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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Perceptron Mk1.5PerceptronRemove
  2. Kimi K3.1 ProMoonShot AIRemove
  3. Gemini 3.8 FlashGoogleRemove
perceptron-mk1.5 vs kimi-k3.1-pro vs gemini-3.8-flash
AttributePerceptron Mk1.5perceptron-mk1.5Kimi K3.1 Prokimi-k3.1-proGemini 3.8 Flashgemini-3.8-flash
Pricing
Input$0.15 / 1M$2.00 / 1M$0.75 / 1M
Output$1.50 / 1M$10.00 / 1M$3.75 / 1M
Cache Write (5m)$0.15 / 1M$2.00 / 1M$0.75 / 1M
Cache Write (1h)$0.15 / 1M$2.00 / 1M$0.75 / 1M
Cache Read$0.15 / 1M$2.00 / 1M$0.75 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context36.9K1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingYesYesYes
Catalogue
ProviderPerceptronMoonShot AIGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-25——
Description
SummaryPerceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.Kimi K3.1 Pro is Moonshot AI's flagship open-weight multimodal reasoning model, building on Kimi K3 with enhanced post-training for advanced reasoning and autonomous task execution. It features a 1M-token context window and native image and video understanding, enabling complex, long-context multimodal workflows. Optimized for long-horizon coding, 3D creation, financial research, and end-to-end agentic knowledge work, Kimi K3.1 Pro is well suited for autonomous software engineering, multimodal analysis, and complex professional workflows that require sustained reasoning and reliable execution.Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.