Context Window

Compare

Amazon Titan Text Premier vs Qwen3 Next 80B A3B Instruct

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 Titan Text Premier

Context window

42K

42,000 tokens · ~32K words

Model page
Alibaba

Model

Qwen3 Next 80B A3B Instruct

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 Titan Text Premier42K
Qwen3 Next 80B A3B Instruct262K

Qwen3 Next 80B A3B Instruct has about 6.2× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3 Next 80B A3B Instruct has 524% more context capacity (262K vs 42K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 Next 80B A3B Instruct. Its 262K context fits entire documents without chunking (vs 42K).

Full specs

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

SpecAmazon Titan Text PremierQwen3 Next 80B A3B Instruct
Context window42,000 tokens (42K)262,144 tokens (262K)
Max output tokens32,000 tokens (32K)N/A
Speed tierBalancedFast
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/ASep 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.

ProviderAmazon Titan Text Premier inAmazon Titan Text Premier outQwen3 Next 80B A3B Instruct inQwen3 Next 80B A3B Instruct out
Aws Bedrock$0.500/M$1.50/M——

Frequently asked questions

Qwen3 Next 80B A3B Instruct has a larger context window: 262K tokens vs 42K. 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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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.

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Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
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Current turn

80% less to send — works with any model