Z.ai: GLM 4.6V vs Qwen: Qwen3.6 35B A3B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 4.6V (z-ai/glm-4.6v) vs Qwen: Qwen3.6 35B A3B (qwen/qwen3.6-35b-a3b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Z.ai: GLM 4.6V (z-ai/glm-4.6v) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.6V: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.15/1M) or route via asynchronous Batch API for 50% off standard token rates.
Qwen: Qwen3.6 35B A3B (qwen/qwen3.6-35b-a3b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.6 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 4.6V (API ID: z-ai/glm-4.6v) vs Qwen: Qwen3.6 35B A3B (API ID: qwen/qwen3.6-35b-a3b), Qwen: Qwen3.6 35B A3B offers lower standard input pricing at $0.15/1M tokens ($1/1M output), while Qwen: Qwen3.6 35B A3B leads in cached prefix economics at $0.075/1M tokens (50% discount). Z.ai: GLM 4.6V 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 4.6V | Qwen: Qwen3.6 35B A3B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-4.6v | qwen/qwen3.6-35b-a3b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.3 / $0.9 | $0.15 / $1 | Qwen: Qwen3.6 35B A3B |
| Cache Read Price (per 1M) | $0.15/1M (50% Off) | $0.075/1M (50% Off) | Qwen: Qwen3.6 35B A3B |
| Context Window / Max Output | 128K In / 32K Out | 256K In / 230K Out | Qwen: Qwen3.6 35B A3B |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 4.6V (-0ms) |
GLM-4.6V is a large multimodal model designed for high-fidelity visual understanding and long-context reasoning across images, documents, and mixed media. It supports up to 128K to
Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35 billion total parameters and 3 billion active parameters per token. It uses a hybrid sparse mixture-of
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 4.6V to Qwen: Qwen3.6 35B A3B
Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35 billion total parameters and 3 billion active parameters per token. It uses a hybrid sparse mixture-of Verified P90 latency of 280ms.