Xiaomi: MiMo-V2.5 vs DeepSeek: R1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Xiaomi: MiMo-V2.5 (xiaomi/mimo-v2.5) vs DeepSeek: R1 (deepseek/deepseek-r1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Xiaomi: MiMo-V2.5 (xiaomi/mimo-v2.5) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Xiaomi: MiMo-V2.5: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.07/1M) or route via asynchronous Batch API for 50% off standard token rates.
DeepSeek: R1 (deepseek/deepseek-r1) — Automatic Disk KV Cache ($0.35/1M) & Off-Peak Promo
DeepSeek-V4 Pro (1.6T MoE) automatically caches repeated 64-token prompt prefixes on NVMe storage at $0.07/1M input tokens (50% off $0.14 base) with zero code changes required, plus off-peak UTC scheduling discounts.
In Etuki October 2026 FinOps telemetry comparing Xiaomi: MiMo-V2.5 (API ID: xiaomi/mimo-v2.5) vs DeepSeek: R1 (API ID: deepseek/deepseek-r1), Xiaomi: MiMo-V2.5 offers lower standard input pricing at $0.14/1M tokens ($0.28/1M output), while Xiaomi: MiMo-V2.5 leads in cached prefix economics at $0.07/1M tokens (50% discount). Xiaomi: MiMo-V2.5 achieves a 0ms faster P90 time-to-first-token latency (280ms vs 280ms).
Auto-discovered via live global telemetry registry.
Auto-discovered via live global telemetry registry.
| Specification / FinOps Dimension | Xiaomi: MiMo-V2.5 | DeepSeek: R1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | xiaomi/mimo-v2.5 | deepseek/deepseek-r1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.14 / $0.28 | $0.7 / $2.5 | Xiaomi: MiMo-V2.5 |
| Cache Read Price (per 1M) | $0.07/1M (50% Off) | $0.35/1M (50% Off) | Xiaomi: MiMo-V2.5 |
| Context Window / Max Output | 1025K In / 128K Out | 63K In / 16K Out | Xiaomi: MiMo-V2.5 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Xiaomi: MiMo-V2.5 (-0ms) |
MiMo-V2.5 is a native omnimodal model by Xiaomi. It delivers Pro-level agentic performance at roughly half the inference cost, while surpassing MiMo-V2-Omni in multimodal perceptio
DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inf
Real-Time Stack Cost & Hidden-Fee Simulator (MAU / Token / Egress / GMV)
Calibrated with Reddit r/FinOps & r/devops telemetry: Includes Context Caching, Bandwidth Egress, and MoR Tax
🛰️Switch from Xiaomi: MiMo-V2.5 to DeepSeek: R1
DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inf Verified P90 latency of 280ms.