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Devstral Small 1.1 vs Gemini Omni 1.2 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.
Same context window size for both models.
Devstral Small 1.1 and Gemini Omni 1.2 Flash have identical context windows (131K tokens).
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Devstral Small 1.1 | Gemini Omni 1.2 Flash |
|---|---|---|
| Context window | 131,072 tokens (131K) | 131,072 tokens (131K) |
| Max output tokens | N/A | 260,640 tokens (260K) |
| Speed tier | Balanced | Fast |
| Vision | No | Yes |
| Function calling | Yes | No |
| Extended thinking | No | Yes |
| Prompt caching | Yes | No |
| Batch API | No | No |
| Release date | Jul 2025 | Nov 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 | Devstral Small 1.1 in | Devstral Small 1.1 out | Gemini Omni 1.2 Flash in | Gemini Omni 1.2 Flash out |
|---|---|---|---|---|
| — | — | $1.50/M | $9.00/M | |
| Google Vertex | — | — | $1.50/M | $9.00/M |
Frequently asked questions
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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