Qwen: Qwen3.8 Max (0902) vs Z.ai: GLM 4.6: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3.8 Max (0902) (qwen/qwen3.8-max-0902) vs Z.ai: GLM 4.6 (z-ai/glm-4.6). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen3.8 Max (0902) (qwen/qwen3.8-max-0902) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.8 Max (0902): Activate ephemeral prefix caching to slash input costs by 50% (down to $1/1M) or route via asynchronous Batch API for 50% off standard token rates.
Z.ai: GLM 4.6 (z-ai/glm-4.6) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.6: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.25/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Qwen: Qwen3.8 Max (0902) (API ID: qwen/qwen3.8-max-0902) vs Z.ai: GLM 4.6 (API ID: z-ai/glm-4.6), Z.ai: GLM 4.6 offers lower standard input pricing at $0.5/1M tokens ($2/1M output), while Z.ai: GLM 4.6 leads in cached prefix economics at $0.25/1M tokens (50% discount). Qwen: Qwen3.8 Max (0902) 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: Qwen3.8 Max (0902) | Z.ai: GLM 4.6 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3.8-max-0902 | z-ai/glm-4.6 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $2 / $6 | $0.5 / $2 | Z.ai: GLM 4.6 |
| Cache Read Price (per 1M) | $1/1M (50% Off) | $0.25/1M (50% Off) | Z.ai: GLM 4.6 |
| Context Window / Max Output | 977K In / 128K Out | 200K In / 128K Out | Qwen: Qwen3.8 Max (0902) |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3.8 Max (0902) (-0ms) |
Qwen3.8 Max 0902 is an updated snapshot of Qwen3.8 Max from Alibaba's Qwen team. It is a 2.4-trillion-parameter mixture-of-experts model that accepts text, image, and video input a
Compared with GLM-4.5, this generation brings several key improvements: Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to
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: Qwen3.8 Max (0902) to Z.ai: GLM 4.6
Compared with GLM-4.5, this generation brings several key improvements: Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to Verified P90 latency of 280ms.