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LFM3-VL-2B

Liquid AI开源权重LFM Open License v1.0 · 商用许可

描述

LFM3-VL-2B is Liquid AI's open-weight 1.92B vision-language model (1.73B LFM3 language model + new 164M in-house conv/attention vision encoder + 25M projector), released Oct 27, 2026 as part of the LFM3 family. Inputs: text, images, and video (first LFM vision model with video, sampled at 2 fps) at native resolution; text output. Reasons step by step before answering (thinking), with spatial/embodied reasoning and grounding aimed at robotics, plus document/OCR/chart and GUI understanding. 128K context; 16 languages. Vendor reports a 71.4 average across 62 vision benchmarks (vs 57.6 for LFM2.5-VL-3B). Not recommended for coding, multi-turn conversations, or complex reasoning per the model card. Hugging Face: LiquidAI/LFM3-VL-2B (+ GGUF, MLX, ONNX). License: LFM Open License v1.0. Catalog benches are the thinking-mode column from the model card table. No catalog id (recorded here only): SEED-Bench image 78.6, MM-Vet v1 57.4, PerceptionBench 37.1 (catalog id is Moonshot's internal benchmark), HR-Bench 8K 76.2, ZoomBench 62.6, V*Bench 86.4, CV-Bench 88.8, LogicVista 60.4, RefCOCO-M 95.2, Point-Bench 73.8, MindCube tiny 56.5, SAT 80.2, Where2Place 44.9, OpenEQA 76.3, VSI-Bench 63.1, ScreenSpot-v2 desktop/mobile/web 94.6/92.2/89.9, MMMB 85.2, Multilingual MMBench 83.6, MM-ToolSandbox 42.5. No gateway id or price at time of adding, so no provider row. Sources: https://huggingface.co/LiquidAI/LFM3-VL-2B · https://www.liquid.ai/blog/lfm3.

发布日期
2026-10-27
参数规模
1.9B
上下文长度
—
支持模态
—

能力雷达图

70
general
0
coding
70
reasoning
43
science估算
49
agents
70
multimodal

缺少专门科学评测时,Science 由 LLM Stats 科学得分或推理能力估算。

排行榜排名

领域#排名分数来源
多模态榜129
27.0
LS

基准测试分数 (LLM Stats)

(LLM Stats (zeroeval))

General

MLVU-M71.3%自报

Math

MathVista-Mini84.2%自报
DynaMath79.2%自报
We-Math68.6%自报
MathVision51.6%自报

Multimodal

MM IF-Eval72.8%自报
VideoMME w/o sub.62.8%自报
VideoMMMU49.6%自报

Reasoning

ChartQAMasry et al. (2022)92.7%自报
CharXiv-D87.5%自报
CharXiv-R70.2%自报

Vision

DocVQADocVQA (2020)96.7%自报
CountBench0.93 / 100自报
RefCOCO-avg0.91 / 100自报
POPE88.7%自报
TextVQA87.9%自报
MME86.9%自报
OCRBench86.3%自报
InfoVQA86.2%自报
MMBench-V1.186.1%自报
EmbSpatialBench0.83 / 100自报
HR-Bench (4k)82.3%自报
RealWorldQA79.3%自报
MMStar74.4%自报
ActivityNet71.2%自报
BLINK71.1%自报
MVBench69.4%自报
VideoMME w sub.69.0%自报
MuirBench66.4%自报
Hallusion Bench65.6%自报
MotionBench64.0%自报
OCRBench-V2 (en)63.5%自报
RoboSpatialHome0.63 / 100自报
MMMU (val)55.7%自报
ScreenSpot Pro55.5%自报
MMVU52.9%自报
RefSpatialBench0.50 / 100自报
ERQA48.2%自报
MMMU-Pro45.7%自报
LVBench41.2%自报
SimpleVQA0.36 / 100自报

AA 评测指数

(Artificial Analysis)

暂无 AA 评测数据

LLM Stats 分类评分

(LLM Stats (zeroeval))
Safety
90
Structured Output
80
Embodied
80
Image To Text
70
Math
70
Multimodal
70
Reasoning
70
Spatial Reasoning
70
General
70
Grounding
70
3d
70
Vision
70
Video
60
Healthcare
50
Long Context
40

定价

暂无定价数据

速度

暂无速度数据

供应商价格排行

暂无提供商数据

外部链接