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.

Claude Fable 5.1Anthropic Remove
Qwen3 Coder NextAlibaba Remove
GLM 5.3Z.AI Remove
Muse Spark 1.3Meta Remove

4 is the maximum. Remove one to add another.

claude-fable-5.1 vs qwen3-coder-next vs glm-5.3 vs muse-spark-1.3
Attribute	Claude Fable 5.1claude-fable-5.1	Qwen3 Coder Nextqwen3-coder-next	GLM 5.3glm-5.3	Muse Spark 1.3muse-spark-1.3
Pricing
Input	$10.00 / 1M	$0.175 / 1M	$1.40 / 1M	$1.25 / 1M
Output	$50.00 / 1M	$1.40 / 1M	$4.40 / 1M	$4.25 / 1M
Cache Write (5m)	$12.50 / 1M	$0.175 / 1M	$1.40 / 1M	$1.25 / 1M
Cache Write (1h)	$20.00 / 1M	$0.175 / 1M	$1.40 / 1M	$1.25 / 1M
Cache Read	$1.00 / 1M	$0.175 / 1M	$1.40 / 1M	$1.25 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	262.1K	1M	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	Yes	No	Yes
Function Calling	Yes	Yes	Yes	Yes
JSON Mode	Yes	Yes	Yes	Yes
Streaming	Yes	Yes	Yes	Yes
Catalogue
Provider	Anthropic	Alibaba	Z.AI	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	—	—	—	—
Description
Summary	Claude Fable 5.1 is an upgraded version of Fable 5, delivering broad improvements with particularly strong gains in agentic coding, long-running workflows, and professional knowledge work. It excels at large code refactors, front-end and visual code generation, financial analysis, and complex analytical tasks. Compared with Fable 5, it also produces more concise plans and summaries while maintaining strong performance across extended tasks, making it a natural upgrade for existing Fable workflows and a strong option alongside Opus 5 for reasoning-intensive applications.	Qwen3-Coder-Next is an open-weight causal language model purpose-built for coding agents and local development workflows. It employs a sparse Mixture-of-Experts (MoE) architecture with 80B total parameters and only 3B activated per token, achieving performance comparable to models with 10–20× higher active compute. This efficiency makes it especially well suited for cost-sensitive, always-on agent deployments. Trained with a strong agentic focus, Qwen3-Coder-Next performs reliably on long-horizon coding tasks, complex tool interactions, and robust recovery from execution failures. With a native 256K context window, it integrates smoothly into real-world CLI and IDE environments and aligns well with common agent scaffolding used by modern coding tools. The model operates exclusively in non-thinking mode and does not emit <think> blocks, simplifying production integration for coding agents.	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.	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.