DeepSeek-R1 (Reasoner) vs DeepSeek: DeepSeek V3.2: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek-R1 (Reasoner) (deepseek-reasoner) vs DeepSeek: DeepSeek V3.2 (deepseek/deepseek-v3.2). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek-R1 (Reasoner) (deepseek-reasoner) — Automatic Disk KV Cache ($0.275/1M) & 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.
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.
In Etuki October 2026 FinOps telemetry comparing DeepSeek-R1 (Reasoner) (API ID: deepseek-reasoner) vs DeepSeek: DeepSeek V3.2 (API ID: deepseek/deepseek-v3.2), DeepSeek: DeepSeek V3.2 offers lower standard input pricing at $0.259/1M tokens ($0.8/1M output), while DeepSeek: DeepSeek V3.2 leads in cached prefix economics at $0.1295/1M tokens (50% discount). DeepSeek: DeepSeek V3.2 achieves a 260ms faster P90 time-to-first-token latency (280ms vs 540ms).
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 | DeepSeek-R1 (Reasoner) | DeepSeek: DeepSeek V3.2 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek-reasoner | deepseek/deepseek-v3.2 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.55 / $2.19 | $0.259 / $0.8 | DeepSeek: DeepSeek V3.2 |
| Cache Read Price (per 1M) | $0.275/1M (50% Off) | $0.1295/1M (50% Off) | DeepSeek: DeepSeek V3.2 |
| Context Window / Max Output | 400K In / 128K Out | 160K In / 144K Out | DeepSeek: DeepSeek V3.2 |
| P90 First-Token / Edge Latency | 540ms | 280ms | DeepSeek: DeepSeek V3.2 (-260ms) |
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis.
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
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-R1 (Reasoner) to DeepSeek: DeepSeek V3.2
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 Verified P90 latency of 280ms.