Z.ai: GLM 4.6V vs MiniMax: MiniMax M2.5: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 4.6V (z-ai/glm-4.6v) vs MiniMax: MiniMax M2.5 (minimax/minimax-m2.5). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Z.ai: GLM 4.6V (z-ai/glm-4.6v) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.6V: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.15/1M) or route via asynchronous Batch API for 50% off standard token rates.
MiniMax: MiniMax M2.5 (minimax/minimax-m2.5) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for MiniMax: MiniMax M2.5: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.135/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Z.ai: GLM 4.6V (API ID: z-ai/glm-4.6v) vs MiniMax: MiniMax M2.5 (API ID: minimax/minimax-m2.5), MiniMax: MiniMax M2.5 offers lower standard input pricing at $0.27/1M tokens ($0.95/1M output), while MiniMax: MiniMax M2.5 leads in cached prefix economics at $0.135/1M tokens (50% discount). Z.ai: GLM 4.6V 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 4.6V | MiniMax: MiniMax M2.5 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-4.6v | minimax/minimax-m2.5 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.3 / $0.9 | $0.27 / $0.95 | MiniMax: MiniMax M2.5 |
| Cache Read Price (per 1M) | $0.15/1M (50% Off) | $0.135/1M (50% Off) | MiniMax: MiniMax M2.5 |
| Context Window / Max Output | 128K In / 32K Out | 200K In / 32K Out | Z.ai: GLM 4.6V |
| P90 First-Token / Edge Latency | 280ms | 280ms | Z.ai: GLM 4.6V (-0ms) |
GLM-4.6V is a large multimodal model designed for high-fidelity visual understanding and long-context reasoning across images, documents, and mixed media. It supports up to 128K to
MiniMax-M2.5 is a SOTA large language model designed for real-world productivity. Trained in a diverse range of complex real-world digital working environments, M2.5 builds upon th
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 4.6V to MiniMax: MiniMax M2.5
MiniMax-M2.5 is a SOTA large language model designed for real-world productivity. Trained in a diverse range of complex real-world digital working environments, M2.5 builds upon th Verified P90 latency of 280ms.