Meta AI, Llama models, open research and related products.
LatestARC Prize publishes Meta Muse Spark Open's evaluation results on ARC-AGI
Why read thisARC Prize gives Meta's new model's scores on three versions of ARC-AGI along with per-task cost, directly comparable with competitors scoring the same.
Fireworks launched Meta's open-weight model Muse Spark Open on the day of release. It is an 816B-parameter MoE model that activates 42B parameters per token, with a 1M-token context window, targeting long-horizon coding and agentic tasks. Fireworks says it ranks first among 75 models on Vals AI's Harvey Legal Agent Benchmark leaderboard with 25.4%, nearly 4 times that of Claude Opus 5 (6.7%); on the Rogo Big Finance Bench it surpasses Claude Opus 5.5 at every difficulty level. On Fireworks, the model has zero data retention enabled by default, uses only US-based compute at no extra charge, and supports fine-tuning with serverless LoRA.
Why read thisThe original article lists the model's specific scores and comparisons on multiple professional benchmarks, allowing readers to judge where open-weight models stand on regulated professional tasks.
Why read thisMeta has published the technical report for a 30B open-weight model, covering architecture, distillation recipe, and quantization, for readers interested in on-device agent deployment.
Meta Superintelligence Labs releases its second-generation model family Muse Wonder and Muse Wonder Full; the former is positioned as a fast frontier model, the latter as the full version, and Meta says they target personal and professional agent work. On Artificial Analysis's Intelligence Index v4.3, Wonder Full scores 56 and Wonder scores 52, higher than Muse Spark 1.3's 48, while Opus 5.5 leads with 58. Specifications are 1M context, input supports text, images, video and audio, output is text only, and pricing is 4 USD per million tokens for input, 0.20 USD for cached input, and 20 USD for output.
Why read thisSummarizes third-party evaluations and pricing, allowing comparison against models such as Opus 5.5 and GPT-6 to judge the actual position of Meta's new models.
Why read thisMeta open-sources a 30B dense model that runs locally on 18GB of memory and supports vision, letting readers gauge the local deployment threshold.
Fireworks has launched Meta's Muse Glimmer 30B dense model under the Apache 2.0 license, supporting 128K+ token context and native image understanding, targeting persistent agents that require multi-turn tool calling. The model scores 75.5 on MCP Atlas, 74.6 on DeepSearch QA, and 51.2 on SWE-Bench Pro, all higher than the corresponding results of Gemma 4 31B and Qwen 3.6 27B; these benchmark figures are reported by Meta. Fireworks also provides recommended settings of temperature 1.0, top_p 0.95, and top_k 64, and supports adjusting reasoning intensity in the system prompt.
Why read thisThe original article provides comparison data on tool-orchestration benchmarks and deployment configurations for the 30B model, which can be used to judge whether it is suitable for long-running agents.