IBM: Granite 4.2 8B vs Sakana: Sakana Namazu: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing IBM: Granite 4.2 8B (ibm-granite/granite-4.2-8b) vs Sakana: Sakana Namazu (sakana/sakana-namazu). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
IBM: Granite 4.2 8B (ibm-granite/granite-4.2-8b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for IBM: Granite 4.2 8B: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.03/1M) 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 IBM: Granite 4.2 8B (API ID: ibm-granite/granite-4.2-8b) vs Sakana: Sakana Namazu (API ID: sakana/sakana-namazu), IBM: Granite 4.2 8B offers lower standard input pricing at $0.06/1M tokens ($0.25/1M output), while IBM: Granite 4.2 8B leads in cached prefix economics at $0.03/1M tokens (50% discount). IBM: Granite 4.2 8B 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 | IBM: Granite 4.2 8B | Sakana: Sakana Namazu | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | ibm-granite/granite-4.2-8b | sakana/sakana-namazu | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.06 / $0.25 | $0.95 / $4 | IBM: Granite 4.2 8B |
| Cache Read Price (per 1M) | $0.03/1M (50% Off) | $0.475/1M (50% Off) | IBM: Granite 4.2 8B |
| Context Window / Max Output | 128K In / 115K Out | 256K In / 64K Out | IBM: Granite 4.2 8B |
| P90 First-Token / Edge Latency | 280ms | 280ms | IBM: Granite 4.2 8B (-0ms) |
Granite 4.2 8B is a dense reasoning model from IBM. It is suited for mathematics, code generation, multilingual dialogue, and agentic workflows that need multi-step reasoning. It s
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 IBM: Granite 4.2 8B 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.