Qwen: Qwen3.6 Plus vs Qwen: Qwen3.5-122B-A10B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3.6 Plus (qwen/qwen3.6-plus) vs Qwen: Qwen3.5-122B-A10B (qwen/qwen3.5-122b-a10b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen3.6 Plus (qwen/qwen3.6-plus) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.6 Plus: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.1625/1M) 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 Qwen: Qwen3.6 Plus (API ID: qwen/qwen3.6-plus) vs Qwen: Qwen3.5-122B-A10B (API ID: qwen/qwen3.5-122b-a10b), Qwen: Qwen3.5-122B-A10B offers lower standard input pricing at $0.26/1M tokens ($2.08/1M output), while Qwen: Qwen3.5-122B-A10B leads in cached prefix economics at $0.13/1M tokens (50% discount). Qwen: Qwen3.6 Plus 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 | Qwen: Qwen3.6 Plus | Qwen: Qwen3.5-122B-A10B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3.6-plus | qwen/qwen3.5-122b-a10b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.325 / $1.95 | $0.26 / $2.08 | Qwen: Qwen3.5-122B-A10B |
| Cache Read Price (per 1M) | $0.1625/1M (50% Off) | $0.13/1M (50% Off) | Qwen: Qwen3.5-122B-A10B |
| Context Window / Max Output | 977K In / 64K Out | 256K In / 64K Out | Qwen: Qwen3.6 Plus |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3.6 Plus (-0ms) |
Qwen 3.6 Plus builds on a hybrid architecture that combines efficient linear attention with sparse mixture-of-experts routing, enabling strong scalability and high-performance infe
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 Qwen: Qwen3.6 Plus 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.