Laguna-M.1

模型介绍

Laguna M.1 is a 225B total parameter Mixture-of-Experts model with 23B activated parameters per token designed for agentic coding and long-horizon work. > [!NOTE] > For more details on how we trained this model, including our Model Factory approach, post-training recipe, async off-policy agent RL, and evaluations, check out our release blog post and technical report. * **Large sparse MoE for agentic coding**: Laguna M.1 is a 70-layer MoE transformer with 225B total parameters and 23B activated parameters per token * **High-capacity expert routing**: After 3 dense SwiGLU layers, Laguna M.1 uses 67 sparse MoE layers with 256 experts, top-k=16 routing and auxiliary-loss-free load balancing * **Global attention architecture**: Laguna M.1 uses global attention across all layers with 64 Q-heads, 8 KV-heads and softplus attention output gating * **Native reasoning support**: Interleaved thinking between tool calls with support for enabling and disabling thinking per-request * **Strong agentic benchmark performance**: Laguna M.1 is competitive with state-of-the-art open-weight and frontier models on SWE-bench Verified, SWE-bench Multilingual, SWE-Bench Pro and Terminal-Bench 2.0 * **Apache 2.0 license**: Use and modify freely for commercial and non-commercial purposes

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

常见问题

资料来源与可追溯记录

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。