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Meta

Available in Shortly AI

Muse Spark 1.3

Released September 20, 2026

ReasoningVision (Image)Tool Use

Muse Spark 1.3 is Meta’s multimodal reasoning model for long-horizon agentic and coding workflows. With a 1M-token context window, reliable tool calling, higher first-attempt accuracy, and native understanding of video, images, and documents, it helps developers build capable coding agents and AI development workflows with fewer unnecessary turns and cleaner output.

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Context window
1M tokens
Maximum output
1M tokens
Input pricing
$1.25/M
Output pricing
$4.25/M

Independent benchmark

LiveBench performance

81.6overall

Benchmark data from LiveBench, release 2026-06-25. Scores are pinned and are not fetched on page load.

Muse Spark 1.3 LiveBench category scores
CategoryScore
Reasoning89.654
Coding81.063
Agentic Coding64.091
Mathematics95.950
Data Analysis79.565
Language82.794
Instruction Following78.004

Benchmarks measure selected evaluation tasks and do not guarantee performance for every prompt or workflow.

FAQ

Similar AI models

GLM 5.3Z.aiGLM 5.3 delivers comprehensive advancements in complex software engineering and agent capabilities. It uses the same base model as GLM-5.2, with all improvements driven by post-training.Muse Spark 1.2MetaA coding-optimized model purpose-built for agentic workflows. Improvements to code generation, debugging, and codebase understanding — with a 1M context window that handles your entire project in one session.Qwen 3.8 MaxAlibabaQwen 3.8 Max is a 2.4-trillion-parameter MoE model delivering a comprehensive leap in coding and professional work. Autonomously codes and delivers complete projects spanning 10+ days. Handles hundreds of specialized tasks across legal, financial, design, and other professional domains, producing production-grade results end-to-end in a single conversation. Native visual understanding runs through the full cycle of planning, execution, and verification, enabling deep semantic analysis of ultra-long documents and extended video content. In long-horizon tasks, plans autonomously, iterates through closed feedback loops, and continuously evolves.

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