Z.ai: GLM 5.2 vs Qwen: Qwen3.5-27B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 5.2 (z-ai/glm-5.2) vs Qwen: Qwen3.5-27B (qwen/qwen3.5-27b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Z.ai: GLM 5.2 (z-ai/glm-5.2) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 5.2: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.01/1M) or route via asynchronous Batch API for 50% off standard token rates.
Qwen: Qwen3.5-27B (qwen/qwen3.5-27b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5-27B: 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 5.2 (API ID: z-ai/glm-5.2) vs Qwen: Qwen3.5-27B (API ID: qwen/qwen3.5-27b), Z.ai: GLM 5.2 offers lower standard input pricing at $0.02/1M tokens ($7/1M output), while Z.ai: GLM 5.2 leads in cached prefix economics at $0.01/1M tokens (50% discount). Z.ai: GLM 5.2 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 5.2 | Qwen: Qwen3.5-27B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-5.2 | qwen/qwen3.5-27b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.02 / $7 | $0.26 / $2.6 | Z.ai: GLM 5.2 |
| Cache Read Price (per 1M) | $0.01/1M (50% Off) | $0.13/1M (50% Off) | Z.ai: GLM 5.2 |
| Context Window / Max Output | 1024K In / 922K Out | 256K In / 80K Out | Z.ai: GLM 5.2 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 5.2 (-0ms) |
GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level
The Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing inference speed and performance. Its ov
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 5.2 to Qwen: Qwen3.5-27B
The Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing inference speed and performance. Its ov Verified P90 latency of 280ms.