DeepSeek: R1 vs OpenAI GPT-5.5: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek: R1 (deepseek/deepseek-r1) vs OpenAI GPT-5.5 (gpt-5.5). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
DeepSeek: R1 (deepseek/deepseek-r1) — Automatic Disk KV Cache ($0.35/1M) & 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-5.5 (gpt-5.5) 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 (API ID: deepseek/deepseek-r1) vs OpenAI GPT-5.5 (API ID: gpt-5.5), DeepSeek: R1 offers lower standard input pricing at $0.7/1M tokens ($2.5/1M output), while DeepSeek: R1 leads in cached prefix economics at $0.35/1M tokens (50% discount). DeepSeek: R1 achieves a 40ms faster P90 time-to-first-token latency (280ms vs 320ms).
Auto-discovered via live global telemetry registry.
Enable Prompt Context Caching & Batch API to reduce repeated system prompt input token burn by 50%-97.5%.
| Specification / FinOps Dimension | DeepSeek: R1 | OpenAI GPT-5.5 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek/deepseek-r1 | gpt-5.5 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.7 / $2.5 | $5 / $30 | DeepSeek: R1 |
| Cache Read Price (per 1M) | $0.35/1M (50% Off) | $2.5/1M (50% Off) | DeepSeek: R1 |
| Context Window / Max Output | 63K In / 16K Out | 1000K In / 128K Out | OpenAI GPT-5.5 |
| P90 First-Token / Edge Latency | 280ms | 320ms | DeepSeek: R1 (-40ms) |
DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inf
Flagship GPT-5 series general model with configurable reasoning_effort from none to xhigh.
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 to OpenAI GPT-5.5
Flagship GPT-5 series general model with configurable reasoning_effort from none to xhigh. Verified P90 latency of 320ms.