Compare
Amazon Titan Text Lite vs Ft:gpt 4 1 2025 04 14
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.
Model
Ft:gpt 4 1 2025 04 14
Context window
1.0M
1,047,576 tokens · ~786K words
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.
Ft:gpt 4 1 2025 04 14 has about 24.9× the context window of the other in this pair.
Ft:gpt 4 1 2025 04 14 has 2394% more context capacity (1047K vs 42K tokens). Amazon Titan Text Lite is 90% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Ft:gpt 4 1 2025 04 14. Its 1047K context fits entire documents without chunking (vs 42K).
RAG / high-volume retrieval
Use Amazon Titan Text Lite. Input tokens are 90% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Ft:gpt 4 1 2025 04 14. Its 32K 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 Lite | Ft:gpt 4 1 2025 04 14 |
|---|---|---|
| Context window | 42,000 tokens (42K) | 1,047,576 tokens (1047K) |
| Max output tokens | 4,000 tokens (4K) | 32,768 tokens (32K) |
| Speed tier | Balanced | Balanced |
| Vision | No | No |
| Function calling | No | Yes |
| Extended thinking | No | No |
| 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 Lite in | Amazon Titan Text Lite out | Ft:gpt 4 1 2025 04 14 in | Ft:gpt 4 1 2025 04 14 out |
|---|---|---|---|---|
| Aws Bedrock | $0.300/M | $0.400/M | — | — |
| Openai | — | — | $3.00/M | $12.00/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