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Amazon Titan Text Lite vs MiMo-V2-Flash
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.
MiMo-V2-Flash has about 6.2× the context window of the other in this pair.
MiMo-V2-Flash has 524% more context capacity (262K vs 42K tokens). MiMo-V2-Flash is 70% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use MiMo-V2-Flash. Its 262K context fits entire documents without chunking (vs 42K).
RAG / high-volume retrieval
Use MiMo-V2-Flash. Input tokens are 70% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use MiMo-V2-Flash. 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 Lite | MiMo-V2-Flash |
|---|---|---|
| Context window | 42,000 tokens (42K) | 262,144 tokens (262K) |
| Max output tokens | 4,000 tokens (4K) | 16,384 tokens (16K) |
| Speed tier | Balanced | Fast |
| Vision | No | No |
| Function calling | No | Yes |
| Extended thinking | No | Yes |
| Prompt caching | No | Yes |
| Batch API | No | No |
| Release date | N/A | Dec 2025 |
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 | MiMo-V2-Flash in | MiMo-V2-Flash out |
|---|---|---|---|---|
| Aws Bedrock | $0.300/M | $0.400/M | — | — |
| Novita | — | — | $0.100/M | $0.300/M |
| Openrouter | — | — | $0.090/M | $0.290/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