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Amazon Titan Text Express vs GPT Audio
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.
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.
GPT Audio has about 3× the context window of the other in this pair.
GPT Audio has 204% more context capacity (128K vs 42K tokens). Amazon Titan Text Express is 48% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use GPT Audio. Its 128K context fits entire documents without chunking (vs 42K).
RAG / high-volume retrieval
Use Amazon Titan Text Express. Input tokens are 48% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use GPT Audio. Its 16K 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.
| Spec | Amazon Titan Text Express | GPT Audio |
|---|---|---|
| Context window | 42,000 tokens (42K) | 128,000 tokens (128K) |
| Max output tokens | 8,000 tokens (8K) | 16,384 tokens (16K) |
| Speed tier | Balanced | Balanced |
| Vision | No | No |
| Function calling | No | Yes |
| Extended thinking | No | No |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | N/A | Jan 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.
| Provider | Amazon Titan Text Express in | Amazon Titan Text Express out | GPT Audio in | GPT Audio out |
|---|---|---|---|---|
| Aws Bedrock | $1.30/M | $1.70/M | — | — |
| Openai | — | — | $2.50/M | $10.00/M |
Frequently asked questions
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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
80% less to send — works with any model