Z.ai: GLM 4.5V vs Z.ai: GLM 4.5: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 4.5V (z-ai/glm-4.5v) vs Z.ai: GLM 4.5 (z-ai/glm-4.5). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
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.
Z.ai: GLM 4.5 (z-ai/glm-4.5) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.5: 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 Z.ai: GLM 4.5V (API ID: z-ai/glm-4.5v) vs Z.ai: GLM 4.5 (API ID: z-ai/glm-4.5), Z.ai: GLM 4.5V offers lower standard input pricing at $0.6/1M tokens ($1.8/1M output), while Z.ai: GLM 4.5V leads in cached prefix economics at $0.3/1M tokens (50% discount). Z.ai: GLM 4.5V 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 4.5V | Z.ai: GLM 4.5 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-4.5v | z-ai/glm-4.5 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.6 / $1.8 | $0.6 / $2.2 | Z.ai: GLM 4.5V |
| Cache Read Price (per 1M) | $0.3/1M (50% Off) | $0.3/1M (50% Off) | Z.ai: GLM 4.5V |
| Context Window / Max Output | 64K In / 16K Out | 128K In / 96K Out | Z.ai: GLM 4.5 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 4.5V (-0ms) |
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
GLM-4.5 is our latest flagship foundation model, purpose-built for agent-based applications. It leverages a Mixture-of-Experts (MoE) architecture and supports a context length of u
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 4.5V to Z.ai: GLM 4.5
GLM-4.5 is our latest flagship foundation model, purpose-built for agent-based applications. It leverages a Mixture-of-Experts (MoE) architecture and supports a context length of u Verified P90 latency of 280ms.