Mancer: Weaver (alpha) vs Z.ai: GLM 4.5V: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Mancer: Weaver (alpha) (mancer/weaver) vs Z.ai: GLM 4.5V (z-ai/glm-4.5v). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Mancer: Weaver (alpha) (mancer/weaver) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Mancer: Weaver (alpha): Activate ephemeral prefix caching to slash input costs by 50% (down to $0.2/1M) or route via asynchronous Batch API for 50% off standard token rates.
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.
In Etuki October 2026 FinOps telemetry comparing Mancer: Weaver (alpha) (API ID: mancer/weaver) vs Z.ai: GLM 4.5V (API ID: z-ai/glm-4.5v), Mancer: Weaver (alpha) offers lower standard input pricing at $0.4/1M tokens ($0.75/1M output), while Mancer: Weaver (alpha) leads in cached prefix economics at $0.2/1M tokens (50% discount). Mancer: Weaver (alpha) 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 | Mancer: Weaver (alpha) | Z.ai: GLM 4.5V | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | mancer/weaver | z-ai/glm-4.5v | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.4 / $0.75 | $0.6 / $1.8 | Mancer: Weaver (alpha) |
| Cache Read Price (per 1M) | $0.2/1M (50% Off) | $0.3/1M (50% Off) | Mancer: Weaver (alpha) |
| Context Window / Max Output | 8K In / 6K Out | 64K In / 16K Out | Z.ai: GLM 4.5V |
| P90 First-Token / Edge Latency | 280ms | 280ms | Mancer: Weaver (alpha) (-0ms) |
An attempt to recreate Claude-style verbosity, but don't expect the same level of coherence or memory. Meant for use in roleplay/narrative situations.
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
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 Mancer: Weaver (alpha) to Z.ai: GLM 4.5V
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 Verified P90 latency of 280ms.