SpaceXAI: Grok 4.20 vs Z.ai: GLM 4.5V: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing SpaceXAI: Grok 4.20 (x-ai/grok-4.20) vs Z.ai: GLM 4.5V (z-ai/glm-4.5v). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
SpaceXAI: Grok 4.20 (x-ai/grok-4.20) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for SpaceXAI: Grok 4.20: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.625/1M) 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 4.20 (API ID: x-ai/grok-4.20) 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 4.20 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 4.20 | Z.ai: GLM 4.5V | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | x-ai/grok-4.20 | z-ai/glm-4.5v | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $1.25 / $2.5 | $0.6 / $1.8 | Z.ai: GLM 4.5V |
| Cache Read Price (per 1M) | $0.625/1M (50% Off) | $0.3/1M (50% Off) | Z.ai: GLM 4.5V |
| Context Window / Max Output | 1953K In / 1758K Out | 64K In / 16K Out | SpaceXAI: Grok 4.20 |
| P90 First-Token / Edge Latency | 280ms | 280ms | SpaceXAI: Grok 4.20 (-0ms) |
Grok 4.20 is a reasoning model from SpaceXAI with industry-leading speed and agentic tool calling capabilities. It combines the lowest hallucination rate on the market with strict
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 4.20 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.