Joseph Jacks (2026-08-05) claims @LiquidAI is the ONLY American open-weights…

Brief

Liquid AI released LFM2.5-2.6B, an on-device agentic model (2.5–2.6B) pre-trained on ~34T tokens with 128K context and 128K vocab, licensed open-weight under the LFM2.5 hybrid architecture. The company claims single-GPU customizability, zero data-leakage on phones/laptops/robots, and benchmark leads (ToolSandbox 77.83; Multi-IF 80.07; IFStruct 85.49). Joseph Jacks says LiquidAI is the only U.S. open-weights frontier lab ahead of China.

Key details

Liquid AI released LFM2.5-2.6B: an agentic, entirely on-device model (2.5–2.6B) pre-trained on ~34T tokens with LFM2.5 hybrid architecture, 128K context length, 128K vocab, single-GPU customizability, LFM2 open-weight license; reported benchmarks: ToolSandbox 77.83 (vs Qwen3.5-9B 76.44), Multi-IF 80.07 (vs Gemma-4-E4B-it 77.35), IFStruct 85.49 (vs Qwen3.5-9B 78.50).

Spectacular. Seems @LiquidAI is the ONLY American open weights frontier AI lab staying ahead of China. Very proud of this fact. Bravo @raminmh.

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

Why it matters

Key details