Qwen: Qwen3.5-Flash vs Tencent: Hy-MT2-1.8B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3.5-Flash (qwen/qwen3.5-flash-02-23) vs Tencent: Hy-MT2-1.8B (tencent/hy-mt2-1.8b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen3.5-Flash (qwen/qwen3.5-flash-02-23) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-Flash: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.0325/1M) or route via asynchronous Batch API for 50% off standard token rates.
Tencent: Hy-MT2-1.8B (tencent/hy-mt2-1.8b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Tencent: Hy-MT2-1.8B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.022/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Qwen: Qwen3.5-Flash (API ID: qwen/qwen3.5-flash-02-23) vs Tencent: Hy-MT2-1.8B (API ID: tencent/hy-mt2-1.8b), Tencent: Hy-MT2-1.8B offers lower standard input pricing at $0.044/1M tokens ($0.177/1M output), while Tencent: Hy-MT2-1.8B leads in cached prefix economics at $0.022/1M tokens (50% discount). Qwen: Qwen3.5-Flash 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: Qwen3.5-Flash | Tencent: Hy-MT2-1.8B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3.5-flash-02-23 | tencent/hy-mt2-1.8b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.065 / $0.26 | $0.044 / $0.177 | Tencent: Hy-MT2-1.8B |
| Cache Read Price (per 1M) | $0.0325/1M (50% Off) | $0.022/1M (50% Off) | Tencent: Hy-MT2-1.8B |
| Context Window / Max Output | 977K In / 64K Out | 8K In / 4K Out | Qwen: Qwen3.5-Flash |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3.5-Flash (-0ms) |
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving hi
Hy-MT2-1.8B is a compact 1.8B-parameter translation model from Tencent. It supports 33 language pairs and five Chinese dialect and minority-language pairs, with workflows for struc
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: Qwen3.5-Flash to Tencent: Hy-MT2-1.8B
Hy-MT2-1.8B is a compact 1.8B-parameter translation model from Tencent. It supports 33 language pairs and five Chinese dialect and minority-language pairs, with workflows for struc Verified P90 latency of 280ms.