Liquid AI released LFM2.5-2.6B (shared by @ivanfioravanti) on 2026-08-04; the…

Brief

LFM2.5-2.6B is Liquid AI's newly released 2.6B agentic model (posted 2026-08-04) that runs fully on-device — phones, laptops, PCs, and robots — with 128K context and vocab, ~34T-token pretraining, and an open-weight LFM2 license. Liquid AI claims it matches or outperforms models up to ~4x larger on benchmarks like ToolSandbox, Multi-IF, and IFStruct.

Key details

LFM2.5-2.6B is agentic and runs entirely on-device (phones, laptops, PCs, robots), supports planning and tool calls for multi-step tasks, has 128K context length and 128K vocab, and is customizable on a single GPU with data remaining on-device.

Liquid AI reports benchmark wins vs much larger models: ToolSandbox 77.83 (vs Qwen3.5-9B 76.44), Multi-IF 80.07 (vs Gemma-4-E4B-it 77.35), and IFStruct 85.49 (vs Qwen3.5-9B 78.50), claiming parity or better performance vs models up to ~4x its size.

Look at the benchmarks of this 2.6B model!!!
Great release!

Liquid AI (@liquidai)

Today we release LFM2.5-2.6B, an agentic model that runs entirely on-device. It plans, calls tools, and works through multi-step tasks on phones, laptops, PCs, and robots. Data never leaves the device, and the marginal cost of each run is essentially zero.

> Pre-trained on ~34T tokens
> LFM2.5 flagship hybrid architecture
> Context length: 128K
> Vocab size: 128K
> balanced intelligence per watt
> customizable on a single GPU for any specialized task
> LFM2 open-weight license

Comparable or better scores compared to models up to nearly 4x its size:
> ToolSandbox 77.83, ahead of Qwen3.5-9B at 76.44
> Multi-IF 80.07, ahead of Gemma-4-E4B-it at 77.35
> IFStruct 85.49, ahead of Qwen3.5-9B at 78.50

🧵

— https://nitter.net/liquidai/status/2084640701669613906#m

Brief

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Key details