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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. mai-ds-r1:freeNot in the catalogueRemove
  2. Perceptron Mk1.5PerceptronRemove
  3. Kimi K3.1 ProMoonShot AIRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

mai-ds-r1:free vs perceptron-mk1.5 vs kimi-k3.1-pro vs muse-spark-1.3
Attributemai-ds-r1:freeNot in the cataloguePerceptron Mk1.5perceptron-mk1.5Kimi K3.1 Prokimi-k3.1-proMuse 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 context—36.9K1M1M
Max output—N/AN/AN/A
Capabilities
Vision—NoYesYes
Function Calling—NoYesYes
JSON Mode—NoYesYes
Streaming—YesYesYes
Catalogue
Provider—PerceptronMoonShot AIMeta
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.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.