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.

Muse Spark 1.3Meta Remove
GLM 5.3 FlashZ.AI Remove
Perceptron Mk1.5Perceptron Remove
Muse WonderMeta Remove

4 is the maximum. Remove one to add another.

muse-spark-1.3 vs glm-5.3-flash vs perceptron-mk1.5 vs muse-wonder
Attribute	Muse Spark 1.3muse-spark-1.3	GLM 5.3 Flashglm-5.3-flash	Perceptron Mk1.5perceptron-mk1.5	Muse Wondermuse-wonder
Pricing
Input	$1.25 / 1M	$0.075 / 1M	$0.15 / 1M	$4.00 / 1M
Output	$4.25 / 1M	$0.25 / 1M	$1.50 / 1M	$20.00 / 1M
Cache Write (5m)	$1.25 / 1M	$0.075 / 1M	$0.15 / 1M	$4.00 / 1M
Cache Write (1h)	$1.25 / 1M	$0.075 / 1M	$0.15 / 1M	$4.00 / 1M
Cache Read	$1.25 / 1M	$0.075 / 1M	$0.15 / 1M	$4.00 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	1M	36.9K	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	Yes	No	Yes
Function Calling	Yes	Yes	No	Yes
JSON Mode	Yes	Yes	No	Yes
Streaming	Yes	Yes	Yes	Yes
Catalogue
Provider	Meta	Z.AI	Perceptron	Meta
Category	chat	chat	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	—	2026-09-25	—
Description
Summary	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.	GLM-5.3-Flash is Z.AI's efficient native multimodal model, designed for coding and long-horizon agentic workflows. It combines strong multimodal capabilities with an architecture optimized for responsive, cost-efficient task execution. Built on a hybrid sparse and linear attention architecture, GLM-5.3-Flash maintains accurate long-context behavior while reducing computational overhead, making it well suited for coding agents, extended multi-step tasks, and scalable production workloads.	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.