Perceptron: Perceptron Mk1.5 vs DeepSeek-V4 Flash: Live Promos, Pricing, Hidden Fees & Latency
Verified 2026 FinOps benchmark comparing Perceptron: Perceptron Mk1.5 (perceptron/perceptron-mk1.5) vs DeepSeek-V4 Flash (deepseek-v4-flash). 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-V4 Flash (deepseek-v4-flash) — Automatic Disk KV Cache ($0.035/1M) & 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-V4 Flash (API ID: deepseek-v4-flash), DeepSeek-V4 Flash offers lower standard input pricing at $0.07/1M tokens ($0.14/1M output), while DeepSeek-V4 Flash leads in cached prefix economics at $0.035/1M tokens (50% discount). DeepSeek-V4 Flash achieves a 40ms faster P90 time-to-first-token latency (240ms vs 280ms).
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 | Perceptron: Perceptron Mk1.5 | DeepSeek-V4 Flash | Verified Advantage |
|---|---|---|---|
| Official Vendor API ID | perceptron/perceptron-mk1.5 | deepseek-v4-flash | Pinned Snapshot IDs |
| Standard Input / Output (per 1M) | $0.15 / $1.5 | $0.07 / $0.14 | DeepSeek-V4 Flash |
| Cache Read Price (per 1M) | $0.075/1M (50% Off) | $0.035/1M (50% Off) | DeepSeek-V4 Flash |
| Context Window / Max Output | 36K In / 8K Out | 1000K In / 64K Out | DeepSeek-V4 Flash |
| P90 First-Token / Edge Latency | 280ms | 240ms | DeepSeek-V4 Flash (-40ms) |
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
284B parameter (13B active) high-speed sparse-attention model with thinking integrated directly into tool calls.
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-V4 Flash
284B parameter (13B active) high-speed sparse-attention model with thinking integrated directly into tool calls. Verified P90 latency of 240ms.