Z.ai: GLM 5.1 vs Qwen: Qwen3.5-35B-A3B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 5.1 (z-ai/glm-5.1) vs Qwen: Qwen3.5-35B-A3B (qwen/qwen3.5-35b-a3b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Z.ai: GLM 5.1 (z-ai/glm-5.1) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 5.1: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.483/1M) or route via asynchronous Batch API for 50% off standard token rates.
Qwen: Qwen3.5-35B-A3B (qwen/qwen3.5-35b-a3b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-35B-A3B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.075/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Z.ai: GLM 5.1 (API ID: z-ai/glm-5.1) vs Qwen: Qwen3.5-35B-A3B (API ID: qwen/qwen3.5-35b-a3b), Qwen: Qwen3.5-35B-A3B offers lower standard input pricing at $0.15/1M tokens ($1/1M output), while Qwen: Qwen3.5-35B-A3B leads in cached prefix economics at $0.075/1M tokens (50% discount). Z.ai: GLM 5.1 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 | Z.ai: GLM 5.1 | Qwen: Qwen3.5-35B-A3B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-5.1 | qwen/qwen3.5-35b-a3b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.966 / $3.036 | $0.15 / $1 | Qwen: Qwen3.5-35B-A3B |
| Cache Read Price (per 1M) | $0.483/1M (50% Off) | $0.075/1M (50% Off) | Qwen: Qwen3.5-35B-A3B |
| Context Window / Max Output | 200K In / 125K Out | 256K In / 16K Out | Z.ai: GLM 5.1 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 5.1 (-0ms) |
GLM-5.1 delivers a major leap in coding capability, with particularly significant gains in handling long-horizon tasks. Unlike previous models built around minute-level interaction
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model,
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 Z.ai: GLM 5.1 to Qwen: Qwen3.5-35B-A3B
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, Verified P90 latency of 280ms.