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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. Kimi K3.1 ProMoonShot AIRemove
  3. Perceptron Mk1.5PerceptronRemove
  4. Gemini 4 ArgonGoogleRemove

4 is the maximum. Remove one to add another.

mai-ds-r1:free vs kimi-k3.1-pro vs perceptron-mk1.5 vs gemini-4-argon
Attributemai-ds-r1:freeNot in the catalogueKimi K3.1 Prokimi-k3.1-proPerceptron Mk1.5perceptron-mk1.5Gemini 4 Argongemini-4-argon
Pricing
Input—$2.00 / 1M$0.15 / 1M$2.00 / 1M
Output—$10.00 / 1M$1.50 / 1M$10.00 / 1M
Cache Write (5m)—$2.00 / 1M$0.15 / 1M$2.00 / 1M
Cache Write (1h)—$2.00 / 1M$0.15 / 1M$2.00 / 1M
Cache Read—$2.00 / 1M$0.15 / 1M$2.00 / 1M
Web Search—$0 / 1M$0 / 1M$0 / 1M
Context
Max context—1M36.9K1M
Max output—N/AN/AN/A
Capabilities
Vision—YesNoYes
Function Calling—YesNoYes
JSON Mode—YesNoYes
Streaming—YesYesYes
Catalogue
Provider—MoonShot AIPerceptronGoogle
Category—chatchatchat
Charge type—Pay As You GoPay As You GoPay As You Go
Released——2026-09-25—
Description
Summary—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.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.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.