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Amazon Titan Text Express vs Gpt 5 5
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 5 5 has about 6.5× the context window of the other in this pair.
Gpt 5 5 has 547% more context capacity (272K vs 42K tokens). Amazon Titan Text Express is 74% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Gpt 5 5. Its 272K context fits entire documents without chunking (vs 42K).
RAG / high-volume retrieval
Use Amazon Titan Text Express. Input tokens are 74% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Gpt 5 5. 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.
| Spec | Amazon Titan Text Express | Gpt 5 5 |
|---|---|---|
| Context window | 42,000 tokens (42K) | 272,000 tokens (272K) |
| Max output tokens | 8,000 tokens (8K) | 128,000 tokens (128K) |
| Speed tier | Balanced | Balanced |
| Vision | No | Yes |
| Function calling | No | Yes |
| Extended thinking | No | Yes |
| Prompt caching | No | Yes |
| Batch API | No | No |
| Release date | N/A | N/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.
| Provider | Amazon Titan Text Express in | Amazon Titan Text Express out | Gpt 5 5 in | Gpt 5 5 out |
|---|---|---|---|---|
| Aws Bedrock | $1.30/M | $1.70/M | — | — |
| Azure | — | — | $5.00/M | $30.00/M |
| Openai | — | — | $5.00/M | $30.00/M |
Frequently asked questions
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
80% less to send — works with any model