Sakana: Fugu Max vs Thinking Machines: Inkling: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Sakana: Fugu Max (sakana/fugu-max) vs Thinking Machines: Inkling (thinkingmachines/inkling). 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.
Thinking Machines: Inkling (thinkingmachines/inkling) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Thinking Machines: Inkling: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.5/1M) 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 Thinking Machines: Inkling (API ID: thinkingmachines/inkling), Thinking Machines: Inkling offers lower standard input pricing at $1/1M tokens ($4.05/1M output), while Thinking Machines: Inkling leads in cached prefix economics at $0.5/1M 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 | Thinking Machines: Inkling | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | sakana/fugu-max | thinkingmachines/inkling | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $2 / $6 | $1 / $4.05 | Thinking Machines: Inkling |
| Cache Read Price (per 1M) | $1/1M (50% Off) | $0.5/1M (50% Off) | Thinking Machines: Inkling |
| Context Window / Max Output | 977K In / 125K Out | 512K In / 461K Out | Thinking Machines: Inkling |
| 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
Inkling is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 41B active parameters out of 975B total. It is designed for general-purpose reasoning
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 Thinking Machines: Inkling
Inkling is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 41B active parameters out of 975B total. It is designed for general-purpose reasoning Verified P90 latency of 280ms.