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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. Gemini 3.1 Flash Lite Image (Google Vertex AI)GoogleRemove
  2. Perceptron Mk1.5PerceptronRemove
  3. Gemini 3.8 FlashGoogleRemove
gemini-3.1-flash-lite-image-vertex vs perceptron-mk1.5 vs gemini-3.8-flash
AttributeGemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertexPerceptron Mk1.5perceptron-mk1.5Gemini 3.8 Flashgemini-3.8-flash
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$0.15 / 1M$0.75 / 1M
Cache Write (1h)Not applicable$0.15 / 1M$0.75 / 1M
Cache ReadNot applicable$0.15 / 1M$0.75 / 1M
Input—$0.15 / 1M$0.75 / 1M
Output—$1.50 / 1M$3.75 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A36.9K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingNoNoYes
JSON ModeNoNoYes
StreamingNoYesYes
Catalogue
ProviderGooglePerceptronGoogle
Categoryimagechatchat
Charge typePay Per RequestPay As You GoPay As You Go
Released—2026-09-25—
Description
SummaryGemini 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.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.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.