Qwen: Qwen3 Coder Next vs Z.ai: GLM 5.2: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3 Coder Next (qwen/qwen3-coder-next) vs Z.ai: GLM 5.2 (z-ai/glm-5.2). 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 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 $0.03/1M) 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 5.2 (API ID: z-ai/glm-5.2), Z.ai: GLM 5.2 offers lower standard input pricing at $0.06/1M tokens ($7/1M output), while Z.ai: GLM 5.2 leads in cached prefix economics at $0.03/1M 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 5.2 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3-coder-next | z-ai/glm-5.2 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.12 / $0.8 | $0.06 / $7 | Z.ai: GLM 5.2 |
| Cache Read Price (per 1M) | $0.06/1M (50% Off) | $0.03/1M (50% Off) | Z.ai: GLM 5.2 |
| Context Window / Max Output | 256K In / 230K Out | 1024K In / 922K Out | Z.ai: GLM 5.2 |
| 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 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
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 5.2
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 Verified P90 latency of 280ms.