DeepSeek: R1 0528 vs OpenAI: o1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek: R1 0528 (deepseek/deepseek-r1-0528) vs OpenAI: o1 (openai/o1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek: R1 0528 (deepseek/deepseek-r1-0528) — Automatic Disk KV Cache ($0.25/1M) & 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 (openai/o1) 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 0528 (API ID: deepseek/deepseek-r1-0528) vs OpenAI: o1 (API ID: openai/o1), DeepSeek: R1 0528 offers lower standard input pricing at $0.5/1M tokens ($2.15/1M output), while DeepSeek: R1 0528 leads in cached prefix economics at $0.25/1M tokens (50% discount). DeepSeek: R1 0528 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: R1 0528 | OpenAI: o1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-r1-0528 | openai/o1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.5 / $2.15 | $15 / $60 | DeepSeek: R1 0528 |
| Cache Read Price (per 1M) | $0.25/1M (50% Off) | $7.5/1M (50% Off) | DeepSeek: R1 0528 |
| Context Window / Max Output | 160K In / 32K Out | 195K In / 98K Out | OpenAI: o1 |
| P90 First-Token / Edge Latency | 280ms | 280ms | DeepSeek: R1 0528 (-0ms) |
May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 67
The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learn
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 0528 to OpenAI: o1
The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learn Verified P90 latency of 280ms.