Tencent: Hy-MT2-7B vs Z.ai: GLM 5.3 (batch): Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Tencent: Hy-MT2-7B (tencent/hy-mt2-7b) vs Z.ai: GLM 5.3 (batch) (z-ai/glm-5.3:batch). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Tencent: Hy-MT2-7B (tencent/hy-mt2-7b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Tencent: Hy-MT2-7B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.037/1M) or route via asynchronous Batch API for 50% off standard token rates.
Z.ai: GLM 5.3 (batch) (z-ai/glm-5.3:batch) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 5.3 (batch): Activate ephemeral prefix caching to slash input costs by 50% (down to $0.225/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Tencent: Hy-MT2-7B (API ID: tencent/hy-mt2-7b) vs Z.ai: GLM 5.3 (batch) (API ID: z-ai/glm-5.3:batch), Tencent: Hy-MT2-7B offers lower standard input pricing at $0.074/1M tokens ($0.295/1M output), while Tencent: Hy-MT2-7B leads in cached prefix economics at $0.037/1M tokens (50% discount). Tencent: Hy-MT2-7B 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-MT2-7B | Z.ai: GLM 5.3 (batch) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | tencent/hy-mt2-7b | z-ai/glm-5.3:batch | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.074 / $0.295 | $0.45 / $2 | Tencent: Hy-MT2-7B |
| Cache Read Price (per 1M) | $0.037/1M (50% Off) | $0.225/1M (50% Off) | Tencent: Hy-MT2-7B |
| Context Window / Max Output | 8K In / 4K Out | 1024K In / 128K Out | Z.ai: GLM 5.3 (batch) |
| P90 First-Token / Edge Latency | 280ms | 280ms | Tencent: Hy-MT2-7B (-0ms) |
Hy-MT2-7B is a 7B-parameter translation model from Tencent. It supports 33 language pairs and five Chinese dialect and minority-language pairs, with workflows for structured, delim
GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context w
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-MT2-7B to Z.ai: GLM 5.3 (batch)
GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context w Verified P90 latency of 280ms.