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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. Perceptron Mk1.5PerceptronRemove
  2. mai-ds-r1:freeNot in the catalogueRemove
  3. Kimi K3.1 ProMoonShot AIRemove
  4. Gemini 4 ArgonGoogleRemove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs mai-ds-r1:free vs kimi-k3.1-pro vs gemini-4-argon
AttributePerceptron Mk1.5perceptron-mk1.5mai-ds-r1:freeNot in the catalogueKimi K3.1 Prokimi-k3.1-proGemini 4 Argongemini-4-argon
Pricing
Input$0.15 / 1M—$2.00 / 1M$2.00 / 1M
Output$1.50 / 1M—$10.00 / 1M$10.00 / 1M
Cache Write (5m)$0.15 / 1M—$2.00 / 1M$2.00 / 1M
Cache Write (1h)$0.15 / 1M—$2.00 / 1M$2.00 / 1M
Cache Read$0.15 / 1M—$2.00 / 1M$2.00 / 1M
Web Search$0 / 1M—$0 / 1M$0 / 1M
Context
Max context36.9K—1M1M
Max outputN/A—N/AN/A
Capabilities
VisionNo—YesYes
Function CallingNo—YesYes
JSON ModeNo—YesYes
StreamingYes—YesYes
Catalogue
ProviderPerceptron—MoonShot AIGoogle
Categorychat—chatchat
Charge typePay As You Go—Pay 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 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.