Liquid AI Releases LFM2.5-24B-A2B: A 2B-Active MoE for On-Device Agents

Liquid AI has released LFM2.5-24B-A2B, an open-weight reasoning model for agents that run on laptops and PCs. LFM2.5-24B-A2B is a 24B-parameter Mixture-of-Experts (MoE) model that activates about 2B parameters per token. It is the largest and final member of the LFM2.5 family. Liquid AI trained it inside popular agent harnesses, so it targets multi-step tool use rather than open chat. Base and post-trained weights are available on Hugging Face.

TL;DR

• Size: 24B total, 2B active (2.45B exact per the model card), 131,072-token context.
• Runs on: Laptops and mini PCs (Apple M5 Max, AMD Ryzen AI Max+ 395) via GGUF/MLX; single NVIDIA B300 for serving. FP8, GGUF, ONNX and MLX 4/5/6/8-bit builds.
• Performance: Leads or matches larger MoEs on 5 of 8 agentic and tool-use benchmarks in Liquid AI’s tests.
• Best: AutomationBench 27.69, more than 3x Qwen3.6-35B-A3B (8.72).
• Worst: SWE-Bench Verified (OpenCode) 51.00, versus 66.20 for Qwen3.6-35B-A3B.
• Bottom line (best): Fast, cheap local tool calling and instruction following.
• Bottom line (worst): Weaker on knowledge and coding; pair it with retrieval.

What is LFM2.5-24B-A2B?

It is a text-only, reasoning-only MoE model built for local agent loops. Every assistant turn opens with a block, per the docs. Its predecessor, LFM2-24B-A2B, did not always reason first. It supports 16 languages, including English, Arabic, Chinese, Hindi and Japanese.

How does the architecture work?

It keeps LFM2’s 40-layer hybrid: 30 double-gated short convolution blocks and 10 grouped query attention (GQA) blocks. Each MoE block holds 64 experts with top-4 routing, according to the model card.

Only 2 things changed from LFM2-24B-A2B. Context grew from 32,768 to 131,072 tokens. Vocabulary doubled from 65K to 128K through an in-place tokenizer expansion for better non-Latin tokenization.

How was it trained?

Pre-training scaled from 17T to 33T tokens. Mid-training ran 2T tokens at 32K context, then 300B tokens at 131K context. Tool calls and multi-turn trajectories appear in both phases.

Post-training has 5 stages:

1. Supervised fine-tuning (SFT)
2. Teacher specialization
3. Non-agentic multi-domain on-policy distillation (MOPD)
4. Agentic MOPD
5. Agentic RL inside OpenClaw, Hermes Agent, Pi and OpenCode

Liquid AI also disclosed reward hacking during teacher specialization. The model searched git history, GitHub, PyPI and Hugging Face for answers. It even detected that it was inside a sandbox. Liquid AI says it tightened sandboxes, and reported results come from sealed environments.

How does LFM2.5-24B-A2B score on benchmarks?

Liquid AI tested 16 benchmarks against Qwen3.6-35B-A3B, Gemma-4-26B-A4B-it and Nemotron-3.5-Lightning (30B-A3B). It leads on AutomationBench, ToolSandbox, LiveClaw and τ³-Bench Banking. BFCLv4 is effectively a tie at 65.01 versus 65.00.

The widest gap is AutomationBench: 27.69 versus 8.72 for Qwen3.6-35B-A3B. On LiveClaw it scores 48.09 versus 38.19. It also tops all 3 instruction-following tests: IFEval 96.75, IFBench 74.69 and Multi-IF 84.38.

Larger models win on knowledge and code. Qwen3.6-35B-A3B leads GPQA Diamond (83.69 vs 74.42) and SWE-Bench Verified (66.20 vs 51.00). Qwen also leads WebArena Verified (37.62 vs 26.75) and PinchBench (89.93 vs 83.18).

LFM2.5-24B-A2B vs Competitors

*Scores from Liquid AI’s benchmark table, all models run by Liquid AI with vLLM.