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.

Kimi K3.1 ProMoonShot AI Remove
Perceptron Mk1.5Perceptron Remove
mai-ds-r1:freeNot in the catalogue Remove
Muse WonderMeta Remove

4 is the maximum. Remove one to add another.

kimi-k3.1-pro vs perceptron-mk1.5 vs mai-ds-r1:free vs muse-wonder
Attribute	Kimi K3.1 Prokimi-k3.1-pro	Perceptron Mk1.5perceptron-mk1.5	mai-ds-r1:freeNot in the catalogue	Muse Wondermuse-wonder
Pricing
Input	$2.00 / 1M	$0.15 / 1M	—	$4.00 / 1M
Output	$10.00 / 1M	$1.50 / 1M	—	$20.00 / 1M
Cache Write (5m)	$2.00 / 1M	$0.15 / 1M	—	$4.00 / 1M
Cache Write (1h)	$2.00 / 1M	$0.15 / 1M	—	$4.00 / 1M
Cache Read	$2.00 / 1M	$0.15 / 1M	—	$4.00 / 1M
Web Search	$0 / 1M	$0 / 1M	—	$0 / 1M
Context
Max context	1M	36.9K	—	1M
Max output	N/A	N/A	—	N/A
Capabilities
Vision	Yes	No	—	Yes
Function Calling	Yes	No	—	Yes
JSON Mode	Yes	No	—	Yes
Streaming	Yes	Yes	—	Yes
Catalogue
Provider	MoonShot AI	Perceptron	—	Meta
Category	chat	chat	—	chat
Charge type	Pay As You Go	Pay As You Go	—	Pay 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 Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision.