Context Window

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Amazon Titan Text Premier vs Lyria 3 Clip Preview

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
Google

Model

Lyria 3 Clip Preview

Image input

Context window

131K

131,072 tokens · ~98K 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
Lyria 3 Clip Preview131K

Lyria 3 Clip Preview has about 3.1× the context window of the other in this pair.

Token calculator →Memory savings →

Lyria 3 Clip Preview has 212% more context capacity (131K vs 42K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Lyria 3 Clip Preview. Its 131K context fits entire documents without chunking (vs 42K).

  • Long output (reports, code files)

    Use Amazon Titan Text Premier. 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 Titan Text PremierLyria 3 Clip Preview
Context window42,000 tokens (42K)131,072 tokens (131K)
Max output tokens32,000 tokens (32K)8,192 tokens (8K)
Speed tierBalancedBalanced
VisionNoYes
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/AMar 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 Titan Text Premier inAmazon Titan Text Premier outLyria 3 Clip Preview inLyria 3 Clip Preview out
Aws Bedrock$0.500/M$1.50/M——
Google————

Frequently asked questions

Lyria 3 Clip Preview has a larger context window: 131K 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
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model