Context Window

Compare

Amazon Nova Micro vs Qwen3 Max Thinking

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
Alibaba

Model

Qwen3 Max Thinking

Tool calling

Context window

262K

262,144 tokens · ~197K 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
Qwen3 Max Thinking262K

Qwen3 Max Thinking has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3 Max Thinking has 104% more context capacity (262K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 Max Thinking. Its 262K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use Qwen3 Max Thinking. Its 32K 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.

SpecAmazon Nova MicroQwen3 Max Thinking
Context window128,000 tokens (128K)262,144 tokens (262K)
Max output tokens10,000 tokens (10K)32,768 tokens (32K)
Speed tierBalancedDeep
VisionNoNo
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/AFeb 2026

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 outQwen3 Max Thinking inQwen3 Max Thinking out
Aws Bedrock$0.035/M$0.140/M——

Frequently asked questions

Qwen3 Max Thinking has a larger context window: 262K 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.

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Use a smaller model.
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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.

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