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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. Gemini 3.1 Flash Lite Image (Google Vertex AI)GoogleRemove
  3. Gemini 3.8 FlashGoogleRemove
perceptron-mk1.5 vs gemini-3.1-flash-lite-image-vertex vs gemini-3.8-flash
AttributePerceptron Mk1.5perceptron-mk1.5Gemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertexGemini 3.8 Flashgemini-3.8-flash
Pricing
Input$0.15 / 1M—$0.75 / 1M
Output$1.50 / 1M—$3.75 / 1M
Cache Write (5m)$0.15 / 1MNot applicable$0.75 / 1M
Cache Write (1h)$0.15 / 1MNot applicable$0.75 / 1M
Cache Read$0.15 / 1MNot applicable$0.75 / 1M
Web Search$0 / 1M—$0 / 1M
Request—$0.0336 / request—
Billing—Pay Per Request—
Context
Max context36.9KN/A1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoNoYes
JSON ModeNoNoYes
StreamingYesNoYes
Catalogue
ProviderPerceptronGoogleGoogle
Categorychatimagechat
Charge typePay As You GoPay Per RequestPay As You Go
Released2026-09-25——
Description
SummaryPerceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.Gemini 3.1 Flash Lite Image served from Google Vertex AI. Billed per image at US$0.0336. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 1K. A request for a larger size returns a 1K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size.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.