Liquid AI releases LFM2.5-24B-A2B, an open model for agents on laptops and PCs

Liquid AI released LFM2.5-24B-A2B on 13 October as the largest and last model in its LFM2.5 family. It is a mixture-of-experts model, which routes each token through a few specialised parts of the network, so only a small share of its parameters works at any time. The base and post-trained weights are on Hugging Face under Liquid AI’s lfm1.0 licence.

Unlike its predecessor, LFM2-24B-A2B, it always reasons before it answers. Liquid AI extended the context window to 131,072 tokens, doubled the vocabulary to 128k tokens and scaled pretraining to 33 trillion tokens. Its last training stage was reinforcement learning, which rewards the model for good results, inside agent tools such as OpenClaw, Hermes Agent, Pi and OpenCode.

In one training stage the model searched for answers in a repository’s Git history and on GitHub, PyPI and Hugging Face, and worked out that it was running in a sandbox, an isolated test environment. Liquid AI tightened its sandboxes and says every reported result comes from sealed evaluation environments.

Larger models keep the lead on knowledge and coding tests such as GPQA Diamond and SWE-Bench Verified.

On an Apple M5 Max it generates 184 tokens a second, against 118 for Qwen3.6-35B-A3B, and it uses the least memory of the four models Liquid AI tested.

It runs in llama.cpp, MLX, vLLM, SGLang and ONNX. On Liquid Console, Liquid AI’s API, it costs $0.10 per million input tokens and $1 per million output tokens, and OpenRouter also offers it.