DeepSeek Deepseek R1 Zero vs Meta Llama 4 Scout: Live Promos, Pricing, Hidden Fees & Latency
DeepSeek Deepseek R1 Zero (deepseek/deepseek-r1-zero) — Automatic Disk KV Cache (0.05/1M) & Off-Peak Promo
DeepSeek-V4 Pro (1.6T MoE) automatically caches repeated 64-token prompt prefixes on NVMe storage at $0.022/1M off-peak input tokens (vs $0.66 off-peak base) with zero code changes required, plus off-peak UTC scheduling discounts.
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 80% (down to 0.022/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing DeepSeek Deepseek R1 Zero (API ID: deepseek/deepseek-r1-zero) vs Meta Llama 4 Scout (API ID: llama-4-scout), Meta Llama 4 Scout offers lower standard input pricing at $0.11/1M tokens ($0.34/1M output), while Meta Llama 4 Scout leads in cached prefix economics at $0.022/1M tokens (80% discount). Meta Llama 4 Scout achieves a 55ms faster P90 time-to-first-token latency (140ms vs 195ms).
Gateway-synced rate ($0.5/$2 per 1M, flat 128K window). Verify upstream provider cache TTL and regional routing before high-volume batch runs.
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
| Specification / FinOps Dimension | DeepSeek Deepseek R1 Zero | Meta Llama 4 Scout | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-r1-zero | llama-4-scout | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.5 / $2 | $0.11 / $0.34 | Meta Llama 4 Scout |
| Cache Read Price (per 1M) | $0.05/1M (90% Off) | $0.022/1M (80% Off) | Meta Llama 4 Scout |
| Context Window / Max Output | 128K In / 64K Out | 10000K In / 128K Out | Meta Llama 4 Scout |
| P90 First-Token / Edge Latency | 195ms | 140ms | Meta Llama 4 Scout (-55ms) |
DeepSeek Deepseek R1 Zero (deepseek/deepseek-r1-zero): OpenRouter gateway-synced model at $0.5/1M input, $2/1M output, $0.05/1M cached input (128K context window).
Meta Llama 4 Scout 109B open-weight MoE model ($0.11/$0.34 per 1M, $0.022/1M cache read [80% off], 10M token context window).
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 DeepSeek Deepseek R1 Zero to Meta Llama 4 Scout
Meta Llama 4 Scout 109B open-weight MoE model ($0.11/$0.34 per 1M, $0.022/1M cache read [80% off], 10M token context window). Verified P90 latency of 140ms.