# LFM2.5-24B-A2B

Liquid AI released LFM2.5-24B-A2B, a 24B-parameter reasoning model with just 2B active parameters per token, built for fast private agents on-device. Versus its predecessor LFM2-24B-A2B, it extends context from 32,768 to 131,072 tokens, doubles the vocabulary from 65K to 128K, scales pretraining from 17T to 33T tokens, and becomes reasoning-only, emitting an explicit chain of thought before its answer. It matches or leads Qwen3.6-35B-A3B, which has about 50% more total and active parameters, on five of eight tool-use and agentic benchmarks, scoring 27.69 versus 8.72 on AutomationBench, and decodes 184 tokens/s on an Apple M5 Max and over 21K output tokens/s on a single NVIDIA B300 GPU.

Liquid AI released LFM2.5-24B-A2B, a 24B-parameter reasoning model with just 2B active parameters per token, built for fast private agents on-device. Versus its predecessor LFM2-24B-A2B, it extends context from 32,768 to 131,072 tokens, doubles the vocabulary from 65K to 128K, scales pretraining from 17T to 33T tokens, and becomes reasoning-only, emitting an explicit chain of thought before its answer. It matches or leads Qwen3.6-35B-A3B, which has about 50% more total and active parameters, on five of eight tool-use and agentic benchmarks, scoring 27.69 versus 8.72 on AutomationBench, and decodes 184 tokens/s on an Apple M5 Max and over 21K output tokens/s on a single NVIDIA B300 GPU.

Liquid AI released LFM2.5-24B-A2B, a reasoning agent model with 24B total parameters but only 2B active per token, built to run locally on device. It keeps the predecessor LFM2-24B-A2B architecture while growing context from 32K to 131K tokens and vocabulary from 65K to 128K, and it now emits an explicit chain of thought before answering. Across 16 benchmarks it competes with Qwen3.6-35B-A3B, which has roughly 50% more total and active parameters: 48.09 versus 38.19 on LiveClaw and 27.69 versus 8.72 on AutomationBench, while decoding 184 tokens/s on an M5 Max.

Liquid AI released LFM2.5-24B-A2B, a reasoning agent model with 24B total parameters but only 2B active per token, built to run locally on device. It keeps the predecessor LFM2-24B-A2B architecture while growing context from 32K to 131K tokens and vocabulary from 65K to 128K, and it now emits an explicit chain of thought before answering. Across 16 benchmarks it competes with Qwen3.6-35B-A3B, which has roughly 50% more total and active parameters: 48.09 versus 38.19 on LiveClaw and 27.69 versus 8.72 on AutomationBench, while decoding 184 tokens/s on an M5 Max.