Muse Glimmer

Meta Superintelligence Labs' first open-weight Muse model — a 30B dense, natively multimodal model built for always-on, on-device agentic workflows, released Apache 2.0 on August 10, 2026. Rather than pretraining from scratch, Muse Glimmer was distilled from Meta's proprietary Muse Spark via logit distillation on the teacher's outputs, then aligned with safety SFT and RL. Architecturally it is a "Dense Causal Transformer with Perception Encoder" (52 layers, ~29.6B language-model params plus a ~1.8B ViT-G/14 vision encoder), 128K context, text and image in — a Meta-native stack (its own muse_glimmer architecture and 202K vocabulary), not a fork of another lab's weights.

On Meta's own card it leads Gemma 4 31B across the board and trades with Qwen3.6-27B among ~30B open models: SWE-Bench Verified 76.0, SWE-Bench Pro 51.2 (#1), AIME 2026 94.7 (#1), GPQA Diamond 83.5, MMMU Pro 74, and strong agentic tool use (MCP Atlas 75.5, DeepSearch QA 74.6, both #1); Terminal-Bench 2.1 51.7 trails Qwen's 60.7. Ships as BF16 plus 4-bit K-quants and a DFlash speculative-decoding drafter for low-latency local inference — the first open-weight entry in the otherwise-proprietary Muse line (Spark, Muse Code). AA had not yet scored it at release.