Z.ai: GLM 4.6V vs Sakana: Fugu Max: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Z.ai: GLM 4.6V (z-ai/glm-4.6v) vs Sakana: Fugu Max (sakana/fugu-max). 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.
Sakana: Fugu Max (sakana/fugu-max) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Sakana: Fugu Max: Activate ephemeral prefix caching to slash input costs by 50% (down to $1/1M) 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 Sakana: Fugu Max (API ID: sakana/fugu-max), Z.ai: GLM 4.6V offers lower standard input pricing at $0.3/1M tokens ($0.9/1M output), while Z.ai: GLM 4.6V leads in cached prefix economics at $0.15/1M 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 | Sakana: Fugu Max | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | z-ai/glm-4.6v | sakana/fugu-max | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.3 / $0.9 | $2 / $6 | Z.ai: GLM 4.6V |
| Cache Read Price (per 1M) | $0.15/1M (50% Off) | $1/1M (50% Off) | Z.ai: GLM 4.6V |
| Context Window / Max Output | 128K In / 32K Out | 977K In / 125K Out | Sakana: Fugu Max |
| 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
Fugu Max is the cost-performance model in Sakana AI's Fugu family. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system: a language model train
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 Sakana: Fugu Max
Fugu Max is the cost-performance model in Sakana AI's Fugu family. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system: a language model train Verified P90 latency of 280ms.