DeepSeek-R1 (Reasoner) vs Sakana: Sakana Namazu: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek-R1 (Reasoner) (deepseek-reasoner) vs Sakana: Sakana Namazu (sakana/sakana-namazu). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek-R1 (Reasoner) (deepseek-reasoner) — Automatic Disk KV Cache ($0.275/1M) & Off-Peak Promo
DeepSeek-V4 Pro (1.6T MoE) automatically caches repeated 64-token prompt prefixes on NVMe storage at $0.07/1M input tokens (50% off $0.14 base) with zero code changes required, plus off-peak UTC scheduling discounts.
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.
In Etuki October 2026 FinOps telemetry comparing DeepSeek-R1 (Reasoner) (API ID: deepseek-reasoner) vs Sakana: Sakana Namazu (API ID: sakana/sakana-namazu), DeepSeek-R1 (Reasoner) offers lower standard input pricing at $0.55/1M tokens ($2.19/1M output), while DeepSeek-R1 (Reasoner) leads in cached prefix economics at $0.275/1M tokens (50% discount). Sakana: Sakana Namazu achieves a 260ms faster P90 time-to-first-token latency (280ms vs 540ms).
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
Auto-discovered via live global telemetry registry.
| Specification / FinOps Dimension | DeepSeek-R1 (Reasoner) | Sakana: Sakana Namazu | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek-reasoner | sakana/sakana-namazu | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.55 / $2.19 | $0.95 / $4 | DeepSeek-R1 (Reasoner) |
| Cache Read Price (per 1M) | $0.275/1M (50% Off) | $0.475/1M (50% Off) | DeepSeek-R1 (Reasoner) |
| Context Window / Max Output | 400K In / 128K Out | 256K In / 64K Out | DeepSeek-R1 (Reasoner) |
| P90 First-Token / Edge Latency | 540ms | 280ms | Sakana: Sakana Namazu (-260ms) |
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis.
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
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 DeepSeek-R1 (Reasoner) to Sakana: Sakana Namazu
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 Verified P90 latency of 280ms.