MoonshotAI: Kimi K3 vs Z.ai: GLM 4.5V: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing MoonshotAI: Kimi K3 (moonshotai/kimi-k3) vs Z.ai: GLM 4.5V (z-ai/glm-4.5v). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
MoonshotAI: Kimi K3 (moonshotai/kimi-k3) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for MoonshotAI: Kimi K3: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.225/1M) 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 MoonshotAI: Kimi K3 (API ID: moonshotai/kimi-k3) vs Z.ai: GLM 4.5V (API ID: z-ai/glm-4.5v), MoonshotAI: Kimi K3 offers lower standard input pricing at $0.45/1M tokens ($15/1M output), while MoonshotAI: Kimi K3 leads in cached prefix economics at $0.225/1M tokens (50% discount). MoonshotAI: Kimi K3 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 | MoonshotAI: Kimi K3 | Z.ai: GLM 4.5V | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | moonshotai/kimi-k3 | z-ai/glm-4.5v | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.45 / $15 | $0.6 / $1.8 | MoonshotAI: Kimi K3 |
| Cache Read Price (per 1M) | $0.225/1M (50% Off) | $0.3/1M (50% Off) | MoonshotAI: Kimi K3 |
| Context Window / Max Output | 1024K In / 922K Out | 64K In / 16K Out | MoonshotAI: Kimi K3 |
| P90 First-Token / Edge Latency | 280ms | 280ms | MoonshotAI: Kimi K3 (-0ms) |
Kimi K3 is a 2.8T parameter open-weight multimodal reasoning model from Moonshot AI. It is suited for complex coding, knowledge work, and long-horizon agentic workflows, and is par
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 MoonshotAI: Kimi K3 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.