Saltar al contenido principal

Qwen3 VL 4B (Reasoning)

AlibabaQwenOpen WeightApache 2.0 · Uso Comercial

Descripción

Qwen3-VL is a large multimodal model that unifies vision, language, and reasoning to achieve human-level perception and cognition across text, images, and video. Built on a 235B-parameter architecture, it integrates early joint training of visual and textual modalities for strong language grounding. The model supports up to a 1 million-token context window and excels at visual understanding, spatial reasoning, long video comprehension, and tool-based interaction. It can generate code from images, perform precise 2D/3D object grounding, and operate digital interfaces like a visual agent. The “Instruct” version rivals Gemini 2.5 Pro in perception benchmarks, while the “Thinking” version leads in multimodal reasoning and STEM tasks. With multilingual OCR, creative writing, and fine-grained scene interpretation, Qwen3-VL establishes a new open-source frontier for integrated vision-language intelligence.

Fecha de lanzamiento
2025-10-14
Parámetros
4.0B
Longitud del contexto
Modalidades
image, text

Radar de capacidades

26
general
29
coding
30
reasoning
29
science
70
agents
100
multimodal

Rankings

Dominio#PosiciónPuntuaciónFuente
Capacidad agéntica110
37.0
LS
Ranking de codificación412
22.0
AA
Ranking general429
30.0
AA
Ranking multimodal85
22.0
LS
Ciencia464
27.0
AA

Puntuaciones de benchmarks (LLM Stats)

(LLM Stats (zeroeval))

3d

BLINK63.4%Aut.

Agents

BFCL-v367.3%Aut.
OSWorld31.4%Aut.

Biology

GPQANYU + Cohere + Anthropic (2023)64.1%Aut.

Chemistry

SuperGPQA46.8%Aut.

Communication

MM-MT-Bench7.70 / 100Aut.
WritingBench84.0%Aut.
Multi-IF73.6%Aut.

Creativity

Creative Writing v376.1%Aut.
Arena-Hard v236.8%Aut.

Finance

MMLU81.5%Aut.
MMLU-Pro73.6%Aut.
MMLU-ProX65.0%Aut.

General

MMLU-Redux86.0%Aut.
IFEvalGoogle Research (2023)82.6%Aut.
MLVU-M75.7%Aut.
MMStar73.2%Aut.
MMMU (val)70.8%Aut.
LiveBench 2024112568.4%Aut.
Include64.6%Aut.
MMMU-Pro57.0%Aut.
LiveCodeBench v651.3%Aut.

Grounding

ScreenSpot92.9%Aut.
ScreenSpot Pro49.2%Aut.

Healthcare

VideoMMMU69.4%Aut.

Image To Text

OCRBench80.8%Aut.
OCRBench-V2 (en)61.8%Aut.
OCRBench-V2 (zh)55.8%Aut.

Language

CharadesSTA59.0%Aut.

Long Context

LVBench53.5%Aut.

Math

MathVista-Mini79.5%Aut.
AIME 202574.5%Aut.
MathVision60.0%Aut.
HMMT2553.1%Aut.
PolyMATH44.6%Aut.

Multimodal

DocVQAtest94.2%Aut.
MMBench-V1.186.7%Aut.
AI2D84.9%Aut.
CharXiv-D83.9%Aut.
InfoVQAtest83.0%Aut.
MuirBench75.0%Aut.
CC-OCR73.8%Aut.
MVBench69.3%Aut.
CharXiv-R50.3%Aut.

Reasoning

Hallusion Bench64.1%Aut.
ERQA47.3%Aut.

Spatial Reasoning

RealWorldQA73.2%Aut.

Vision

ODinW39.4%Aut.

Índices de evaluación AA

(Artificial Analysis)
Math Index(Artificial Analysis)
25.7
Intelligence Index(Artificial Analysis)
7.7
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))
0.7
Gpqa(NYU + Cohere + Anthropic (2023))
0.5
Ifbench(Google Research (2023))
0.4
Livecodebench(UC Berkeley + MIT + Cornell (2024))
0.3
Aime 25(MAA (Mathematical Association of America))
0.3
Lcr(Artificial Analysis)
0.2
Scicode(UIUC + Argonne National Lab (2024))
0.2
Tau2(Sierra + U Toronto + Vector Institute (2025))
0.2
Hle(Center for AI Safety + Scale AI (2025))
0.0
Terminalbench Hard(Stanford × Laude Institute (2026))
0.0

Puntuaciones por categoría LLM Stats

(LLM Stats (zeroeval))
Communication
3
Multimodal
100
Structured Output
80
Instruction Following
80
General
80
Legal
70
Math
70
Reasoning
70
Image To Text
70
Language
70
Finance
70
Grounding
70
Healthcare
70
Creativity
70
Text-to-image
70
Tool Calling
70
Vision
70
Writing
70
Physics
60
Spatial Reasoning
60
3d
60
Biology
60
Chemistry
60
Video
60
Long Context
50
Agents
50
Economics
50

Precios

Precio de entradaGratis
Precio de salidaGratis
Precio mixto (3:1)Gratis

Velocidad

Tokens/seg0.0
Retraso del primer token0.00s
Tiempo hasta la respuesta0.00s

Ranking de Precios por Proveedor

Ranking de Precios por Proveedor

1 proveedores

ProveedorEntradaSalida
1DeepInfra
$0
$0

Comparar precios entre diferentes proveedores de API para este modelo.

Fuentes externas