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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. Kimi K3.1 ProMoonShot AIRemove
  3. Perceptron Mk1.5PerceptronRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

mai-ds-r1:free vs kimi-k3.1-pro vs perceptron-mk1.5 vs muse-spark-1.3
Attributemai-ds-r1:freeNot in the catalogueKimi K3.1 Prokimi-k3.1-proPerceptron Mk1.5perceptron-mk1.5Muse Spark 1.3muse-spark-1.3
Pricing
Input—$2.00 / 1M$0.15 / 1M$1.25 / 1M
Output—$10.00 / 1M$1.50 / 1M$4.25 / 1M
Cache Write (5m)—$2.00 / 1M$0.15 / 1M$1.25 / 1M
Cache Write (1h)—$2.00 / 1M$0.15 / 1M$1.25 / 1M
Cache Read—$2.00 / 1M$0.15 / 1M$1.25 / 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 AIPerceptronMeta
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.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.