DeepSeek-V4 Pro vs DeepSeek-R1 (Reasoner): Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek-V4 Pro (deepseek-v4-pro) vs DeepSeek-R1 (Reasoner) (deepseek-reasoner). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
Zero-Config Disk KV Cache ($0.07/1M) & Off-Peak UTC Batch Arbitrage
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-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.
In Etuki October 2026 FinOps telemetry comparing DeepSeek-V4 Pro (API ID: deepseek-v4-pro) vs DeepSeek-R1 (Reasoner) (API ID: deepseek-reasoner), DeepSeek-V4 Pro offers lower standard input pricing at $0.14/1M tokens ($0.28/1M output), while DeepSeek-V4 Pro leads in cached prefix economics at $0.07/1M tokens (50% discount). DeepSeek-V4 Pro achieves a 80ms faster P90 time-to-first-token latency (460ms vs 540ms).
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
| Specification / FinOps Dimension | DeepSeek-V4 Pro | DeepSeek-R1 (Reasoner) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek-v4-pro | deepseek-reasoner | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.14 / $0.28 | $0.55 / $2.19 | DeepSeek-V4 Pro |
| Cache Read Price (per 1M) | $0.07/1M (50% Off) | $0.275/1M (50% Off) | DeepSeek-V4 Pro |
| Context Window / Max Output | 1000K In / 128K Out | 400K In / 128K Out | DeepSeek-V4 Pro |
| P90 First-Token / Edge Latency | 460ms | 540ms | DeepSeek-V4 Pro (-80ms) |
1.6-Trillion parameter (49B active) MoE architecture with Fine-Grained Sparse Attention and $0.07/1M disk KV cache hits.
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis.
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-V4 Pro to DeepSeek-R1 (Reasoner)
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis. Verified P90 latency of 540ms.