Z.ai: GLM 4.6 vs inclusionAI: Ling 3.0 Flash: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 4.6 (z-ai/glm-4.6) vs inclusionAI: Ling 3.0 Flash (inclusionai/ling-3.0-flash). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Z.ai: GLM 4.6 (z-ai/glm-4.6) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.6: 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.
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.
In Etuki October 2026 FinOps telemetry comparing Z.ai: GLM 4.6 (API ID: z-ai/glm-4.6) vs inclusionAI: Ling 3.0 Flash (API ID: inclusionai/ling-3.0-flash), 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). Z.ai: GLM 4.6 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 4.6 | inclusionAI: Ling 3.0 Flash | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-4.6 | inclusionai/ling-3.0-flash | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.5 / $2 | $0.021 / $0.063 | inclusionAI: Ling 3.0 Flash |
| Cache Read Price (per 1M) | $0.25/1M (50% Off) | $0.0105/1M (50% Off) | inclusionAI: Ling 3.0 Flash |
| Context Window / Max Output | 200K In / 128K Out | 256K In / 32K Out | Z.ai: GLM 4.6 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 4.6 (-0ms) |
Compared with GLM-4.5, this generation brings several key improvements: Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to
*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
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 4.6 to inclusionAI: Ling 3.0 Flash
*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 Verified P90 latency of 280ms.