SpaceXAI: Grok 4.5 vs Z.ai: GLM 4.5V: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing SpaceXAI: Grok 4.5 (x-ai/grok-4.5) vs Z.ai: GLM 4.5V (z-ai/glm-4.5v). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
SpaceXAI: Grok 4.5 (x-ai/grok-4.5) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for SpaceXAI: Grok 4.5: Activate ephemeral prefix caching to slash input costs by 50% (down to $1/1M) 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 SpaceXAI: Grok 4.5 (API ID: x-ai/grok-4.5) vs Z.ai: GLM 4.5V (API ID: z-ai/glm-4.5v), 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). SpaceXAI: Grok 4.5 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 | SpaceXAI: Grok 4.5 | Z.ai: GLM 4.5V | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | x-ai/grok-4.5 | z-ai/glm-4.5v | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $2 / $6 | $0.6 / $1.8 | Z.ai: GLM 4.5V |
| Cache Read Price (per 1M) | $1/1M (50% Off) | $0.3/1M (50% Off) | Z.ai: GLM 4.5V |
| Context Window / Max Output | 488K In / 439K Out | 64K In / 16K Out | SpaceXAI: Grok 4.5 |
| P90 First-Token / Edge Latency | 280ms | 280ms | SpaceXAI: Grok 4.5 (-0ms) |
Grok 4.5 is a model from SpaceXAI with frontier performance on coding, knowledge work, and STEM.
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 SpaceXAI: Grok 4.5 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.