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Amazon Titan Text Lite vs Gpt 4 32k

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

Gpt 4 32k

Context window

33K

32,768 tokens · ~25K 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
Gpt 4 32k33K

Amazon Titan Text Lite has about 1.3× the context window of the other in this pair.

Amazon Titan Text Lite has 28% more context capacity (42K vs 32K tokens). Amazon Titan Text Lite is 99% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Amazon Titan Text Lite. Its 42K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Amazon Titan Text Lite. Input tokens are 99% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Gpt 4 32k. Its 4K 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 LiteGpt 4 32k
Context window42,000 tokens (42K)32,768 tokens (32K)
Max output tokens4,000 tokens (4K)4,096 tokens (4K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/AN/A

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 outGpt 4 32k inGpt 4 32k out
Aws Bedrock$0.300/M$0.400/M
Azure$60.00/M$120.00/M

Frequently asked questions

Amazon Titan Text Lite has a larger context window: 42K tokens vs 32K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

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
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model