DeepSeek-R1 (Reasoner) vs Perceptron: Perceptron Mk1.5: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing DeepSeek-R1 (Reasoner) (deepseek-reasoner) vs Perceptron: Perceptron Mk1.5 (perceptron/perceptron-mk1.5). 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.
Perceptron: Perceptron Mk1.5 (perceptron/perceptron-mk1.5) 2026 Q4 Developer Promotion & Volume Credits
Official 2026 Q4 promotion for Perceptron: Perceptron Mk1.5: Activate ephemeral prefix caching to slash input costs by 50% (down to $0.075/1M) or route via asynchronous Batch API for 50% off standard token rates.
In Etuki October 2026 FinOps telemetry comparing DeepSeek-R1 (Reasoner) (API ID: deepseek-reasoner) vs Perceptron: Perceptron Mk1.5 (API ID: perceptron/perceptron-mk1.5), Perceptron: Perceptron Mk1.5 offers lower standard input pricing at $0.15/1M tokens ($1.5/1M output), while Perceptron: Perceptron Mk1.5 leads in cached prefix economics at $0.075/1M tokens (50% discount). Perceptron: Perceptron Mk1.5 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) | Perceptron: Perceptron Mk1.5 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | deepseek-reasoner | perceptron/perceptron-mk1.5 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.55 / $2.19 | $0.15 / $1.5 | Perceptron: Perceptron Mk1.5 |
| Cache Read Price (per 1M) | $0.275/1M (50% Off) | $0.075/1M (50% Off) | Perceptron: Perceptron Mk1.5 |
| Context Window / Max Output | 400K In / 128K Out | 36K In / 8K Out | DeepSeek-R1 (Reasoner) |
| P90 First-Token / Edge Latency | 540ms | 280ms | Perceptron: Perceptron Mk1.5 (-260ms) |
Open-weight reinforcement-learning reasoning model for formal math, finance, and theorem synthesis.
Perceptron Mk1.5 is Perceptron's embodied reasoning model for physical agents. It accepts text, image, video, and audio input, and answers with text plus optional structured annota
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 Perceptron: Perceptron Mk1.5
Perceptron Mk1.5 is Perceptron's embodied reasoning model for physical agents. It accepts text, image, video, and audio input, and answers with text plus optional structured annota Verified P90 latency of 280ms.