DeepSeek-R1 (Reasoner) vs OpenAI: o1-pro: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek-R1 (Reasoner) (deepseek-reasoner) vs OpenAI: o1-pro (openai/o1-pro). 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.
OpenAI: o1-pro (openai/o1-pro) Startup Credits & Batch API 50% Off Promo
OpenAI GPT-6.1 Sol ($2/$10 base rate) qualifies for immediate 75% automatic prefix caching ($0.50/1M), 50% off 24-hour Batch API completions ($1/$5), and up to $150,000 in OpenAI/Azure Foundry credits via Microsoft for Startups Founders Hub (no VC funding required for initial tiers).
In Etuki October 2026 FinOps telemetry comparing DeepSeek-R1 (Reasoner) (API ID: deepseek-reasoner) vs OpenAI: o1-pro (API ID: openai/o1-pro), DeepSeek-R1 (Reasoner) offers lower standard input pricing at $0.55/1M tokens ($2.19/1M output), while DeepSeek-R1 (Reasoner) leads in cached prefix economics at $0.275/1M tokens (50% discount). OpenAI: o1-pro 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) | OpenAI: o1-pro | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek-reasoner | openai/o1-pro | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.55 / $2.19 | $150 / $600 | DeepSeek-R1 (Reasoner) |
| Cache Read Price (per 1M) | $0.275/1M (50% Off) | $75/1M (50% Off) | DeepSeek-R1 (Reasoner) |
| Context Window / Max Output | 400K In / 128K Out | 195K In / 98K Out | DeepSeek-R1 (Reasoner) |
| P90 First-Token / Edge Latency | 540ms | 280ms | OpenAI: o1-pro (-260ms) |
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis.
The o1 series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o1-pro model uses more compute to think harder and pr
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 OpenAI: o1-pro
The o1 series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o1-pro model uses more compute to think harder and pr Verified P90 latency of 280ms.