Liquid

Access 8 Liquid models through the OpenRouter unified API including LFM2-24B-A2B, LFM2.5-1.2B-Thinking, and LFM2.5-1.2B-Instruct. Compare pricing, context windows, benchmarks, and capabilities between different Liquid models.

Liquid tokens processed on OpenRouter

LiquidAI: LFM2-24B-A2BLFM2-24B-A2B
LFM2-24B-A2B is the largest model in the LFM2 family of hybrid architectures designed for efficient on-device deployment. Built as a 24B parameter Mixture-of-Experts model with only 2B active parameters per token, it delivers high-quality generation while maintaining low inference costs. The model fits within 32 GB of RAM, making it practical to run on consumer laptops and desktops without sacrificing capability.
by liquidFeb 25, 2026128K context
LiquidAI: LFM2.5-1.2B-ThinkingLFM2.5-1.2B-Thinking
LFM2.5-1.2B-Thinking is a lightweight reasoning-focused model optimized for agentic tasks, data extraction, and RAG—while still running comfortably on edge devices. It supports long context (up to 32K tokens) and is designed to provide higher-quality “thinking” responses in a small 1.2B model.
by liquidJan 20, 202633K context

Liquid

Access 8 Liquid models through the OpenRouter unified API including LFM2-24B-A2B, LFM2.5-1.2B-Thinking, and LFM2.5-1.2B-Instruct. Compare pricing, context windows, benchmarks, and capabilities between different Liquid models.

Liquid tokens processed on OpenRouter

LiquidAI: LFM2.5-1.2B-InstructLFM2.5-1.2B-Instruct

LFM2.5-1.2B-Instruct is a compact, high-performance instruction-tuned model built for fast on-device AI. It delivers strong chat quality in a 1.2B parameter footprint, with efficient edge inference and broad runtime support.

by liquidJan 20, 202633K context

LiquidAI: LFM2-8B-A1BLFM2-8B-A1B

LFM2-8B-A1B is an efficient on-device Mixture-of-Experts (MoE) model from Liquid AI’s LFM2 family, built for fast, high-quality inference on edge hardware. It uses 8.3B total parameters with only ~1.5B active per token, delivering strong performance while keeping compute and memory usage low—making it ideal for phones, tablets, and laptops.

by liquidOct 20, 20258K context

LiquidAI: LFM2-2.6BLFM2-2.6B

LFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency.

by liquidOct 20, 202533K context

Liquid: LFM 7BLFM 7B

LFM-7B, a new best-in-class language model. LFM-7B is designed for exceptional chat capabilities, including languages like Arabic and Japanese. Powered by the Liquid Foundation Model (LFM) architecture, it exhibits unique features like low memory footprint and fast inference speed. LFM-7B is the world’s best-in-class multilingual language model in English, Arabic, and Japanese. See the launch announcement for benchmarks and more info.

by liquidJan 25, 202533K context

Liquid: LFM 3BLFM 3B

Liquid's LFM 3B delivers incredible performance for its size. It positions itself as first place among 3B parameter transformers, hybrids, and RNN models It is also on par with Phi-3.5-mini on multiple benchmarks, while being 18.4% smaller. LFM-3B is the ideal choice for mobile and other edge text-based applications. See the launch announcement for benchmarks and more info.

by liquidJan 25, 202533K context

Liquid: LFM 40B MoELFM 40B MoE

Liquid's 40.3B Mixture of Experts (MoE) model. Liquid Foundation Models (LFMs) are large neural networks built with computational units rooted in dynamic systems. LFMs are general-purpose AI models that can be used to model any kind of sequential data, including video, audio, text, time series, and signals. See the launch announcement for benchmarks and more info.

by liquidSep 30, 202433K context