Meta Llama 4 Scout vs DeepSeek: R1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Meta Llama 4 Scout (llama-4-scout) vs DeepSeek: R1 (deepseek/deepseek-r1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Meta Llama 4 Scout (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 $0.225/1M) or route via asynchronous Batch API for 50% off standard token rates.
DeepSeek: R1 (deepseek/deepseek-r1) — Automatic Disk KV Cache ($0.35/1M) & Off-Peak Promo
DeepSeek-V4 Pro (1.6T MoE) automatically caches repeated 64-token prompt prefixes on NVMe storage at $0.07/1M input tokens (50% off $0.14 base) with zero code changes required, plus off-peak UTC scheduling discounts.
In Etuki October 2026 FinOps telemetry comparing Meta Llama 4 Scout (API ID: llama-4-scout) vs DeepSeek: R1 (API ID: deepseek/deepseek-r1), Meta Llama 4 Scout offers lower standard input pricing at $0.45/1M tokens ($0.65/1M output), while Meta Llama 4 Scout leads in cached prefix economics at $0.225/1M tokens (50% discount). Meta Llama 4 Scout achieves a 140ms faster P90 time-to-first-token latency (140ms vs 280ms).
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
Auto-discovered via live global telemetry registry.
| Specification / FinOps Dimension | Meta Llama 4 Scout | DeepSeek: R1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | llama-4-scout | deepseek/deepseek-r1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.45 / $0.65 | $0.7 / $2.5 | Meta Llama 4 Scout |
| Cache Read Price (per 1M) | $0.225/1M (50% Off) | $0.35/1M (50% Off) | Meta Llama 4 Scout |
| Context Window / Max Output | 400K In / 128K Out | 63K In / 16K Out | Meta Llama 4 Scout |
| P90 First-Token / Edge Latency | 140ms | 280ms | Meta Llama 4 Scout (-140ms) |
Meta open-weight multimodal MoE model supporting 10M+ extended context on dedicated clusters.
DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inf
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 DeepSeek: R1
DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inf Verified P90 latency of 280ms.