Intelligence index
Outperforms 87% of compared models

Muse Spark 1.2 is a reasoning model from Meta, designed for complex agentic tasks. It accepts text, images, video, and PDF documents, returns text, and offers a 1M-token context window.
The model is built to support multi-agent workflows, whether as either a main agent that plans and delegates or as a subagent executing in parallel. It works across multiple coding harnesses and supports structured output, parallel function calling, and configurable reasoning effort. In Meta’s testing, it performs well on multi-file refactors, extended debugging sessions, whole-repository generation, and tasks that stretch well past a single prompt.
Outperforms 87% of compared models
Outperforms 87% of compared models
Outperforms 84% of compared models
Elo scores and win rates across design and agent tasks.
1280Elo
Win rate 55.5%
1335Elo
Win rate 57.7%
1311Elo
Win rate 52.2%
1311Elo
Win rate 52.1%
1280Elo
Win rate 55.1%
1300Elo
Win rate 50.5%
1295Elo
Win rate 51.9%
1299Elo
Win rate 51.8%
1221Elo
Win rate 53.2%
1142Elo
Win rate 42.4%
1210Elo
Win rate 48%
1094Elo
Win rate 42.1%
1150Elo
Win rate 48.5%
1221Elo
Win rate 50.2%
1059Elo
Win rate 42.9%
1031Elo
Win rate 40%
1182Elo
Win rate 47.9%