MiMo-V2-Pro is $1 cheaper per 1M input tokens (50% lower; 2x difference).
API cost decision in 10 seconds
Gemini 3.1 Pro Preview vs MiMo-V2-Pro
Pick MiMo-V2-Pro when budget is the priority.
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Data confirmed:
Prices normalized to USD per 1M tokens
Sample workload: 1M input + 500K output
Budget verdict
Pick MiMo-V2-Pro when budget is the priority.
On the standard 1M input plus 500K output workload, MiMo-V2-Pro is estimated at $2.5 vs $8 for Gemini 3.1 Pro Preview, saving $5.5 (68.8% lower).
Cost-first pickMiMo-V2-Pro
Context-first pickBoth models
Sample savings$5.568.8%
10x traffic gap$55
The reported context window is tied, so cost and provider fit carry more weight. At 10x that traffic, the same price gap is about $55. Use the calculator below to replace the sample workload with your own token volume.
Cost sensitivity
Workload Sensitivity
MiMo-V2-Pro stays cheaper across input-heavy, balanced, and output-heavy sample workloads.
| Workload shape | Token mix | Better pick | Gemini 3.1 Pro Preview | MiMo-V2-Pro |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | MiMo-V2-Pro | $16 | $6.5 |
| Balanced workload | 1M input + 1M output | MiMo-V2-Pro | $14 | $4 |
| Output-heavy chatbot | 1M input + 5M output | MiMo-V2-Pro | $62 | $16 |
MiMo-V2-Pro is $9 cheaper per 1M output tokens (75% lower; 4x difference).
Both models report the same context window at 1.05M tokens.
MiMo-V2-Pro is $5.5 cheaper on the standard workload (68.8% lower).
Estimate your workload cost
Your Workload Cost
This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.
Quick Decision
MiMo-V2-Pro has the lower input price; MiMo-V2-Pro has the lower output price; both models report the same context window. For the 1M input plus 500K output sample, MiMo-V2-Pro is cheaper for the standard workload.
For a 1M input token plus 500K output token workload, the estimated API cost is $8 for Gemini 3.1 Pro Preview and $2.5 for MiMo-V2-Pro.
Choose Gemini 3.1 Pro Preview when its provider, model quality, latency, or availability is more important than the numeric price/context winner.
Choose MiMo-V2-Pro when you care most about lower input-token price, and lower output-token price.
- On the standard 1M input plus 500K output workload, MiMo-V2-Pro is estimated at $2.5 vs $8 for Gemini 3.1 Pro Preview, saving $5.5 (68.8% lower).
- MiMo-V2-Pro is $5.5 cheaper on the standard workload (68.8% lower).
- MiMo-V2-Pro is $1 cheaper per 1M input tokens (50% lower; 2x difference).
- MiMo-V2-Pro is $9 cheaper per 1M output tokens (75% lower; 4x difference).
- Both models report the same context window at 1.05M tokens.
| Feature | Gemini 3.1 Pro Preview (Google) | MiMo-V2-Pro (Xiaomi) |
|---|---|---|
| Input Price prompt tokens per 1M | $2 | $1 |
| Completion Price per 1M tokens | $12 | $3 |
| Sample Workload Cost 1M input + 500K output | $8 | $2.5 |
| Context Window | 1.05M | 1.05M |
| Release Date | ||
| Popularity | #28 | #74 |
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | MiMo-V2-Pro | On the standard 1M input plus 500K output workload, MiMo-V2-Pro is estimated at $2.5 vs $8 for Gemini 3.1 Pro Preview, saving $5.5 (68.8% lower). |
| High-volume input processing | MiMo-V2-Pro | Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill. |
| Long responses and chatbots | MiMo-V2-Pro | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | Tie | A larger context window leaves more room for retrieved passages, conversation history, or source files. |
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Cheaper alternatives
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Open Xiaomi modelsGemini 3.1 Pro Preview is Google’s frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage across complex workflows. Building on the multimodal foundation...
MiMo-V2-Pro is Xiaomi's flagship foundation model, featuring over 1T total parameters and a 1M context length, deeply optimized for agentic scenarios. It is highly adaptable to general agent frameworks like...