Context Window

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Amazon Nova Micro 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.

Amazon

Model

Amazon Nova Micro

Tool calling

Context window

128K

128,000 tokens · ~96K 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.

Amazon Nova Micro128K
Nova Pro 1.0300K

Nova Pro 1.0 has about 2.3× the context window of the other in this pair.

Token calculator →Memory savings →

Nova Pro 1.0 has 134% more context capacity (300K vs 128K tokens). Amazon Nova Micro is 95% 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 128K).

  • RAG / high-volume retrieval

    Use Amazon Nova Micro. Input tokens are 95% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecAmazon Nova MicroNova Pro 1.0
Context window128,000 tokens (128K)300,000 tokens (300K)
Max output tokens10,000 tokens (10K)10,000 tokens (10K)
Speed tierBalancedBalanced
VisionNoYes
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
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.

ProviderAmazon Nova Micro inAmazon Nova Micro outNova Pro 1.0 inNova Pro 1.0 out
Amazon——$0.800/M$3.20/M
Aws Bedrock$0.035/M$0.140/M——

Frequently asked questions

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

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