Z.ai: GLM 5.3 vs Qwen: Qwen3.5 397B A17B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 5.3 (z-ai/glm-5.3) 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 5.3 (z-ai/glm-5.3) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 5.3: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.015/1M) 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 5.3 (API ID: z-ai/glm-5.3) vs Qwen: Qwen3.5 397B A17B (API ID: qwen/qwen3.5-397b-a17b), Z.ai: GLM 5.3 offers lower standard input pricing at $0.03/1M tokens ($4.8/1M output), while Z.ai: GLM 5.3 leads in cached prefix economics at $0.015/1M tokens (50% discount). Z.ai: GLM 5.3 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 5.3 | Qwen: Qwen3.5 397B A17B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-5.3 | qwen/qwen3.5-397b-a17b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.03 / $4.8 | $0.5 / $3.6 | Z.ai: GLM 5.3 |
| Cache Read Price (per 1M) | $0.015/1M (50% Off) | $0.25/1M (50% Off) | Z.ai: GLM 5.3 |
| Context Window / Max Output | 1024K In / 922K Out | 256K In / 230K Out | Z.ai: GLM 5.3 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 5.3 (-0ms) |
GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context w
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 5.3 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.