Qwen: Qwen3.5 Plus 2026-02-15 vs Z.ai: GLM 5.3 (batch): Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3.5 Plus 2026-02-15 (qwen/qwen3.5-plus-02-15) vs Z.ai: GLM 5.3 (batch) (z-ai/glm-5.3:batch). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen3.5 Plus 2026-02-15 (qwen/qwen3.5-plus-02-15) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5 Plus 2026-02-15: 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.
Z.ai: GLM 5.3 (batch) (z-ai/glm-5.3:batch) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 5.3 (batch): Activate ephemeral prefix caching to slash input costs by 50% (down to $0.225/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Qwen: Qwen3.5 Plus 2026-02-15 (API ID: qwen/qwen3.5-plus-02-15) vs Z.ai: GLM 5.3 (batch) (API ID: z-ai/glm-5.3:batch), Qwen: Qwen3.5 Plus 2026-02-15 offers lower standard input pricing at $0.26/1M tokens ($1.56/1M output), while Qwen: Qwen3.5 Plus 2026-02-15 leads in cached prefix economics at $0.13/1M tokens (50% discount). Qwen: Qwen3.5 Plus 2026-02-15 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 | Qwen: Qwen3.5 Plus 2026-02-15 | Z.ai: GLM 5.3 (batch) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3.5-plus-02-15 | z-ai/glm-5.3:batch | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.26 / $1.56 | $0.45 / $2 | Qwen: Qwen3.5 Plus 2026-02-15 |
| Cache Read Price (per 1M) | $0.13/1M (50% Off) | $0.225/1M (50% Off) | Qwen: Qwen3.5 Plus 2026-02-15 |
| Context Window / Max Output | 977K In / 64K Out | 1024K In / 128K Out | Z.ai: GLM 5.3 (batch) |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3.5 Plus 2026-02-15 (-0ms) |
The Qwen3.5 native vision-language series Plus models are built on a hybrid architecture that integrates linear attention mechanisms with sparse mixture-of-experts models, achievin
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
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 Qwen: Qwen3.5 Plus 2026-02-15 to Z.ai: GLM 5.3 (batch)
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 Verified P90 latency of 280ms.