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Liquid AI released LFM2.5-2.6B on 2026-08-04

2026-08-04 · 17:54 UTC ·@songdng ·1 min read

Brief

LFM2.5-2.6B is Liquid AI's new 2.5–2.6B agentic model (released 2026-08-04) that runs fully on-device (<1.7GB Q4 phone), was pre-trained on ~34T tokens with a LFM2 hybrid architecture, and supports 128K context and 128K vocab. It used agentic RL (OpenClaw, Hermes), is open-weight, and reports higher benchmark scores than several larger models.

Why it matters

Liquid AI released LFM2.5-2.6B on 2026-08-04: a 2.5–2.6B-parameter agentic model that runs entirely on-device (<1.7GB Q4 phone) across phones, laptops, PCs, and robots; data never leaves the device and marginal cost per run is essentially zero.

Key details

  • Model details: pre-trained on ~34T tokens using the LFM2 flagship hybrid architecture; context length 128K, vocabulary size 128K; customizable on a single GPU; released under the LFM2 open-weight license; post-training agentic RL used OpenClaw and Hermes harnesses.
  • Benchmarks reported: 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 superiority to models up to ~4x larger.
Source evidence

LFM2.5-2.6B is OUT!

I LOVED post-training this model, especially working on agentic RL. An exciting part was training inside real agent harnesses like OpenClaw and Hermes. <1.7GB Q4 runs on a phone.

Incredibly proud of the team, can't wait to see what people build with it 🥳

Video

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