ReMM SLERP 13B vs Sakana: Sakana Namazu: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing ReMM SLERP 13B (undi95/remm-slerp-l2-13b) vs Sakana: Sakana Namazu (sakana/sakana-namazu). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
ReMM SLERP 13B (undi95/remm-slerp-l2-13b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for ReMM SLERP 13B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.175/1M) or route via asynchronous Batch API for 50% off standard token rates.
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 ReMM SLERP 13B (API ID: undi95/remm-slerp-l2-13b) vs Sakana: Sakana Namazu (API ID: sakana/sakana-namazu), ReMM SLERP 13B offers lower standard input pricing at $0.35/1M tokens ($0.65/1M output), while ReMM SLERP 13B leads in cached prefix economics at $0.175/1M tokens (50% discount). ReMM SLERP 13B 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 | ReMM SLERP 13B | Sakana: Sakana Namazu | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | undi95/remm-slerp-l2-13b | sakana/sakana-namazu | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.35 / $0.65 | $0.95 / $4 | ReMM SLERP 13B |
| Cache Read Price (per 1M) | $0.175/1M (50% Off) | $0.475/1M (50% Off) | ReMM SLERP 13B |
| Context Window / Max Output | 6K In / 5K Out | 256K In / 64K Out | Sakana: Sakana Namazu |
| P90 First-Token / Edge Latency | 280ms | 280ms | ReMM SLERP 13B (-0ms) |
A recreation trial of the original MythoMax-L2-B13 but with updated models. #merge
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 ReMM SLERP 13B 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.