Meta: Llama 4 Maverick vs DeepSeek: DeepSeek V4.1 Flash: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Meta: Llama 4 Maverick (meta-llama/llama-4-maverick) vs DeepSeek: DeepSeek V4.1 Flash (deepseek/deepseek-v4.1-flash). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Meta: Llama 4 Maverick (meta-llama/llama-4-maverick) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Meta: Llama 4 Maverick: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.0938/1M) or route via asynchronous Batch API for 50% off standard token rates.
DeepSeek: DeepSeek V4.1 Flash (deepseek/deepseek-v4.1-flash) — Automatic Disk KV Cache ($0.0108/1M) & 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 Maverick (API ID: meta-llama/llama-4-maverick) vs DeepSeek: DeepSeek V4.1 Flash (API ID: deepseek/deepseek-v4.1-flash), DeepSeek: DeepSeek V4.1 Flash offers lower standard input pricing at $0.0216/1M tokens ($0.72/1M output), while DeepSeek: DeepSeek V4.1 Flash leads in cached prefix economics at $0.0108/1M tokens (50% discount). Meta: Llama 4 Maverick 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 Maverick | DeepSeek: DeepSeek V4.1 Flash | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | meta-llama/llama-4-maverick | deepseek/deepseek-v4.1-flash | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.1875 / $0.6525 | $0.0216 / $0.72 | DeepSeek: DeepSeek V4.1 Flash |
| Cache Read Price (per 1M) | $0.0938/1M (50% Off) | $0.0108/1M (50% Off) | DeepSeek: DeepSeek V4.1 Flash |
| Context Window / Max Output | 1024K In / 16K Out | 1024K In / 922K Out | DeepSeek: DeepSeek V4.1 Flash |
| P90 First-Token / Edge Latency | 280ms | 280ms | Meta: Llama 4 Maverick (-0ms) |
Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active
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
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 Maverick to DeepSeek: DeepSeek V4.1 Flash
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 Verified P90 latency of 280ms.