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.

GLM 5.3Z.AI Remove
Ministral 3 8B 2512Mistral AI Remove
Muse Spark 1.3Meta Remove

Add a model

glm-5.3 vs ministral-8b-2512 vs muse-spark-1.3
Attribute	GLM 5.3glm-5.3	Ministral 3 8B 2512ministral-8b-2512	Muse Spark 1.3muse-spark-1.3
Pricing
Input	$1.40 / 1M	$0.15 / 1M	$1.25 / 1M
Output	$4.40 / 1M	$0.15 / 1M	$4.25 / 1M
Cache Write (5m)	$1.40 / 1M	$0.15 / 1M	$1.25 / 1M
Cache Write (1h)	$1.40 / 1M	$0.15 / 1M	$1.25 / 1M
Cache Read	$1.40 / 1M	$0.15 / 1M	$1.25 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	262.1K	1M
Max output	N/A	N/A	N/A
Capabilities
Vision	No	Yes	Yes
Function Calling	Yes	Yes	Yes
JSON Mode	Yes	Yes	Yes
Streaming	Yes	Yes	Yes
Catalogue
Provider	Z.AI	Mistral AI	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 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution.	Ministral 3 8B is the balanced model in the Ministral 3 series — a compact, efficient language model that still delivers strong performance and includes vision capabilities.	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.