API cost decision in 10 seconds
MiMo-V2-Omni vs 🔥DeepSeek V3.2
Pick DeepSeek V3.2 for lower cost; pick MiMo-V2-Omni only if the larger context window matters more.
Pricing data updated:
Prices normalized to USD per 1M tokens
Sample workload: 1M input + 500K output
Budget verdict
Pick DeepSeek V3.2 for lower cost; pick MiMo-V2-Omni only if the larger context window matters more.
On the standard 1M input plus 500K output workload, DeepSeek V3.2 is estimated at $0.44 vs $1.4 for MiMo-V2-Omni, saving $0.96 (68.5% lower).
Cost-first pickDeepSeek V3.2
Context-first pickMiMo-V2-Omni
Sample savings$0.9668.5%
10x traffic gap$9.59
MiMo-V2-Omni has more context, but DeepSeek V3.2 saves $0.96 on the standard workload. At 10x that traffic, the same price gap is about $9.59. Use the calculator below to replace the sample workload with your own token volume.
Cost sensitivity
Workload Sensitivity
DeepSeek V3.2 stays cheaper across input-heavy, balanced, and output-heavy sample workloads.
| Workload shape | Token mix | Better pick | MiMo-V2-Omni | DeepSeek V3.2 |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | DeepSeek V3.2 | $3 | $1.45 |
| Balanced workload | 1M input + 1M output | DeepSeek V3.2 | $2.4 | $0.63 |
| Output-heavy chatbot | 1M input + 5M output | DeepSeek V3.2 | $10.4 | $2.14 |
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
DeepSeek V3.2 has the lower input price, DeepSeek V3.2 has the lower output price, and MiMo-V2-Omni offers the larger context window.
For a 1M input token plus 500K output token workload, the estimated API cost is $1.4 for MiMo-V2-Omni and $0.44 for DeepSeek V3.2.
Choose MiMo-V2-Omni when you care most about larger context window.
Choose DeepSeek V3.2 when you care most about lower input-token price, and lower output-token price.
| Feature | MiMo-V2-Omni (Xiaomi) | 🔥DeepSeek V3.2 (DeepSeek) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.4 | $0.25 |
| Completion Price per 1M tokens | $2 | $0.38 |
| Sample Workload Cost 1M input + 500K output | $1.4 | $0.44 |
| Context Window | 262.14K | 131.07K |
| Release Date | 2026-03-18 | 2025-12-01 |
| Popularity | #8 |
MiMo-V2-Omni is a frontier omni-modal model that natively processes image, video, and audio inputs within a unified architecture. It combines strong multimodal perception with agentic capability - visual grounding, multi-step...
DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | DeepSeek V3.2 | On the standard 1M input plus 500K output workload, DeepSeek V3.2 is estimated at $0.44 vs $1.4 for MiMo-V2-Omni, saving $0.96 (68.5% lower). |
| High-volume input processing | DeepSeek V3.2 | Lower prompt-token price matters most when prompts or retrieved passages dominate the bill. |
| Long responses and chatbots | DeepSeek V3.2 | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | MiMo-V2-Omni | A larger context window leaves more room for retrieved passages and source files. |