SpaceXAI: Grok 4.3 (batch) vs Z.ai: GLM 4.5: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing SpaceXAI: Grok 4.3 (batch) (x-ai/grok-4.3:batch) vs Z.ai: GLM 4.5 (z-ai/glm-4.5). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
SpaceXAI: Grok 4.3 (batch) (x-ai/grok-4.3:batch) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for SpaceXAI: Grok 4.3 (batch): Activate ephemeral prefix caching to slash input costs by 50% (down to $0.5/1M) 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 SpaceXAI: Grok 4.3 (batch) (API ID: x-ai/grok-4.3:batch) vs Z.ai: GLM 4.5 (API ID: z-ai/glm-4.5), Z.ai: GLM 4.5 offers lower standard input pricing at $0.6/1M tokens ($2.2/1M output), while Z.ai: GLM 4.5 leads in cached prefix economics at $0.3/1M tokens (50% discount). SpaceXAI: Grok 4.3 (batch) 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 4.3 (batch) | Z.ai: GLM 4.5 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | x-ai/grok-4.3:batch | z-ai/glm-4.5 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $1 / $2 | $0.6 / $2.2 | Z.ai: GLM 4.5 |
| Cache Read Price (per 1M) | $0.5/1M (50% Off) | $0.3/1M (50% Off) | Z.ai: GLM 4.5 |
| Context Window / Max Output | 977K In / 879K Out | 128K In / 96K Out | SpaceXAI: Grok 4.3 (batch) |
| P90 First-Token / Edge Latency | 280ms | 280ms | SpaceXAI: Grok 4.3 (batch) (-0ms) |
Grok 4.3 is a reasoning model from SpaceXAI. It accepts text and image inputs with text output, and is suited for agentic workflows, instruction-following tasks, and applications r
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 SpaceXAI: Grok 4.3 (batch) 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.