Z.ai: GLM 4.7 Flash vs Qwen: Qwen3.6 35B A3B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 4.7 Flash (z-ai/glm-4.7-flash) 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 4.7 Flash (z-ai/glm-4.7-flash) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.7 Flash: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.0302/1M) 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 4.7 Flash (API ID: z-ai/glm-4.7-flash) vs Qwen: Qwen3.6 35B A3B (API ID: qwen/qwen3.6-35b-a3b), Z.ai: GLM 4.7 Flash offers lower standard input pricing at $0.0605/1M tokens ($0.4/1M output), while Z.ai: GLM 4.7 Flash leads in cached prefix economics at $0.0302/1M tokens (50% discount). Z.ai: GLM 4.7 Flash 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 4.7 Flash | Qwen: Qwen3.6 35B A3B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-4.7-flash | qwen/qwen3.6-35b-a3b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.0605 / $0.4 | $0.15 / $1 | Z.ai: GLM 4.7 Flash |
| Cache Read Price (per 1M) | $0.0302/1M (50% Off) | $0.075/1M (50% Off) | Z.ai: GLM 4.7 Flash |
| Context Window / Max Output | 195K In / 115K Out | 256K In / 230K Out | Qwen: Qwen3.6 35B A3B |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 4.7 Flash (-0ms) |
As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding ca
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 4.7 Flash 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.