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Liquid AI Releases LFM2.5-24B-A2B On-Device Reasoning Model
On October 13, 2026, Liquid AI released the LFM2.5-24B-A2B on-device agent model. With 24B total parameters and only 2B activated per token, it is the largest and final model in the LFM2.5 family, and both the base and post-trained versions have open weights on Hugging Face and Playground. According to the official blog, it uses a pure reasoning mode with explicit chain-of-thought, expands the context window from 32,768 to 131,072 tokens, grows the vocabulary from 65K to 128K, and scales pretraining data from 17T to 33T tokens; it leads or ties Qwen3.6-35B-A3B on five of eight agent and tool-calling benchmarks, scoring 27.69 versus 8.72 on AutomationBench and 48.09 versus 38.19 on LiveClaw; it decodes at 184 tokens/s on an Apple M5 Max and outputs over 21K tokens/s under high concurrency on a single NVIDIA B300 GPU. On October 14, OpenRouter announced that the model is now available, saying it reasons before answering and targets agent and tool-calling scenarios.
On October 13, 2026, Liquid AI released the LFM2.5-24B-A2B on-device agent model. With 24B total parameters and only 2B activated per token, it is the largest and final model in the LFM2.5 family, and both the base and post-trained versions have open weights on Hugging Face and Playground. According to the official blog, it uses a pure reasoning mode with explicit chain-of-thought, expands the context window from 32,768 to 131,072 tokens, grows the vocabulary from 65K to 128K, and scales pretrai
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On October 13, 2026, Liquid AI released the LFM2.5-24B-A2B on-device agent model. With 24B total parameters and only 2B activated per token, it is the largest and final model in the LFM2.5 family, and both the base and post-trained versions have open weights on Hugging Face and Playground. According to the official blog, it uses a pure reasoning mode with explicit chain-of-thought, expands the context window from 32,768 to 131,072 tokens, grows the vocabulary from 65K to 128K, and scales pretraining data from 17T to 33T tokens; it leads or ties Qwen3.6-35B-A3B on five of eight agent and tool-calling benchmarks, scoring 27.69 versus 8.72 on AutomationBench and 48.09 versus 38.19 on LiveClaw; it decodes at 184 tokens/s on an Apple M5 Max and outputs over 21K tokens/s under high concurrency on a single NVIDIA B300 GPU. On October 14, OpenRouter announced that the model is now available, saying it reasons before answering and targets agent and tool-calling scenarios.
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View story:Liquid AI Releases LFM2.5-24B-A2B On-Device Reasoning Model
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