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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. mai-ds-r1:freeNot in the catalogueRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs kimi-k3.1-pro vs mai-ds-r1:free vs muse-spark-1.3
AttributePerceptron Mk1.5perceptron-mk1.5Kimi K3.1 Prokimi-k3.1-promai-ds-r1:freeNot in the catalogueMuse Spark 1.3muse-spark-1.3
Pricing
Input$0.15 / 1M$2.00 / 1M—$1.25 / 1M
Output$1.50 / 1M$10.00 / 1M—$4.25 / 1M
Cache Write (5m)$0.15 / 1M$2.00 / 1M—$1.25 / 1M
Cache Write (1h)$0.15 / 1M$2.00 / 1M—$1.25 / 1M
Cache Read$0.15 / 1M$2.00 / 1M—$1.25 / 1M
Web Search$0 / 1M$0 / 1M—$0 / 1M
Context
Max context36.9K1M—1M
Max outputN/AN/A—N/A
Capabilities
VisionNoYes—Yes
Function CallingNoYes—Yes
JSON ModeNoYes—Yes
StreamingYesYes—Yes
Catalogue
ProviderPerceptronMoonShot AI—Meta
Categorychatchat—chat
Charge typePay As You GoPay As You Go—Pay 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.