Sakana: Fugu Max vs Microsoft: Phi 4: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Sakana: Fugu Max (sakana/fugu-max) vs Microsoft: Phi 4 (microsoft/phi-4). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
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.
Microsoft: Phi 4 (microsoft/phi-4) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Microsoft: Phi 4: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.035/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Sakana: Fugu Max (API ID: sakana/fugu-max) vs Microsoft: Phi 4 (API ID: microsoft/phi-4), Microsoft: Phi 4 offers lower standard input pricing at $0.07/1M tokens ($0.14/1M output), while Microsoft: Phi 4 leads in cached prefix economics at $0.035/1M tokens (50% discount). Sakana: Fugu Max 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 Max | Microsoft: Phi 4 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | sakana/fugu-max | microsoft/phi-4 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $2 / $6 | $0.07 / $0.14 | Microsoft: Phi 4 |
| Cache Read Price (per 1M) | $1/1M (50% Off) | $0.035/1M (50% Off) | Microsoft: Phi 4 |
| Context Window / Max Output | 977K In / 125K Out | 16K In / 14K Out | Sakana: Fugu Max |
| P90 First-Token / Edge Latency | 280ms | 280ms | Sakana: Fugu Max (-0ms) |
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
[Microsoft Research](/microsoft) Phi-4 is designed to perform well in complex reasoning tasks and can operate efficiently in situations with limited memory or where quick responses
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 Max to Microsoft: Phi 4
[Microsoft Research](/microsoft) Phi-4 is designed to perform well in complex reasoning tasks and can operate efficiently in situations with limited memory or where quick responses Verified P90 latency of 280ms.