Sakana: Sakana Namazu vs Google: Gemini 3.1 Pro Preview (batch): Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Sakana: Sakana Namazu (sakana/sakana-namazu) vs Google: Gemini 3.1 Pro Preview (batch) (google/gemini-3.1-pro-preview:batch). 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.
Google: Gemini 3.1 Pro Preview (batch) (google/gemini-3.1-pro-preview:batch) — Google AI Startup Credits ($350K) & 50% Cache Promo
Google DeepMind is currently subsidizing Gemini 4 Argon at 50% off standard rates ($2.00/1M input & $10.00/1M output instead of $4/$20) until November 30, 2026, alongside 95% off Context Caching ($0.10/1M) and up to $350,000 in Google for Startups Cloud/Vertex AI credits.
In Etuki October 2026 FinOps telemetry comparing Sakana: Sakana Namazu (API ID: sakana/sakana-namazu) vs Google: Gemini 3.1 Pro Preview (batch) (API ID: google/gemini-3.1-pro-preview:batch), Sakana: Sakana Namazu offers lower standard input pricing at $0.95/1M tokens ($4/1M output), while Sakana: Sakana Namazu leads in cached prefix economics at $0.475/1M 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 | Google: Gemini 3.1 Pro Preview (batch) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | sakana/sakana-namazu | google/gemini-3.1-pro-preview:batch | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.95 / $4 | $1 / $6 | Sakana: Sakana Namazu |
| Cache Read Price (per 1M) | $0.475/1M (50% Off) | $0.5/1M (50% Off) | Sakana: Sakana Namazu |
| Context Window / Max Output | 256K In / 64K Out | 1024K In / 64K 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
Gemini 3.1 Pro Preview is Google’s frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage acro
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 Google: Gemini 3.1 Pro Preview (batch)
Gemini 3.1 Pro Preview is Google’s frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage acro Verified P90 latency of 280ms.