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Amazon Titan Text Premier vs o1-pro

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
Openai

Model

o1-pro

Image input

Context window

200K

200,000 tokens · ~150K 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
o1-pro200K

o1-pro has about 4.8× the context window of the other in this pair.

o1-pro has 376% more context capacity (200K vs 42K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use o1-pro. Its 200K context fits entire documents without chunking (vs 42K).

  • Long output (reports, code files)

    Use o1-pro. Its 100K 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 Premiero1-pro
Context window42,000 tokens (42K)200,000 tokens (200K)
Max output tokens32,000 tokens (32K)100,000 tokens (100K)
Speed tierBalancedDeep
VisionNoYes
Function callingNoNo
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoYes
Release dateN/AMar 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 outo1-pro ino1-pro out
Aws Bedrock$0.500/M$1.50/M

Frequently asked questions

o1-pro has a larger context window: 200K 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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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.

Example: a multi-turn chat session

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