Perceptron: Perceptron Mk1.5 vs DeepSeek: DeepSeek V3.1: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Perceptron: Perceptron Mk1.5 (perceptron/perceptron-mk1.5) vs DeepSeek: DeepSeek V3.1 (deepseek/deepseek-chat-v3.1). Simulate cache read multipliers, context cliffs, MAU bills, and P90 latency.
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.
DeepSeek: DeepSeek V3.1 (deepseek/deepseek-chat-v3.1) — Automatic Disk KV Cache ($0.125/1M) & 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 Perceptron: Perceptron Mk1.5 (API ID: perceptron/perceptron-mk1.5) vs DeepSeek: DeepSeek V3.1 (API ID: deepseek/deepseek-chat-v3.1), 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 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 | Perceptron: Perceptron Mk1.5 | DeepSeek: DeepSeek V3.1 | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | perceptron/perceptron-mk1.5 | deepseek/deepseek-chat-v3.1 | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.15 / $1.5 | $0.25 / $0.95 | Perceptron: Perceptron Mk1.5 |
| Cache Read Price (per 1M) | $0.075/1M (50% Off) | $0.125/1M (50% Off) | Perceptron: Perceptron Mk1.5 |
| Context Window / Max Output | 36K In / 8K Out | 160K In / 32K Out | DeepSeek: DeepSeek V3.1 |
| P90 First-Token / Edge Latency | 280ms | 280ms | Perceptron: Perceptron Mk1.5 (-0ms) |
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
DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 ba
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 Perceptron: Perceptron Mk1.5 to DeepSeek: DeepSeek V3.1
DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 ba Verified P90 latency of 280ms.