DeepSeek: DeepSeek V3.2 vs ScaleDown: Extract: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek: DeepSeek V3.2 (deepseek/deepseek-v3.2) vs ScaleDown: Extract (scaledown/extract). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek: DeepSeek V3.2 (deepseek/deepseek-v3.2) — Automatic Disk KV Cache ($0.1295/1M) & 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.
ScaleDown: Extract (scaledown/extract) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for ScaleDown: Extract: 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 DeepSeek: DeepSeek V3.2 (API ID: deepseek/deepseek-v3.2) vs ScaleDown: Extract (API ID: scaledown/extract), ScaleDown: Extract offers lower standard input pricing at $0.04/1M tokens ($0/1M output), while ScaleDown: Extract leads in cached prefix economics at $0.02/1M tokens (50% discount). DeepSeek: DeepSeek V3.2 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 V3.2 | ScaleDown: Extract | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-v3.2 | scaledown/extract | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.259 / $0.8 | $0.04 / $0 | ScaleDown: Extract |
| Cache Read Price (per 1M) | $0.1295/1M (50% Off) | $0.02/1M (50% Off) | ScaleDown: Extract |
| Context Window / Max Output | 160K In / 144K Out | 977K In / 879K Out | ScaleDown: Extract |
| P90 First-Token / Edge Latency | 280ms | 280ms | DeepSeek: DeepSeek V3.2 (-0ms) |
DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse At
Extract is a description-driven entity extraction model from ScaleDown. Instead of a fixed set of entity types, callers describe the fields they want in plain English through `prov
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 V3.2 to ScaleDown: Extract
Extract is a description-driven entity extraction model from ScaleDown. Instead of a fixed set of entity types, callers describe the fields they want in plain English through `prov Verified P90 latency of 280ms.