Qwen: Qwen3 Coder Next vs Sakana: Fugu Ultra: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Qwen: Qwen3 Coder Next (qwen/qwen3-coder-next) vs Sakana: Fugu Ultra (sakana/fugu-ultra). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Qwen: Qwen3 Coder Next (qwen/qwen3-coder-next) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Qwen: Qwen3 Coder Next: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.06/1M) or route via asynchronous Batch API for 50% off standard token rates.
Sakana: Fugu Ultra (sakana/fugu-ultra) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Sakana: Fugu Ultra: 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.
In Etuki October 2026 FinOps telemetry comparing Qwen: Qwen3 Coder Next (API ID: qwen/qwen3-coder-next) vs Sakana: Fugu Ultra (API ID: sakana/fugu-ultra), Qwen: Qwen3 Coder Next offers lower standard input pricing at $0.12/1M tokens ($0.8/1M output), while Qwen: Qwen3 Coder Next leads in cached prefix economics at $0.06/1M tokens (50% discount). Qwen: Qwen3 Coder Next 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 Coder Next | Sakana: Fugu Ultra | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | qwen/qwen3-coder-next | sakana/fugu-ultra | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.12 / $0.8 | $5 / $30 | Qwen: Qwen3 Coder Next |
| Cache Read Price (per 1M) | $0.06/1M (50% Off) | $2.5/1M (50% Off) | Qwen: Qwen3 Coder Next |
| Context Window / Max Output | 256K In / 230K Out | 977K In / 125K Out | Qwen: Qwen3 Coder Next |
| P90 First-Token / Edge Latency | 280ms | 280ms | Qwen: Qwen3 Coder Next (-0ms) |
Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only
Fugu Ultra 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 model t
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 Coder Next to Sakana: Fugu Ultra
Fugu Ultra 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 model t Verified P90 latency of 280ms.