inclusionAI: Ling 3.0 Flash vs Z.ai: GLM 5.2: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing inclusionAI: Ling 3.0 Flash (inclusionai/ling-3.0-flash) vs Z.ai: GLM 5.2 (z-ai/glm-5.2). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
inclusionAI: Ling 3.0 Flash (inclusionai/ling-3.0-flash) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for inclusionAI: Ling 3.0 Flash: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.0105/1M) 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 $0.1/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing inclusionAI: Ling 3.0 Flash (API ID: inclusionai/ling-3.0-flash) vs Z.ai: GLM 5.2 (API ID: z-ai/glm-5.2), inclusionAI: Ling 3.0 Flash offers lower standard input pricing at $0.021/1M tokens ($0.063/1M output), while inclusionAI: Ling 3.0 Flash leads in cached prefix economics at $0.0105/1M tokens (50% discount). inclusionAI: Ling 3.0 Flash 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 | inclusionAI: Ling 3.0 Flash | Z.ai: GLM 5.2 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | inclusionai/ling-3.0-flash | z-ai/glm-5.2 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.021 / $0.063 | $0.2 / $10 | inclusionAI: Ling 3.0 Flash |
| Cache Read Price (per 1M) | $0.0105/1M (50% Off) | $0.1/1M (50% Off) | inclusionAI: Ling 3.0 Flash |
| Context Window / Max Output | 256K In / 32K Out | 1024K In / 922K Out | Z.ai: GLM 5.2 |
| P90 First-Token / Edge Latency | 280ms | 280ms | inclusionAI: Ling 3.0 Flash (-0ms) |
*Ling-3.0-flash* is a *124B-parameter Mixture-of-Experts (MoE) model*, with approximately *5.1B parameters activated per token*. The model is designed with *token efficiency and pr
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 inclusionAI: Ling 3.0 Flash 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.