Liquid AI releases open-weight LFM2.5-24B-A2B, a reasoning agent model with 2B active parameters that decodes 184 tokens/s on an M5 Max
A small mixture-of-experts model trained inside agent harnesses, meant for always-on agents running on your own hardware.
// Key points
- 24B total parameters with 2B active per token; the context window grows from 32K to 131K tokens and pre-training from about 17T to 33T tokens.
- It scores 48.09 on LiveClaw versus 38.19 for Qwen3.6-35B-A3B and 27.69 on AutomationBench versus 8.72, but trails it on SWE-Bench Verified (51.00 vs 66.20) and WebArena Verified (26.75 vs 37.62).
- Agentic RL was run inside OpenClaw, Hermes Agent, Pi and OpenCode; day-one support covers llama.cpp, MLX, vLLM, SGLang and ONNX.
- Both the instruct and base models are open-weight on Hugging Face.
Builder's takeRead the table, not the headline: it wins on tool calling and instruction following and loses clearly on coding and web tasks. That makes it a candidate for the local tool-routing and form-filling steps in PandaClaws, not for a coding agent. Given the 10-13 self-replication research, if you self-host it, keep the endpoint off the public internet.
// Background · from #Open models
Full timeline →- Oct 14 NVIDIA lines up software for RTX Spark laptops shipping October 16, including the Nemotron 3.5 Bolt 74B agent model on October 15
- Oct 13 Anthropic’s Frontier Red Team says small models may be months from self-replicating on IoT devices; a 9B model set up inference on 8 of 9 hardware types
- Oct 13 AWS shows decision models served with llama.cpp on SageMaker: a quantized 4B model scores 77.9% on 231 JevBench tasks at 0.352 s p50 on GPU