Sakana: Sakana Namazu vs PrismML: Ternary Bonsai 2 27B: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Sakana: Sakana Namazu (sakana/sakana-namazu) vs PrismML: Ternary Bonsai 2 27B (prism-ml/ternary-bonsai-2-27b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Sakana: Sakana Namazu (sakana/sakana-namazu) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Sakana: Sakana Namazu: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.475/1M) or route via asynchronous Batch API for 50% off standard token rates.
PrismML: Ternary Bonsai 2 27B (prism-ml/ternary-bonsai-2-27b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for PrismML: Ternary Bonsai 2 27B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.0375/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Sakana: Sakana Namazu (API ID: sakana/sakana-namazu) vs PrismML: Ternary Bonsai 2 27B (API ID: prism-ml/ternary-bonsai-2-27b), PrismML: Ternary Bonsai 2 27B offers lower standard input pricing at $0.075/1M tokens ($0.5/1M output), while PrismML: Ternary Bonsai 2 27B leads in cached prefix economics at $0.0375/1M tokens (50% discount). Sakana: Sakana Namazu 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: Sakana Namazu | PrismML: Ternary Bonsai 2 27B | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | sakana/sakana-namazu | prism-ml/ternary-bonsai-2-27b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.95 / $4 | $0.075 / $0.5 | PrismML: Ternary Bonsai 2 27B |
| Cache Read Price (per 1M) | $0.475/1M (50% Off) | $0.0375/1M (50% Off) | PrismML: Ternary Bonsai 2 27B |
| Context Window / Max Output | 256K In / 64K Out | 256K In / 32K Out | Sakana: Sakana Namazu |
| P90 First-Token / Edge Latency | 280ms | 280ms | Sakana: Sakana Namazu (-0ms) |
Sakana Namazu is a Japanese-specialized reasoning model from Sakana AI, based on Kimi K2.6 with additional training for Japanese language and business contexts. It is suited for Ja
Bonsai 2 27B is a 27B-parameter reasoning model from PrismML derived from Qwen3.8-27B. It supports coding, mathematics, tool calling, and image understanding with a 262K-token cont
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: Sakana Namazu to PrismML: Ternary Bonsai 2 27B
Bonsai 2 27B is a 27B-parameter reasoning model from PrismML derived from Qwen3.8-27B. It supports coding, mathematics, tool calling, and image understanding with a 262K-token cont Verified P90 latency of 280ms.