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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. Kimi K3.1 ProMoonShot AIRemove
  3. Muse Spark 1.3MetaRemove
  4. Gemini 4 ArgonGoogleRemove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs kimi-k3.1-pro vs muse-spark-1.3 vs gemini-4-argon
AttributePerceptron Mk1.5perceptron-mk1.5Kimi K3.1 Prokimi-k3.1-proMuse Spark 1.3muse-spark-1.3Gemini 4 Argongemini-4-argon
Pricing
Input$0.15 / 1M$2.00 / 1M$1.25 / 1M$2.00 / 1M
Output$1.50 / 1M$10.00 / 1M$4.25 / 1M$10.00 / 1M
Cache Write (5m)$0.15 / 1M$2.00 / 1M$1.25 / 1M$2.00 / 1M
Cache Write (1h)$0.15 / 1M$2.00 / 1M$1.25 / 1M$2.00 / 1M
Cache Read$0.15 / 1M$2.00 / 1M$1.25 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context36.9K1M1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesYes
Function CallingNoYesYesYes
JSON ModeNoYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderPerceptronMoonShot AIMetaGoogle
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay 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.Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.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.