Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | MiMo-V2-Pro | Qwen3.5 Omni Plus |
|---|---|---|
| Input ($/M tokens) | — | $0.4 |
| Output ($/M tokens) | — | $4.8 |
Verdict. MiMo-V2-Pro wins the overall benchmark matchup 1–0 across 1 overlapping categories, but raw benchmark score is only one input to the decision.
Pricing. Pricing varies significantly between these models — check the table above for the exact per-token rates. Many production workloads actually surface input-token cost (retrieval-augmented prompts, code-context windows), so factor both directions.
Strengths. MiMo-V2-Pro is strongest on Intelligence Index (41.4). Qwen3.5 Omni Plus leads on Intelligence Index (31.3).
Speed. Speed data is incomplete for this pair; benchmark and price should decide.
Provider. Xiaomi and Alibaba sell to overlapping but distinct developer audiences: Xiaomi tends to ship frontier reasoning models with premium positioning, while Alibaba often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Data from Artificial Analysis API — 12 benchmarks
Qwen3.5 Omni Plus is cheaper overall. Its blended price (3:1 input/output ratio) is $1.50/M tokens vs $—/M for MiMo-V2-Pro.
MiMo-V2-Pro wins 1 out of 12 benchmarks compared to 0 for Qwen3.5 Omni Plus. See the detailed benchmark chart above for per-category results.
Qwen3.5 Omni Plus generates tokens faster at 49 tok/s vs — tok/s. However, Qwen3.5 Omni Plus has lower time-to-first-token (2.51s vs —s).
Choose based on your priorities: Qwen3.5 Omni Plus for lower cost, MiMo-V2-Pro for stronger benchmark performance, and Qwen3.5 Omni Plus for faster generation. For latency-sensitive apps, check the TTFT comparison above.