Sakana: Fugu Ultra v2 vs Z.ai: GLM 4.7 Flash: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Sakana: Fugu Ultra v2 (sakana/fugu-ultra-v2) vs Z.ai: GLM 4.7 Flash (z-ai/glm-4.7-flash). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Sakana: Fugu Ultra v2 (sakana/fugu-ultra-v2) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Sakana: Fugu Ultra v2: Activate ephemeral prefix caching to slash input costs by 50% (down to $2.5/1M) or route via asynchronous Batch API for 50% off standard token rates.
Z.ai: GLM 4.7 Flash (z-ai/glm-4.7-flash) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Z.ai: GLM 4.7 Flash: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.0302/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Sakana: Fugu Ultra v2 (API ID: sakana/fugu-ultra-v2) vs Z.ai: GLM 4.7 Flash (API ID: z-ai/glm-4.7-flash), Z.ai: GLM 4.7 Flash offers lower standard input pricing at $0.0605/1M tokens ($0.4/1M output), while Z.ai: GLM 4.7 Flash leads in cached prefix economics at $0.0302/1M tokens (50% discount). Sakana: Fugu Ultra v2 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 | Sakana: Fugu Ultra v2 | Z.ai: GLM 4.7 Flash | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | sakana/fugu-ultra-v2 | z-ai/glm-4.7-flash | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $5 / $30 | $0.0605 / $0.4 | Z.ai: GLM 4.7 Flash |
| Cache Read Price (per 1M) | $2.5/1M (50% Off) | $0.0302/1M (50% Off) | Z.ai: GLM 4.7 Flash |
| Context Window / Max Output | 977K In / 125K Out | 195K In / 115K Out | Sakana: Fugu Ultra v2 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Sakana: Fugu Ultra v2 (-0ms) |
Fugu Ultra v2 is the higher-performance model in Sakana AI's Fugu family. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system: a language mode
As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding ca
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 Sakana: Fugu Ultra v2 to Z.ai: GLM 4.7 Flash
As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding ca Verified P90 latency of 280ms.