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LiquidAI released LFM2.5-2.6B on 2026-08-04

2026-08-04 · 14:00 UTC ·@liquidai ·1 min read

Brief

LFM2.5-2.6B is LiquidAI's new 2.5–2.6B-parameter agentic model released 2026-08-04 that runs entirely on-device (phones, laptops, PCs, robots), preserving local data and reducing marginal cost. It was pre-trained on ~34T tokens, uses a hybrid LFM2.5 architecture with 128K context and vocab, is single-GPU customizable, open-weight, and reports benchmarks that beat several larger models.

Why it matters

LiquidAI released LFM2.5-2.6B on 2026-08-04: an agentic on-device model that plans, calls tools, and executes multi-step tasks on phones, laptops, PCs, and robots; the release claims data never leaves the device and the marginal cost per run is essentially zero.

Key details

  • Model specs: pre-trained on ~34T tokens, LFM2.5 flagship hybrid architecture, context length 128K, vocabulary size 128K, described as offering 'balanced intelligence per watt', customizable on a single GPU, and provided under the LFM2 open-weight license.
  • Reported benchmarks show parity or better vs larger models: 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), while the model size is ~2.5–2.6B parameters.
Source evidence

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

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