Meta Muse Glimmer Release: 30B Parameters, Apache 2.0, Benchmark Analysis

What Shipped

Meta released Muse Glimmer, a 30-billion-parameter open-weights language model distributed under the Apache 2.0 license. The release includes full-precision weights, quantized variants, a drafter, and a perception encoder, all downloadable via Hugging Face without API restrictions.

Architecture and Hardware Requirements

The model contains 29.6 billion dense parameters including the vision encoder and supports a 128,000-token context window extendable to 131,000+ tokens. Full-precision inference requires over 55 GB of VRAM, while 4-bit quantization reduces the footprint to under 20 GB, enabling deployment on consumer GPUs.

Benchmark Results

Meta-reported scores include SWE-Bench Verified at 75.5, GPQA Diamond at 83.5, and AIME 2026 at 23.5. Independent testing by Artificial Analysis assigns an overall Intelligence Index of 35, a 21-point gain over Llama 4 Maverick but below Qwen3.6 27B at 38. On agentic reasoning, Glimmer scores 953 Elo on GDPval-AA v2, trailing Qwen3.6 27B and Gemini 3.5 Flash-Lite (both 1,141).

Comparative Positioning

Despite utilizing 33× fewer parameters than Kimi K2.5 (1T total), Glimmer matches its reasoning performance and exceeds Gemma 4 31B by 5 points. However, it records an 82% hallucination rate on AA-Omniscience, significantly higher than Qwen3.6 27B (49%) and Gemini 3.5 Flash-Lite (34%). Third-party task completion testing shows 83.3%, outperforming Gemma 4 31B and Qwen3.6-27B (both 77.7%), while Artificial Analysis reports 24% on Tau3-Banking agentic tool use, ahead of Gemini 3.5 Flash-Lite (18%) and Qwen3.6 27B (17%).

Availability and Licensing

Meta ships the model under Apache 2.0, permitting commercial use and modification. No official API pricing exists; inference costs depend on third-party hosting or local hardware. Weights are available freely on Hugging Face.

Source: TechCrunch AI