Liquid AI
Massachusetts, 美国关于 Liquid AI
Liquid AI is a US AI company headquartered in Cambridge, Massachusetts. It builds efficient, general-purpose foundation models for organisations, publishing models and related resources publicly (e.g., on Hugging Face) alongside its main site.
性能
Liquid AI 的模型近期 Gateway 性能。
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此实验室的模型开始处理 Gateway 流量后，性能指标会显示在这里。
最新模型
包含可用性、上下文、价格和使用情况的近期发布。
Liquid AI open-weight 1.92B decision model built on LFM3-VL-2B. Answers classification, routing and scoring questions over text, images and video in one forward pass, without generating tokens.
Liquid AI open-weight 2.17B decision model built on LFM3-Omni-2B. Adds audio input to d1-2B for classification, routing and scoring in one forward pass, without generating tokens.
Liquid AI open-weight 1.73B text model with a 256K-token context window, built for local agents, tool calling and structured output. Reasons before answering by default.
Liquid AI open-weight 2B audio model for speech recognition, text-to-speech, speech translation and spoken tool calling in 18 languages, answering in text, speech or both.
Liquid AI's first omni model: a 2.17B open-weight model that understands text, images, video and audio and answers in text, speech or interleaved text and speech.
Liquid AI open-weight 1.92B vision-language model that reads text, images and video at native resolution, with a 128K-token context window and optional reasoning.
Liquid AI hosted decision model for classification, routing and scoring from text and images. Returns Noul, Choice and Score probabilities without generating tokens.
Liquid AI multimodal decision model for classification, routing and scoring from text and images, returning probabilities without generating tokens.
Liquid AI mixture-of-experts reasoning model with 24B total and 2B active parameters, a 131,072-token context window and tool calling.
Liquid AI decision model for classification, routing and scoring, returning calibrated probabilities without generating tokens.
D1 (Free) is a decision model from Liquid AI for classification, routing and scoring. It reads text and images together with typed yes/no, choice or score questions and returns probabilities in a single pass without generating any tokens.
LFM2.5-2.6B is a compact reasoning model from Liquid AI. It is suited for agent workflows, data extraction, RAG, and long-context processing. Liquid advises against using it for agentic coding or...
网络信息
官方资料、研究和社区渠道。
