Z.ai: GLM Latest vs Qwen: Qwen3.5-122B-A10B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM Latest (~z-ai/glm-latest) vs Qwen: Qwen3.5-122B-A10B (qwen/qwen3.5-122b-a10b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Z.ai: GLM Latest (~z-ai/glm-latest) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM Latest: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.012/1M) or route via asynchronous Batch API for 50% off standard token rates.
Qwen: Qwen3.5-122B-A10B (qwen/qwen3.5-122b-a10b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-122B-A10B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.13/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Z.ai: GLM Latest (API ID: ~z-ai/glm-latest) vs Qwen: Qwen3.5-122B-A10B (API ID: qwen/qwen3.5-122b-a10b), Z.ai: GLM Latest offers lower standard input pricing at $0.024/1M tokens ($4.4/1M output), while Z.ai: GLM Latest leads in cached prefix economics at $0.012/1M tokens (50% discount). Z.ai: GLM Latest 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 Latest | Qwen: Qwen3.5-122B-A10B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | ~z-ai/glm-latest | qwen/qwen3.5-122b-a10b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.024 / $4.4 | $0.26 / $2.08 | Z.ai: GLM Latest |
| Cache Read Price (per 1M) | $0.012/1M (50% Off) | $0.13/1M (50% Off) | Z.ai: GLM Latest |
| Context Window / Max Output | 1024K In / 128K Out | 256K In / 64K Out | Z.ai: GLM Latest |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM Latest (-0ms) |
This model always redirects to the latest GLM model from Z.ai.
The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving
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 Latest to Qwen: Qwen3.5-122B-A10B
The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving Verified P90 latency of 280ms.