DeepSeek-R1 (Reasoner) vs OpenAI: GPT-4.1 Nano (batch): Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek-R1 (Reasoner) (deepseek-reasoner) vs OpenAI: GPT-4.1 Nano (batch) (openai/gpt-4.1-nano:batch). 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: GPT-4.1 Nano (batch) (openai/gpt-4.1-nano:batch) 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: GPT-4.1 Nano (batch) (API ID: openai/gpt-4.1-nano:batch), OpenAI: GPT-4.1 Nano (batch) offers lower standard input pricing at $0.05/1M tokens ($0.2/1M output), while OpenAI: GPT-4.1 Nano (batch) leads in cached prefix economics at $0.025/1M tokens (50% discount). OpenAI: GPT-4.1 Nano (batch) 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: GPT-4.1 Nano (batch) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek-reasoner | openai/gpt-4.1-nano:batch | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.55 / $2.19 | $0.05 / $0.2 | OpenAI: GPT-4.1 Nano (batch) |
| Cache Read Price (per 1M) | $0.275/1M (50% Off) | $0.025/1M (50% Off) | OpenAI: GPT-4.1 Nano (batch) |
| Context Window / Max Output | 400K In / 128K Out | 1023K In / 32K Out | DeepSeek-R1 (Reasoner) |
| P90 First-Token / Edge Latency | 540ms | 280ms | OpenAI: GPT-4.1 Nano (batch) (-260ms) |
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis.
For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million...
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: GPT-4.1 Nano (batch)
For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million... Verified P90 latency of 280ms.