Sakana: Sakana Namazu vs ScaleDown: Summarize: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Sakana: Sakana Namazu (sakana/sakana-namazu) vs ScaleDown: Summarize (scaledown/summarize). 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.
ScaleDown: Summarize (scaledown/summarize) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for ScaleDown: Summarize: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.02/1M) 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 ScaleDown: Summarize (API ID: scaledown/summarize), ScaleDown: Summarize offers lower standard input pricing at $0.04/1M tokens ($0/1M output), while ScaleDown: Summarize leads in cached prefix economics at $0.02/1M 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 | ScaleDown: Summarize | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | sakana/sakana-namazu | scaledown/summarize | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.95 / $4 | $0.04 / $0 | ScaleDown: Summarize |
| Cache Read Price (per 1M) | $0.475/1M (50% Off) | $0.02/1M (50% Off) | ScaleDown: Summarize |
| Context Window / Max Output | 256K In / 64K Out | 977K In / 4K 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
Summarize is an abstractive summarization model from ScaleDown. It condenses long text and documents into a fluent, faithful rewrite rather than lifting sentences from the source,
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 ScaleDown: Summarize
Summarize is an abstractive summarization model from ScaleDown. It condenses long text and documents into a fluent, faithful rewrite rather than lifting sentences from the source, Verified P90 latency of 280ms.