Inception: Mercury 3 vs DeepSeek-R1 (Reasoner): Live Promos, Pricing, Hidden Fees & Latency
Inception: Mercury 3 (inception/mercury-3) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Inception: Mercury 3: Activate ephemeral prefix caching to slash input costs by 50% (down to 0.05/1M) or route via asynchronous Batch API for 50% off standard token rates.
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 Inception: Mercury 3 (API ID: inception/mercury-3) vs DeepSeek-R1 (Reasoner) (API ID: deepseek-reasoner), Inception: Mercury 3 offers lower standard input pricing at $0.1/1M tokens ($0.35/1M output), while Inception: Mercury 3 leads in cached prefix economics at $0.05/1M tokens (50% discount). Inception: Mercury 3 achieves a 260ms faster P90 time-to-first-token latency (280ms vs 540ms).
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 | Inception: Mercury 3 | DeepSeek-R1 (Reasoner) | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | inception/mercury-3 | deepseek-reasoner | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.1 / $0.35 | $0.55 / $2.19 | Inception: Mercury 3 |
| Cache Read Price (per 1M) | $0.05/1M (50% Off) | $0.275/1M (50% Off) | Inception: Mercury 3 |
| Context Window / Max Output | 977K In / 128K Out | 400K In / 128K Out | Inception: Mercury 3 |
| P90 First-Token / Edge Latency | 280ms | 540ms | Inception: Mercury 3 (-260ms) |
Mercury 3 is a diffusion-based language model from Inception, and the lab's most capable model to date. It is suited for agentic workflows and real-time applications where latency
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 Inception: Mercury 3 to DeepSeek-R1 (Reasoner)
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis. Verified P90 latency of 540ms.