Z.ai: GLM 4.7 vs Z.ai: GLM 4.6V: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 4.7 (z-ai/glm-4.7) vs Z.ai: GLM 4.6V (z-ai/glm-4.6v). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Z.ai: GLM 4.7 (z-ai/glm-4.7) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.7: 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 4.6V (z-ai/glm-4.6v) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.6V: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.15/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Z.ai: GLM 4.7 (API ID: z-ai/glm-4.7) vs Z.ai: GLM 4.6V (API ID: z-ai/glm-4.6v), Z.ai: GLM 4.6V offers lower standard input pricing at $0.3/1M tokens ($0.9/1M output), while Z.ai: GLM 4.6V leads in cached prefix economics at $0.15/1M tokens (50% discount). Z.ai: GLM 4.7 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 4.7 | Z.ai: GLM 4.6V | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-4.7 | z-ai/glm-4.6v | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.6 / $2.2 | $0.3 / $0.9 | Z.ai: GLM 4.6V |
| Cache Read Price (per 1M) | $0.3/1M (50% Off) | $0.15/1M (50% Off) | Z.ai: GLM 4.6V |
| Context Window / Max Output | 200K In / 128K Out | 128K In / 32K Out | Z.ai: GLM 4.7 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 4.7 (-0ms) |
GLM-4.7 is Z.ai’s latest flagship model, featuring upgrades in two key areas: enhanced programming capabilities and more stable multi-step reasoning/execution. It demonstrates sign
GLM-4.6V is a large multimodal model designed for high-fidelity visual understanding and long-context reasoning across images, documents, and mixed media. It supports up to 128K to
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 4.7 to Z.ai: GLM 4.6V
GLM-4.6V is a large multimodal model designed for high-fidelity visual understanding and long-context reasoning across images, documents, and mixed media. It supports up to 128K to Verified P90 latency of 280ms.