.

Nova Pro 1.0 has 50% more context capacity (300K vs 200K tokens). Nova Pro 1.0 is 73% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Nova Pro 1.0. Its 300K context fits entire documents without chunking (vs 200K).

  • RAG / high-volume retrieval

    Use Nova Pro 1.0. Input tokens are 73% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Claude 4 Sonnet. 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 4 SonnetNova Pro 1.0
Context window200,000 tokens (200K)300,000 tokens (300K)
Max output tokens200,000 tokens (200K)10,000 tokens (10K)
Speed tierBalancedBalanced
VisionNoYes
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APIYesNo
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.

ProviderClaude 4 Sonnet inClaude 4 Sonnet outNova Pro 1.0 inNova Pro 1.0 out
Amazon$0.800/M$3.20/M
Deepinfra$3.30/M$16.50/M
Replicate$3.00/M$15.00/M

Frequently asked questions

Nova Pro 1.0 has a larger context window: 300K 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.

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