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

4 is the maximum. Remove one to add another.

mai-ds-r1:free vs perceptron-mk1.5 vs kimi-k3.1-pro vs gemini-4-argon
Attributemai-ds-r1:freeNot in the cataloguePerceptron Mk1.5perceptron-mk1.5Kimi 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 context—36.9K1M1M
Max output—N/AN/AN/A
Capabilities
Vision—NoYesYes
Function Calling—NoYesYes
JSON Mode—NoYesYes
Streaming—YesYesYes
Catalogue
Provider—PerceptronMoonShot AIGoogle
Category—chatchatchat
Charge type—Pay As You GoPay As You GoPay As You Go
Released—2026-09-25——
Description
Summary—Perceptron 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.