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.

Qwen3.8 27BAlibaba Remove
Muse Spark 1.3Meta Remove
Qwen2.5 VL 72B InstructAlibaba Remove
GLM 5.3Z.AI Remove

4 is the maximum. Remove one to add another.

qwen3.8-27b vs muse-spark-1.3 vs qwen2.5-vl-72b-instruct vs glm-5.3
Attribute	Qwen3.8 27Bqwen3.8-27b	Muse Spark 1.3muse-spark-1.3	Qwen2.5 VL 72B Instructqwen2.5-vl-72b-instruct	GLM 5.3glm-5.3
Pricing
Input	$0.45 / 1M	$1.25 / 1M	$0.25 / 1M	$1.40 / 1M
Output	$3.20 / 1M	$4.25 / 1M	$0.75 / 1M	$4.40 / 1M
Cache Write (5m)	$0.45 / 1M	$1.25 / 1M	$0.25 / 1M	$1.40 / 1M
Cache Write (1h)	$0.45 / 1M	$1.25 / 1M	$0.25 / 1M	$1.40 / 1M
Cache Read	$0.45 / 1M	$1.25 / 1M	$0.25 / 1M	$1.40 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	262K	1M	32K	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	Yes	No	No
Function Calling	Yes	Yes	No	Yes
JSON Mode	Yes	Yes	No	Yes
Streaming	Yes	Yes	No	Yes
Catalogue
Provider	Alibaba	Meta	Alibaba	Z.AI
Category	chat	chat	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	—	—	—
Description
Summary	Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.	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 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.