Tencent: Hy-MT2-1.8B vs Z.ai: GLM 4.5V: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Tencent: Hy-MT2-1.8B (tencent/hy-mt2-1.8b) vs Z.ai: GLM 4.5V (z-ai/glm-4.5v). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
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.
Z.ai: GLM 4.5V (z-ai/glm-4.5v) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.5V: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.3/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Tencent: Hy-MT2-1.8B (API ID: tencent/hy-mt2-1.8b) vs Z.ai: GLM 4.5V (API ID: z-ai/glm-4.5v), 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). Tencent: Hy-MT2-1.8B 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 | Tencent: Hy-MT2-1.8B | Z.ai: GLM 4.5V | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | tencent/hy-mt2-1.8b | z-ai/glm-4.5v | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.044 / $0.177 | $0.6 / $1.8 | Tencent: Hy-MT2-1.8B |
| Cache Read Price (per 1M) | $0.022/1M (50% Off) | $0.3/1M (50% Off) | Tencent: Hy-MT2-1.8B |
| Context Window / Max Output | 8K In / 4K Out | 64K In / 16K Out | Z.ai: GLM 4.5V |
| P90 First-Token / Edge Latency | 280ms | 280ms | Tencent: Hy-MT2-1.8B (-0ms) |
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
GLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters
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 Tencent: Hy-MT2-1.8B to Z.ai: GLM 4.5V
GLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters Verified P90 latency of 280ms.