Meta: Llama 4 Scout vs Sao10K: Llama 3 8B Lunaris: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Meta: Llama 4 Scout (meta-llama/llama-4-scout) vs Sao10K: Llama 3 8B Lunaris (sao10k/l3-lunaris-8b). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Meta: Llama 4 Scout (meta-llama/llama-4-scout) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Llama 4 Scout: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.09/1M) or route via asynchronous Batch API for 50% off standard token rates.
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.
In Etuki October 2026 FinOps telemetry comparing Meta: Llama 4 Scout (API ID: meta-llama/llama-4-scout) vs Sao10K: Llama 3 8B Lunaris (API ID: sao10k/l3-lunaris-8b), 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). Meta: Llama 4 Scout 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 | Meta: Llama 4 Scout | Sao10K: Llama 3 8B Lunaris | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | meta-llama/llama-4-scout | sao10k/l3-lunaris-8b | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.18 / $0.59 | $0.04 / $0.05 | Sao10K: Llama 3 8B Lunaris |
| Cache Read Price (per 1M) | $0.09/1M (50% Off) | $0.02/1M (50% Off) | Sao10K: Llama 3 8B Lunaris |
| Context Window / Max Output | 1280K In / 115K Out | 8K In / 7K Out | Meta: Llama 4 Scout |
| P90 First-Token / Edge Latency | 280ms | 280ms | Meta: Llama 4 Scout (-0ms) |
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimo
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
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 Meta: Llama 4 Scout to Sao10K: Llama 3 8B Lunaris
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 Verified P90 latency of 280ms.