Leaderboard / Liquid AI

LFM3-Omni 2B

Liquid AI · LFM3 · released 2026-10-21 · data checked 2026-11-01

LFM3-Omni 2B is a 2.2B-parameter dense model from Liquid AI (United States), released 2026-10-21 under the LFM Open License license. It doesn't have enough reported benchmarks for an index score yet. It needs about 2 GB of memory at 4-bit with an 8K-token context.

Takes text, image, audio and video; answers in text or speech.

Small LM Index
—
Needs 2 core benchmarks (has 0)
Parameters
2.2B
Dense
Price per 1M tokens
—
No hosted API on OpenRouter; self-host
Context window
128K
tokens

Benchmarks

Each bar shows this model's score. The tick marks the best tiny model on that benchmark. Sources: 1 reported by Liquid AI. How we label sources.

MMMU-Pro Lab-reported Vision27.3
Class best: Qwen3.5 4B 65.4

Specifications

Lab
Liquid AI (US · United States)
Architecture
Dense transformer
Total parameters
2.2B
Active parameters
All (dense)
Size class
Tiny
Context window
128K tokens
Input modalities
text, image, audio, video
Reasoning
Instruct (no thinking)
License
LFM Open License
Openness
Open weights
Memory to run
~2 GB at 4-bit, 8K context (more sizes)
Hugging Face
11.7K downloads (30 days) · 324 likes

Compare with

Sources

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Run it locally

Uses LiquidAI/LFM3-Omni-2B-GGUF (Q4_K_M). Swap the tag for another size: BF16, F16, Q4_0, Q4_K_M, Q8_0.

Ollama
ollama run hf.co/LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M
llama.cpp
llama-server -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M
Then open http://localhost:8080 for a chat window.
MLX (Mac)
pip install -U mlx-lm
mlx_lm.chat --model LiquidAI/LFM3-Omni-2B-MLX-4bit
Text only; for images use mlx-vlm.
vLLM (GPU server)
vllm serve LiquidAI/LFM3-Omni-2B
Full-precision weights, for a server GPU.

Open in LM Studio

Memory needed: weights, context cache and runtime buffers.

Quantization4K context32K context128K context
16-bit F164.9 GB5.3 GB6.5 GB
8-bit Q8_02.9 GB3.3 GB4.4 GB
4-bit Q4_K_M1.9 GB2.3 GB3.4 GB

Check it against your GPU, Mac or phone →