Skip to content

Compare models

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

  1. Kimi K3.1 ProMoonShot AIRemove
  2. Perceptron Mk1.5PerceptronRemove
  3. Gemini 3.1 Flash Lite Image (Google Vertex AI)GoogleRemove
kimi-k3.1-pro vs perceptron-mk1.5 vs gemini-3.1-flash-lite-image-vertex
AttributeKimi K3.1 Prokimi-k3.1-proPerceptron Mk1.5perceptron-mk1.5Gemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertex
Pricing
Input$2.00 / 1M$0.15 / 1M—
Output$10.00 / 1M$1.50 / 1M—
Cache Write (5m)$2.00 / 1M$0.15 / 1MNot applicable
Cache Write (1h)$2.00 / 1M$0.15 / 1MNot applicable
Cache Read$2.00 / 1M$0.15 / 1MNot applicable
Web Search$0 / 1M$0 / 1M—
Request——$0.0336 / request
Billing——Pay Per Request
Context
Max context1M36.9KN/A
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesNoNo
JSON ModeYesNoNo
StreamingYesYesNo
Catalogue
ProviderMoonShot AIPerceptronGoogle
Categorychatchatimage
Charge typePay As You GoPay As You GoPay Per Request
Released—2026-09-25—
Description
SummaryKimi 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.Perceptron 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.