Meta 发布开源权重模型 Muse Spark Open：816B 总参数，登顶美国开源模型
Meta 放了个 816B 的开源模型，美国开源第一，跟自家旗舰就差 1 分。token 特别省，还能塞进两台 Mac Studio 自己跑，就是做 agent 有点弱。
Meta 发布开源权重模型 Muse Spark Open，总参数 816B、激活 42B，在 Artificial Analysis Intelligence Index 得分 39，成为排名第一的美国开源权重模型，仅比 Meta 8 月旗舰 Muse Spark 1.2（40 分）低 1 分。该模型在九个最高分开源模型中输出 token 最省，每任务约 40k 输出 token，比 GLM-5.3 少 43%。API 定价 $1.25/$4.25 每百万输入/输出 token，每任务成本 $0.84，不到 GLM-5.3（$2.01）的一半。短板在智能体评测：四项 agent 评测中在开源模型里垫底，正好是 GLM-5.3 领先 6 分的来源。NVFP4 版本约 530GB，可用四块 Blackwell Ultra 或两台 512GB M5 Ultra Mac Studio 自部署。
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
