Context Window

Compare

Amazon Titan Text Lite vs o4 Mini High

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 Lite

Context window

42K

42,000 tokens · ~32K words

Model page
Openai

Model

o4 Mini High

Image inputTool calling

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 Lite42K
o4 Mini High200K

o4 Mini High has about 4.8× the context window of the other in this pair.

Token calculator →Memory savings →

o4 Mini High has 376% more context capacity (200K vs 42K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use o4 Mini High. Its 200K context fits entire documents without chunking (vs 42K).

  • Long output (reports, code files)

    Use o4 Mini High. 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 Liteo4 Mini High
Context window42,000 tokens (42K)200,000 tokens (200K)
Max output tokens4,000 tokens (4K)100,000 tokens (100K)
Speed tierBalancedFast
VisionNoYes
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APINoNo
Release dateN/AApr 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 Lite inAmazon Titan Text Lite outo4 Mini High ino4 Mini High out
Aws Bedrock$0.300/M$0.400/M——

Frequently asked questions

o4 Mini High 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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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