Sao10K: Llama 3 8B Lunaris vs Microsoft: Phi 4: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Sao10K: Llama 3 8B Lunaris (sao10k/l3-lunaris-8b) vs Microsoft: Phi 4 (microsoft/phi-4). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Sao10K: Llama 3 8B Lunaris (sao10k/l3-lunaris-8b) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Sao10K: Llama 3 8B Lunaris: 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.
Microsoft: Phi 4 (microsoft/phi-4) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Microsoft: Phi 4: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.035/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing Sao10K: Llama 3 8B Lunaris (API ID: sao10k/l3-lunaris-8b) vs Microsoft: Phi 4 (API ID: microsoft/phi-4), Sao10K: Llama 3 8B Lunaris offers lower standard input pricing at $0.04/1M tokens ($0.05/1M output), while Sao10K: Llama 3 8B Lunaris leads in cached prefix economics at $0.02/1M tokens (50% discount). Sao10K: Llama 3 8B Lunaris 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 | Sao10K: Llama 3 8B Lunaris | Microsoft: Phi 4 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | sao10k/l3-lunaris-8b | microsoft/phi-4 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.04 / $0.05 | $0.07 / $0.14 | Sao10K: Llama 3 8B Lunaris |
| Cache Read Price (per 1M) | $0.02/1M (50% Off) | $0.035/1M (50% Off) | Sao10K: Llama 3 8B Lunaris |
| Context Window / Max Output | 8K In / 7K Out | 16K In / 14K Out | Microsoft: Phi 4 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Sao10K: Llama 3 8B Lunaris (-0ms) |
Lunaris 8B is a versatile generalist and roleplaying model based on Llama 3. It's a strategic merge of multiple models, designed to balance creativity with improved logic and gener
[Microsoft Research](/microsoft) Phi-4 is designed to perform well in complex reasoning tasks and can operate efficiently in situations with limited memory or where quick responses
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 Sao10K: Llama 3 8B Lunaris to Microsoft: Phi 4
[Microsoft Research](/microsoft) Phi-4 is designed to perform well in complex reasoning tasks and can operate efficiently in situations with limited memory or where quick responses Verified P90 latency of 280ms.