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Qwen3 VL 4B Instruct

AlibabaQwenOpen WeightApache 2.0 · Usage Commercial

Description

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.

Date de sortie
2025-10-14
Paramètres
4.0B
Longueur du contexte
—
Modalités
image, text

Radar de capacités

23
general
29
coding
37
reasoning
27
science
60
agents
90
multimodal

Classements

Domaine#RangScoreSource
Classement codage541
16.0
AA
Classement général529
26.0
AA
Classement multimodal138
26.0
LS
Science585
18.0
AA

Scores de benchmarks (LLM Stats)

(LLM Stats (zeroeval))

Chat

MM-MT-Bench7.50 / 100Aut.
IFEvalGoogle Research (2023)82.3%Aut.

Factuality

SimpleQA48.0%Aut.

General

MMLU77.2%Aut.
MLVU-M75.3%Aut.
BFCL-v363.3%Aut.
Include61.4%Aut.

Language

MMLU-Redux81.5%Aut.
MMLU-Pro67.1%Aut.
MMLU-ProX59.4%Aut.

Math

MathVista-Mini73.7%Aut.
LiveBench 2024112560.9%Aut.
MathVision51.6%Aut.
AIME 202546.6%Aut.
HMMT2530.7%Aut.
PolyMATH28.8%Aut.

Multimodal

VideoMMMU56.2%Aut.
OSWorld26.2%Aut.

Reasoning

CharXiv-D76.2%Aut.
SuperGPQA40.3%Aut.
CharXiv-R39.7%Aut.
LiveCodeBench v637.9%Aut.

Video

CharadesSTA55.5%Aut.

Vision

DocVQAtest95.3%Aut.
ScreenSpot94.0%Aut.
OCRBench88.1%Aut.
MMBench-V1.185.1%Aut.
AI2D84.1%Aut.
InfoVQAtest80.3%Aut.
CC-OCR76.2%Aut.
RealWorldQA70.9%Aut.
MMStar69.8%Aut.
MVBench68.9%Aut.
MMMU (val)67.4%Aut.
BLINK65.8%Aut.
MuirBench63.8%Aut.
OCRBench-V2 (en)63.7%Aut.
ScreenSpot Pro59.5%Aut.
OCRBench-V2 (zh)57.6%Aut.
Hallusion Bench57.6%Aut.
LVBench56.2%Aut.
MMMU-Pro53.2%Aut.
ODinW48.2%Aut.
ERQA41.3%Aut.

Writing

WritingBench82.5%Aut.

Indices d'évaluation AA

(Artificial Analysis)
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))
63.4
Gpqa(NYU + Cohere + Anthropic (2023))
37.1
Math Index(Artificial Analysis)
37.0
Aime 25(MAA (Mathematical Association of America))
37.0
Ifbench(Google Research (2023))
31.8
Livecodebench(UC Berkeley + MIT + Cornell (2024))
29.0
Tau2(Sierra + U Toronto + Vector Institute (2025))
23.4
Lcr(Artificial Analysis)
14.0
Intelligence Index(Artificial Analysis)
5.7
Hle(Center for AI Safety + Scale AI (2025))
3.6
Terminalbench Hard(Stanford × Laude Institute (2026))
0.0

Scores par catégorie LLM Stats

(LLM Stats (zeroeval))
Chat
4
Communication
4
Multimodal
90
Instruction Following
80
General
80
Grounding
80
Creativity
80
Text-to-image
80
Writing
80
Image To Text
70
Language
70
Legal
70
Structured Output
70
3d
70
Long Context
60
Math
60
Reasoning
60
Spatial Reasoning
60
Finance
60
Healthcare
60
Tool Calling
60
Video
60
Vision
60
Factuality
50
Physics
40
Agents
40
Chemistry
40
Economics
40

Tarification

Prix d'entréeGratuit
Prix de sortieGratuit
Prix mixte (3:1)Gratuit

Vitesse

Tokens/sec0.0
Délai du premier token0.00s
Temps de réponse0.00s

Classement des Prix par Fournisseur

Aucune donnée de fournisseur disponible

Sources externes