Microsoft: Phi 4 vs Qwen: Qwen3.5-122B-A10B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Microsoft: Phi 4 (microsoft/phi-4) vs Qwen: Qwen3.5-122B-A10B (qwen/qwen3.5-122b-a10b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Microsoft: Phi 4 (microsoft/phi-4) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Microsoft: Phi 4: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.035/1M) or route via asynchronous Batch API for 50% off standard token rates.
Qwen: Qwen3.5-122B-A10B (qwen/qwen3.5-122b-a10b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-122B-A10B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.13/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Microsoft: Phi 4 (API ID: microsoft/phi-4) vs Qwen: Qwen3.5-122B-A10B (API ID: qwen/qwen3.5-122b-a10b), Microsoft: Phi 4 offers lower standard input pricing at $0.07/1M tokens ($0.14/1M output), while Microsoft: Phi 4 leads in cached prefix economics at $0.035/1M tokens (50% discount). Microsoft: Phi 4 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 | Microsoft: Phi 4 | Qwen: Qwen3.5-122B-A10B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | microsoft/phi-4 | qwen/qwen3.5-122b-a10b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.07 / $0.14 | $0.26 / $2.08 | Microsoft: Phi 4 |
| Cache Read Price (per 1M) | $0.035/1M (50% Off) | $0.13/1M (50% Off) | Microsoft: Phi 4 |
| Context Window / Max Output | 16K In / 14K Out | 256K In / 64K Out | Qwen: Qwen3.5-122B-A10B |
| P90 First-Token / Edge Latency | 280ms | 280ms | Microsoft: Phi 4 (-0ms) |
[Microsoft Research](/microsoft) Phi-4 is designed to perform well in complex reasoning tasks and can operate efficiently in situations with limited memory or where quick responses
The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving
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 Microsoft: Phi 4 to Qwen: Qwen3.5-122B-A10B
The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving Verified P90 latency of 280ms.