Compare
Amazon Titan Text Express vs Jamba 1 5 Mini
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.
Jamba 1 5 Mini has about 6.1× the context window of the other in this pair.
Jamba 1 5 Mini has 509% more context capacity (256K vs 42K tokens). Jamba 1 5 Mini is 84% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Jamba 1 5 Mini. Its 256K context fits entire documents without chunking (vs 42K).
RAG / high-volume retrieval
Use Jamba 1 5 Mini. Input tokens are 84% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Jamba 1 5 Mini. Its 256K 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 | Jamba 1 5 Mini |
|---|---|---|
| Context window | 42,000 tokens (42K) | 256,000 tokens (256K) |
| Max output tokens | 8,000 tokens (8K) | 256,000 tokens (256K) |
| Speed tier | Balanced | Fast |
| Vision | No | No |
| Function calling | No | No |
| Extended thinking | No | No |
| Prompt caching | No | No |
| 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 | Jamba 1 5 Mini in | Jamba 1 5 Mini out |
|---|---|---|---|---|
| Ai21 | — | — | $0.200/M | $0.400/M |
| Aws Bedrock | $1.30/M | $1.70/M | — | — |
| Google Vertex | — | — | $0.200/M | $0.400/M |
| Snowflake | — | — | — | — |
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