DeepSeek: DeepSeek V4.1 Flash (batch) vs Meta: Llama 4 Scout: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek: DeepSeek V4.1 Flash (batch) (deepseek/deepseek-v4.1-flash:batch) vs Meta: Llama 4 Scout (meta-llama/llama-4-scout). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek: DeepSeek V4.1 Flash (batch) (deepseek/deepseek-v4.1-flash:batch) — Automatic Disk KV Cache ($0.056/1M) & 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.
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.
In Etuki October 2026 FinOps telemetry comparing DeepSeek: DeepSeek V4.1 Flash (batch) (API ID: deepseek/deepseek-v4.1-flash:batch) vs Meta: Llama 4 Scout (API ID: meta-llama/llama-4-scout), DeepSeek: DeepSeek V4.1 Flash (batch) offers lower standard input pricing at $0.112/1M tokens ($0.336/1M output), while DeepSeek: DeepSeek V4.1 Flash (batch) leads in cached prefix economics at $0.056/1M tokens (50% discount). DeepSeek: DeepSeek V4.1 Flash (batch) 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 | DeepSeek: DeepSeek V4.1 Flash (batch) | Meta: Llama 4 Scout | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-v4.1-flash:batch | meta-llama/llama-4-scout | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.112 / $0.336 | $0.18 / $0.59 | DeepSeek: DeepSeek V4.1 Flash (batch) |
| Cache Read Price (per 1M) | $0.056/1M (50% Off) | $0.09/1M (50% Off) | DeepSeek: DeepSeek V4.1 Flash (batch) |
| Context Window / Max Output | 1024K In / 128K Out | 1280K In / 115K Out | DeepSeek: DeepSeek V4.1 Flash (batch) |
| P90 First-Token / Edge Latency | 280ms | 280ms | DeepSeek: DeepSeek V4.1 Flash (batch) (-0ms) |
DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters o
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
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 V4.1 Flash (batch) to Meta: Llama 4 Scout
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 Verified P90 latency of 280ms.