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Amazon Titan Text Premier vs GPT-5.4

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

GPT-5.4

Image inputTool calling

Context window

1.1M

1,050,000 tokens · ~788K 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
GPT-5.41.1M

GPT-5.4 has about 25× the context window of the other in this pair.

GPT-5.4 has 2400% more context capacity (1050K vs 42K tokens). Amazon Titan Text Premier is 80% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-5.4. Its 1050K context fits entire documents without chunking (vs 42K).

  • RAG / high-volume retrieval

    Use Amazon Titan Text Premier. Input tokens are 80% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use GPT-5.4. Its 128K 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 PremierGPT-5.4
Context window42,000 tokens (42K)1,050,000 tokens (1050K)
Max output tokens32,000 tokens (32K)128,000 tokens (128K)
Speed tierBalancedBalanced
VisionNoYes
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
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 outGPT-5.4 inGPT-5.4 out
Aws Bedrock$0.500/M$1.50/M
Azure$2.50/M$15.00/M
Openai$2.50/M$15.00/M

Frequently asked questions

GPT-5.4 has a larger context window: 1050K 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.

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