Meta: Llama 4 Scout vs Xiaomi: MiMo-V2.5-Pro: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Meta: Llama 4 Scout (meta-llama/llama-4-scout) vs Xiaomi: MiMo-V2.5-Pro (xiaomi/mimo-v2.5-pro). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Meta: Llama 4 Scout (meta-llama/llama-4-scout) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Llama 4 Scout: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.09/1M) or route via asynchronous Batch API for 50% off standard token rates.
Xiaomi: MiMo-V2.5-Pro (xiaomi/mimo-v2.5-pro) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Xiaomi: MiMo-V2.5-Pro: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.2175/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Meta: Llama 4 Scout (API ID: meta-llama/llama-4-scout) vs Xiaomi: MiMo-V2.5-Pro (API ID: xiaomi/mimo-v2.5-pro), Meta: Llama 4 Scout offers lower standard input pricing at $0.18/1M tokens ($0.59/1M output), while Meta: Llama 4 Scout leads in cached prefix economics at $0.09/1M tokens (50% discount). Meta: Llama 4 Scout 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 | Meta: Llama 4 Scout | Xiaomi: MiMo-V2.5-Pro | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | meta-llama/llama-4-scout | xiaomi/mimo-v2.5-pro | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.18 / $0.59 | $0.435 / $0.87 | Meta: Llama 4 Scout |
| Cache Read Price (per 1M) | $0.09/1M (50% Off) | $0.2175/1M (50% Off) | Meta: Llama 4 Scout |
| Context Window / Max Output | 1280K In / 115K Out | 1025K In / 128K Out | Xiaomi: MiMo-V2.5-Pro |
| P90 First-Token / Edge Latency | 280ms | 280ms | Meta: Llama 4 Scout (-0ms) |
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimo
MiMo-V2.5-Pro is Xiaomi’s flagship model, delivering strong performance in general agentic capabilities, complex software engineering, and long-horizon tasks, with top rankings on
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 Meta: Llama 4 Scout to Xiaomi: MiMo-V2.5-Pro
MiMo-V2.5-Pro is Xiaomi’s flagship model, delivering strong performance in general agentic capabilities, complex software engineering, and long-horizon tasks, with top rankings on Verified P90 latency of 280ms.