Qwen: Qwen3.5 Plus 2026-02-15 vs Sakana: Fugu Max: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3.5 Plus 2026-02-15 (qwen/qwen3.5-plus-02-15) vs Sakana: Fugu Max (sakana/fugu-max). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen3.5 Plus 2026-02-15 (qwen/qwen3.5-plus-02-15) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3.5 Plus 2026-02-15: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.13/1M) 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 Qwen: Qwen3.5 Plus 2026-02-15 (API ID: qwen/qwen3.5-plus-02-15) vs Sakana: Fugu Max (API ID: sakana/fugu-max), Qwen: Qwen3.5 Plus 2026-02-15 offers lower standard input pricing at $0.26/1M tokens ($1.56/1M output), while Qwen: Qwen3.5 Plus 2026-02-15 leads in cached prefix economics at $0.13/1M tokens (50% discount). Qwen: Qwen3.5 Plus 2026-02-15 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 | Qwen: Qwen3.5 Plus 2026-02-15 | Sakana: Fugu Max | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3.5-plus-02-15 | sakana/fugu-max | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.26 / $1.56 | $2 / $6 | Qwen: Qwen3.5 Plus 2026-02-15 |
| Cache Read Price (per 1M) | $0.13/1M (50% Off) | $1/1M (50% Off) | Qwen: Qwen3.5 Plus 2026-02-15 |
| Context Window / Max Output | 977K In / 64K Out | 977K In / 125K Out | Sakana: Fugu Max |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3.5 Plus 2026-02-15 (-0ms) |
The Qwen3.5 native vision-language series Plus models are built on a hybrid architecture that integrates linear attention mechanisms with sparse mixture-of-experts models, achievin
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 Qwen: Qwen3.5 Plus 2026-02-15 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.