Z.ai: GLM 4.5 Air vs Pareto 26.9: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 4.5 Air (z-ai/glm-4.5-air) vs Pareto 26.9 (unbiased/pareto-26.9). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Z.ai: GLM 4.5 Air (z-ai/glm-4.5-air) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.5 Air: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.065/1M) or route via asynchronous Batch API for 50% off standard token rates.
Pareto 26.9 (unbiased/pareto-26.9) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Pareto 26.9: Activate ephemeral prefix caching to slash input costs by 50% (down to $1.25/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Z.ai: GLM 4.5 Air (API ID: z-ai/glm-4.5-air) vs Pareto 26.9 (API ID: unbiased/pareto-26.9), Z.ai: GLM 4.5 Air offers lower standard input pricing at $0.13/1M tokens ($0.85/1M output), while Z.ai: GLM 4.5 Air leads in cached prefix economics at $0.065/1M tokens (50% discount). Z.ai: GLM 4.5 Air 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 4.5 Air | Pareto 26.9 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-4.5-air | unbiased/pareto-26.9 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.13 / $0.85 | $2.5 / $7.5 | Z.ai: GLM 4.5 Air |
| Cache Read Price (per 1M) | $0.065/1M (50% Off) | $1.25/1M (50% Off) | Z.ai: GLM 4.5 Air |
| Context Window / Max Output | 128K In / 96K Out | 256K In / 128K Out | Pareto 26.9 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 4.5 Air (-0ms) |
GLM-4.5-Air is the lightweight variant of our latest flagship model family, also purpose-built for agent-centric applications. Like GLM-4.5, it adopts the Mixture-of-Experts (MoE)
Pareto is a multimodal composite model built for research, coding, and agentic workflows, while delivering frontier-level performance across a broad range of general-purpose tasks.
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 4.5 Air to Pareto 26.9
Pareto is a multimodal composite model built for research, coding, and agentic workflows, while delivering frontier-level performance across a broad range of general-purpose tasks. Verified P90 latency of 280ms.