Meta 发布开源权重模型 Muse Spark Open：816B 参数，登顶美国开源权重榜首
Meta 放了个 816B 的开源权重模型 Muse Spark Open，美国开源第一，1M 上下文还能塞进两台 Mac Studio 自托管，不过跑智能体任务不如 GLM-5.3。
Meta 发布开源权重模型 Muse Spark Open，总参数 816B（激活 42B，MoE 架构），在 Artificial Analysis Intelligence Index 得分 39，为美国开源权重模型第一。该模型比 Meta 8 月发布的 30B 稠密模型 Muse Glimmer 高出 22 分，领先 NVIDIA Nemotron 3.5 Super VL（26 分）。定价为每 100 万输入/输出 token $1.25/$4.25，单任务成本 $0.84，不到 GLM-5.3（max，$2.01）的一半，但约为 DeepSeek V4.1 Flash（max，$0.27）的三倍。其短板是智能体任务，在九个最高分开源权重模型中的四项智能体评估里排名垫底，规模最接近的中国模型 GLM-5.3（max）领先它的 6 分全部来自智能体评估。
Congrats @alexandr_wang and @Muse team on releasing such an epic open weights model! Artificial Analysis @ArtificialAnlys Meta has released Muse Spark Open, an 816B (42B active) open-weights model that scores 39 on the Artificial Analysis Intelligence Index, making it the #1 US open-weights model Muse Spark Open (xhigh) scores 22 points above Muse Glimmer (high, 17), Meta's 30B dense open-weights model from August. It is also ahead of NVIDIA's Nemotron 3.5 Super VL (26), the next highest-scoring US open-weights model, which launched today @AIatMeta shared access with us ahead of public release for benchmarking. Congratulations to @AIatMeta , @finkd , and @alexandr_wang on the release! Key Takeaways: ➤ Open weights at the level of Meta's August flagship. Muse Spark Open scores within a point of Muse Spark 1.2 (xhigh, 40). It is stronger on AutomationBench-AA and has a lower hallucination rate, but weaker on GDPval-AA v2.1, CritPt and AA-Omniscience accuracy. Within Meta's current lineup, it sits 13 points behind Muse Wonder (max, 52) and 17 points behind Muse Wonder Full (max, 56) ➤ Token-efficient, at a moderate cost per task. Muse Spark Open uses 40k output tokens per Intelligence Index task, the fewest among the nine highest-scoring open-weights models: e.g. 16% fewer than Muse Spark 1.2 (xhigh, 48k) and 43% fewer than GLM-5.3 (max, 71k), its closest Chinese peer by size. Together AI, Fireworks and Baseten offer it at launch for $1.25/$4.25 per 1M input/output tokens ($0.15 cached input). At that pricing it costs $0.84 per Intelligence Index task. That is less than Muse Spark 1.2 ($0.97) at the same per-token prices and less than half of GLM-5.3 (max, $2.01), but about three times DeepSeek V4.1 Flash (max, $0.27) ➤ A relative weakness in agentic work compared to Chinese open-weights leaders. Among the nine highest-scoring open-weights models, Muse Spark Open ranks last on the four agentic evaluations in the index but fourth on the other six combined, and it leads all open-weights models on AA-Omniscience (25). GLM-5.3 (max), the closest Chinese model by size, owes all of its 6-point lead to the agentic evaluations ➤ Self-hostable on a single node, or two Mac Studios. BF16 weights are ~1.6TB. Meta's NVFP4 checkpoint quantizes only the 256 experts, cutting the weights to ~530GB. Meta says this runs on four Blackwell Ultra GPUs or eight H200s, or on two 512GB M5 Ultra Mac Studios using MLX. The architecture uses 62 layers with grouped-query attention (16 KV heads), 8 of 256 experts active per token, and a 1.8B-parameter Perception Encoder for vision Other model details: ➤ Parameters: 816B total, 42B active (MoE) ➤ Context window: 1M tokens ➤ License: Muse Model License, a custom license that requires "Built with Muse Spark Open" attribution and a separate agreement with Meta for Model-as-a-Service businesses with over $20M in annual revenue ➤ Input modalities: text, image, video and audio. Output: text ➤ Availability: weights on Hugging Face (BF16 and NVFP4), with API access via Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI 🔗 View Quoted Tweet 💬 1 🔄 1 ❤️ 22 👀 974 📊 3 ⚡
