Run fast, private agents on-device with LFM2.5-24B-A2B

LFM2.5-24B-A2B is a 24B-parameter reasoning model with just 2B active parameters, built for fast, private agents on-device. It thinks before it answers, calls tools, and works through multi-step tasks inside popular agent harnesses. Because only 2B parameters are active per token, each reasoning token is cheap, so you can run agents continuously on your own hardware.

- Best-in-class agent: Competitive with Qwen3.6-35B-A3B, a model with ~50% more total and active parameters, on agentic and tool-use tasks, scoring 48.09 on LiveClaw versus 38.19.

- Reasoning at MoE speed: Always thinks before it answers. With only 2B active parameters per token, each reasoning token is cheap.

- Efficient inference: 184 tok/s on an Apple M5 Max and up to 21K output tok/s on a single NVIDIA B300 GPU.

How we built a reasoning agent with 2B active parameters

LFM2.5-24B-A2B builds on LFM2-24B-A2B and keeps its architecture: 40 layers, 64 experts per MoE block with top-4 routing, and the same hybrid of gated short convolutions and GQA. The context window grows from 32K to 131K tokens, and an in-place tokenizer expansion doubles the vocabulary from 65K to 128K tokens for better non-Latin tokenization. Unlike its predecessor, it is a reasoning-only model that produces an explicit chain of thought before its final answer.

Pre-training was scaled up from ~17T to ~33T tokens. Mid-training then adds 2T tokens at 32K context, weighted toward reasoning, math, code, instruction following, and multilingual text, and 300B tokens at 131K context, weighted toward long-context data. Tool calls and multi-turn trajectories appear throughout both phases. Post-training turns the base model into an agent in five stages:

- Supervised fine-tuning (SFT)

- Teacher specialization

- Multi-domain on-policy distillation (MOPD), non-agentic: distill the specialist teachers on non-agentic tasks.

- MOPD, agentic: a second distillation stage focused on agentic tasks.

- Agentic reinforcement learning (Agentic RL): train the model inside agent harnesses, such as OpenClaw , Hermes Agent , Pi , and OpenCode .

Benchmark results

We evaluated LFM2.5-24B-A2B against Qwen3.6-35B-A3B, Gemma-4-26B-A4B-it, and Nemotron-3.5-Lightning on 16 benchmarks covering tool use and agentic workflows, knowledge, instruction following, math, and coding. It is the smallest model in the group, yet it is competitive with MoEs with ~50% more total and active parameters.

Benchmark LFM2.5-24B-A2B Qwen3.6-35B-A3B Gemma-4-26B-A4B-it Nemotron-3.5-Lightning (30B-A3B)

AutomationBench 27.69 8.72 5.46 2.61

BFCLv4 65.01 65.00 57.09 53.78

ToolSandbox 80.71 75.02 72.09 75.13

LiveClaw 48.09 38.19 41.25 37.72

PinchBench 83.18 89.93 77.12 84.20

τ³-Bench Banking 8.04 7.63 4.54 7.01

WebArena Verified 26.75 37.62 25.62 28.69

GPQA Diamond 74.42 83.69 74.90 72.42

AA-Omniscience -24.90 -24.35 -59.39 -3.58