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On 2026-08-04 Liquid AI released LFM2.5-2.6B, an agentic on-device model…

2026-08-04 · 14:16 UTC ·@tugot17 ·1 min read

Brief

Liquid AI released LFM2.5-2.6B on 2026-08-04, an on-device agentic model pre-trained on ~34T tokens with LFM2.5 hybrid architecture, 128K context and vocab, and an open-weight license. The model runs on phones, laptops, PCs and robots (data stays local), is single-GPU-customizable, and claims benchmark wins over larger models with near-zero marginal run cost.

Why it matters

On 2026-08-04 Liquid AI released LFM2.5-2.6B, an agentic on-device model pre-trained on ~34T tokens using the LFM2.5 hybrid architecture, with a 128K context length, 128K vocabulary, and distributed under the LFM2 open-weight license.

Key details

  • LFM2.5-2.6B runs entirely on phones, laptops, PCs and robots (data never leaves the device), is customizable on a single GPU, and is promoted as having essentially zero marginal cost per run.
  • Liquid AI reports benchmark leads versus 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).
Source evidence

already fast and will get faster 😏

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