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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.

GLM 5.3 FlashZ.AI Remove
Qwen3 Coder FlashAlibaba Remove
Muse Spark 1.3Meta Remove

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glm-5.3-flash vs qwen3-coder-flash vs muse-spark-1.3
Attribute	GLM 5.3 Flashglm-5.3-flash	Qwen3 Coder Flashqwen3-coder-flash	Muse Spark 1.3muse-spark-1.3
Pricing
Input	$0.075 / 1M	$0.30 / 1M	$1.25 / 1M
Output	$0.25 / 1M	$1.50 / 1M	$4.25 / 1M
Cache Write (5m)	$0.075 / 1M	$0.30 / 1M	$1.25 / 1M
Cache Write (1h)	$0.075 / 1M	$0.30 / 1M	$1.25 / 1M
Cache Read	$0.075 / 1M	$0.30 / 1M	$1.25 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	128K	1M
Max output	N/A	N/A	N/A
Capabilities
Vision	Yes	No	Yes
Function Calling	Yes	Yes	Yes
JSON Mode	Yes	Yes	Yes
Streaming	Yes	Yes	Yes
Catalogue
Provider	Z.AI	Alibaba	Meta
Category	chat	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	—	—
Description
Summary	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.	Qwen3 Coder Flash is Alibaba's fast, cost-efficient coding agent model — a lighter version of Qwen3 Coder Plus — built for autonomous programming through tool calling and environment interaction, while still retaining strong general-purpose abilities.	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.