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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
Mistral Medium 3.5Mistral Remove

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muse-spark-1.3 vs glm-5.3-flash vs mistral-medium-3-5
Attribute	Muse Spark 1.3muse-spark-1.3	GLM 5.3 Flashglm-5.3-flash	Mistral Medium 3.5mistral-medium-3-5
Pricing
Input	$1.25 / 1M	$0.075 / 1M	$1.50 / 1M
Output	$4.25 / 1M	$0.25 / 1M	$7.50 / 1M
Cache Write (5m)	$1.25 / 1M	$0.075 / 1M	$1.50 / 1M
Cache Write (1h)	$1.25 / 1M	$0.075 / 1M	$1.50 / 1M
Cache Read	$1.25 / 1M	$0.075 / 1M	$1.50 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	1M	262.1K
Max output	N/A	N/A	N/A
Capabilities
Vision	Yes	Yes	Yes
Function Calling	Yes	Yes	Yes
JSON Mode	Yes	Yes	Yes
Streaming	Yes	Yes	Yes
Catalogue
Provider	Meta	Z.AI	Mistral
Category	chat	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	—	—
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.	Mistral Medium 3.5 is a 128B dense instruction-following model from Mistral AI, supporting text and image inputs with text output. It is designed for agentic workflows, coding, and complex multi-step reasoning, with strong reliability in multi-tool orchestration and long-horizon tasks. The model features a 256K token context window, configurable reasoning effort per request, and a custom vision encoder that handles variable image sizes and aspect ratios. With support for self-hosting on as few as four GPUs and availability under open weights, it is well suited for scalable, production-grade deployments.