Z.ai: GLM 5.1 vs Qwen: Qwen3.5 397B A17B: 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 397B A17B (qwen/qwen3.5-397b-a17b). 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 397B A17B (qwen/qwen3.5-397b-a17b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5 397B A17B: 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 Z.ai: GLM 5.1 (API ID: z-ai/glm-5.1) vs Qwen: Qwen3.5 397B A17B (API ID: qwen/qwen3.5-397b-a17b), Qwen: Qwen3.5 397B A17B offers lower standard input pricing at $0.5/1M tokens ($3.6/1M output), while Qwen: Qwen3.5 397B A17B leads in cached prefix economics at $0.25/1M 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 397B A17B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-5.1 | qwen/qwen3.5-397b-a17b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.966 / $3.036 | $0.5 / $3.6 | Qwen: Qwen3.5 397B A17B |
| Cache Read Price (per 1M) | $0.483/1M (50% Off) | $0.25/1M (50% Off) | Qwen: Qwen3.5 397B A17B |
| Context Window / Max Output | 200K In / 125K Out | 256K In / 230K Out | Qwen: Qwen3.5 397B A17B |
| 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 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, ach
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 397B A17B
The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, ach Verified P90 latency of 280ms.