Qwen: Qwen3 Coder Next vs Z.ai: GLM 4.5V: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3 Coder Next (qwen/qwen3-coder-next) vs Z.ai: GLM 4.5V (z-ai/glm-4.5v). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen3 Coder Next (qwen/qwen3-coder-next) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3 Coder Next: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.06/1M) or route via asynchronous Batch API for 50% off standard token rates.
Z.ai: GLM 4.5V (z-ai/glm-4.5v) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.5V: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.3/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Qwen: Qwen3 Coder Next (API ID: qwen/qwen3-coder-next) vs Z.ai: GLM 4.5V (API ID: z-ai/glm-4.5v), Qwen: Qwen3 Coder Next offers lower standard input pricing at $0.12/1M tokens ($0.8/1M output), while Qwen: Qwen3 Coder Next leads in cached prefix economics at $0.06/1M tokens (50% discount). Qwen: Qwen3 Coder Next 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 Coder Next | Z.ai: GLM 4.5V | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3-coder-next | z-ai/glm-4.5v | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.12 / $0.8 | $0.6 / $1.8 | Qwen: Qwen3 Coder Next |
| Cache Read Price (per 1M) | $0.06/1M (50% Off) | $0.3/1M (50% Off) | Qwen: Qwen3 Coder Next |
| Context Window / Max Output | 256K In / 230K Out | 64K In / 16K Out | Qwen: Qwen3 Coder Next |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3 Coder Next (-0ms) |
Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only
GLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters
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 Coder Next to Z.ai: GLM 4.5V
GLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters Verified P90 latency of 280ms.