Z.ai: GLM 5.1 vs MoonshotAI: Kimi K2 0711: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 5.1 (z-ai/glm-5.1) vs MoonshotAI: Kimi K2 0711 (moonshotai/kimi-k2). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Z.ai: GLM 5.1 (z-ai/glm-5.1) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 5.1: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.483/1M) or route via asynchronous Batch API for 50% off standard token rates.
MoonshotAI: Kimi K2 0711 (moonshotai/kimi-k2) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for MoonshotAI: Kimi K2 0711: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.285/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Z.ai: GLM 5.1 (API ID: z-ai/glm-5.1) vs MoonshotAI: Kimi K2 0711 (API ID: moonshotai/kimi-k2), MoonshotAI: Kimi K2 0711 offers lower standard input pricing at $0.57/1M tokens ($2.3/1M output), while MoonshotAI: Kimi K2 0711 leads in cached prefix economics at $0.285/1M tokens (50% discount). Z.ai: GLM 5.1 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 | Z.ai: GLM 5.1 | MoonshotAI: Kimi K2 0711 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-5.1 | moonshotai/kimi-k2 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.966 / $3.036 | $0.57 / $2.3 | MoonshotAI: Kimi K2 0711 |
| Cache Read Price (per 1M) | $0.483/1M (50% Off) | $0.285/1M (50% Off) | MoonshotAI: Kimi K2 0711 |
| Context Window / Max Output | 200K In / 125K Out | 128K In / 96K Out | Z.ai: GLM 5.1 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 5.1 (-0ms) |
GLM-5.1 delivers a major leap in coding capability, with particularly significant gains in handling long-horizon tasks. Unlike previous models built around minute-level interaction
Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. I
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 Z.ai: GLM 5.1 to MoonshotAI: Kimi K2 0711
Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. I Verified P90 latency of 280ms.