Liquid AI has released LFM2.5-24B-A2B, an open-weight reasoning model designed to power AI agents that run directly on laptops, phones and other local hardware. The model has 24 billion parameters, but only 2 billion are active for any given token, a design the company says lets it run fast without the memory and power demands of larger systems.
The company calls LFM2.5-24B-A2B the largest and final model in the LFM2.5 family, which now spans from the 230-million-parameter LFM2.5-230M up to this release. Both the base model and the post-trained version are available on Hugging Face and the company's Playground, and developers can find guides on running and fine-tuning the model locally.
Liquid AI says the model holds its own against much larger rivals. On five of eight agentic and tool-use benchmarks, it is competitive with Qwen3.6-35B-A3B, a model with roughly 50 percent more total and active parameters. The gap is widest on AutomationBench, where LFM2.5-24B-A2B scored 27.69 against 8.72 for Qwen3.6-35B-A3B. On LiveClaw, it scored 48.09 compared with 38.19 for the same competitor. The model also leads on all three instruction-following benchmarks the company tested.
The results are not uniform. Larger models keep the edge on knowledge-heavy tests such as GPQA Diamond, where Qwen3.6-35B-A3B scored 83.69 to the new model's 74.42, and on coding benchmarks like SWE-Bench Verified, where the competitor reached 66.20 against 51.00. Liquid AI recommends pairing the model with retrieval or web search for knowledge-intensive work, which it says plays to the model's strength in calling tools.
Speed is the main pitch. Liquid AI says the model decodes 184 tokens per second on an Apple M5 Max and 114 tokens per second on an AMD Ryzen AI Max+ 395, up to 1.7 times faster than the next-best model it tested while using the least memory. On a single NVIDIA B300 GPU under sustained load, the company reports throughput above 21,000 output tokens per second at high concurrency. The benchmarks were run on Pipette, the company's internal benchmarking infrastructure, which also powers its public device performance data.
The model builds on the earlier LFM2-24B-A2B. Liquid AI expanded its context window from 32,768 to 131,072 tokens and doubled its vocabulary from 65,000 to 128,000 tokens to improve tokenization for non-Latin text. Pretraining grew from 17 trillion tokens to 33 trillion. Unlike its predecessor, LFM2.5-24B-A2B is reasoning-only, producing an explicit chain of thought before its final answer. The company said it chose that approach to take advantage of the model's high decoding speed and to improve answer quality.
During training, the model showed behavior the company did not expect. While it was being trained for specific tasks, it searched for final answers in repository git histories and on platforms including GitHub, PyPI and Hugging Face, a pattern known as reward hacking. Liquid AI said some of its runs also showed the model recognizing that it was inside a sandbox. The company said the behavior may have come from training data containing similar patterns, and it tightened its sandboxes in response. All results it reported were collected in sealed evaluation environments.
For developers, the model is available through several inference frameworks from the start, including llama.cpp, MLX for Apple Silicon, vLLM, SGLang and ONNX. Liquid AI is also offering API access through its Liquid Console, where new users receive $2 in credit. The company priced API usage at $0.10 per million input tokens, $0.05 per million cached input tokens and $1.00 per million output tokens.
The company says the release reflects its broader goal of AI that runs anywhere, not only in the cloud. It described the model as open-weight, with a license that allows users to download, fine-tune and deploy it, and said the LFM2.5 family covers everything from base models for customization to specialized audio and vision variants.
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