Alibaba Cloud / Qwen Team model product
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.
Updated Aug 10, 2026. Default version: Qwen3 VL 8B Instruct
Structured fields from the published default version.
This profile uses the latest version under this unique model that has a calculated LLMBoard score. Arena and price are excluded.
Published benchmark records for the scored version Qwen3 VL 8B Instruct.
| BLINK | 0.7 | 4 | 13 | 75.0% | C | |
| MLVU-M | 0.8 | 4 | 8 | 57.1% | C | |
| OCRBench-V2 (zh) | 0.6 | 4 | 11 | 70.0% | C | |
| OCRBench-V2 (en) | 0.7 | 5 | 12 | 63.6% | C | |
| DocVQAtest | 1.0 | 6 | 11 | 50.0% | C | |
| OCRBench | 0.9 | 6 | 22 | 76.2% | C | |
| MMMU (val) | 0.7 | 7 | 11 | 40.0% | C | |
| ScreenSpot | 0.9 | 7 | 16 | 60.0% | C | |
| MuirBench | 0.6 | 8 | 11 | 30.0% | C | |
| ODinW | 0.4 | 8 | 16 | 53.3% | C | |
| CharadesSTA | 0.6 | 9 | 12 | 27.3% | C | |
| InfoVQAtest | 0.8 | 9 | 12 | 27.3% | C | |
| Multi-IF | 0.8 | 9 | 20 | 57.9% | C | |
| CC-OCR | 0.8 | 11 | 18 | 41.2% | C | |
| WritingBench | 0.8 | 11 | 15 | 28.6% | C | |
| OSWorld | 0.3 | 12 | 20 | 42.1% | C | |
| Hallusion Bench | 0.6 | 13 | 16 | 20.0% | C | |
| LiveBench 20241125 | 0.6 | 13 | 14 | 7.7% | C | |
| CharXiv-D | 0.8 | 14 | 16 | 13.3% | C | |
| MMBench-V1.1 | 0.8 | 14 | 18 | 23.5% | C | |
| MMStar | 0.7 | 14 | 22 | 38.1% | C | |
| MathVista-Mini | 0.8 | 15 | 23 | 36.4% | C | |
| MM-MT-Bench | 7.7 | 15 | 17 | 12.5% | C | |
| Video-MME | 0.7 | 15 | 17 | 12.5% | C | |
| LVBench | 0.6 | 16 | 24 | 34.8% | C | |
| BFCL-v3 | 0.7 | 17 | 19 | 11.1% | C | |
| MVBench | 0.7 | 17 | 17 | 0.0% | C | |
| AI2D | 0.9 | 19 | 32 | 41.9% | C | |
| ERQA | 0.5 | 19 | 23 | 18.2% | C | |
| ScreenSpot Pro | 0.5 | 19 | 24 | 21.7% | C | |
| PolyMATH | 0.3 | 20 | 23 | 13.6% | C | |
| RealWorldQA | 0.7 | 20 | 26 | 24.0% | C | |
| Include | 0.7 | 23 | 31 | 26.7% | C | |
| MMLU-ProX | 0.7 | 23 | 32 | 29.0% | C | |
| HMMT25 | 0.3 | 24 | 25 | 4.2% | C | |
| VideoMMMU | 0.7 | 24 | 26 | 8.0% | C | |
| MathVision | 0.5 | 25 | 32 | 22.6% | C | |
| SuperGPQA | 0.4 | 30 | 34 | 12.1% | C | |
| MMLU-Redux | 0.8 | 34 | 48 | 29.8% | C | |
| IFEval | 0.8 | 44 | 65 | 32.8% | C | |
| CharXiv-R | 0.5 | 45 | 47 | 4.3% | C | |
| LiveCodeBench v6 | 0.4 | 51 | 53 | 3.9% | C | |
| MMMU-Pro | 0.6 | 53 | 65 | 18.8% | C | |
| MMLU | 0.8 | 56 | 100 | 44.4% | C | |
| MMLU-Pro | 0.7 | 80 | 129 | 38.3% | C | |
| AIME 2025 | 0.5 | 107 | 114 | 6.2% | C |
Preference and agent-evaluation signals from published Arena datasets.
The default version has no published Arena rows, or its source alias has not been resolved.
Official vendor API PAYG pricing is summarized first. The table then lists individual provider offerings without treating their minimum as the official price.
| OpenRouter | qwen/qwen3-vl-8b-instruct | global | $0.117 | $0.455 | 262.1K |
Official prices use only the vendor's configured official Provider and positive standard USD PAYG rates. Third-party offers remain explicitly labeled.
All published versions linked to this unique model. The score columns identify the version used by the current overall ranking.
| Qwen3 VL 8B Instruct | 12.6 | 9B | 262.1K | 262.1K | No | Apache 2.0 |
A concise description based on the published model registry.
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.
Use the benchmark, Arena and pricing sections above as separate evidence. A missing field means the current data snapshot does not support that claim.
Data snapshot: 2026-08-07. Editorial model content is not available in the backend.
Open a comparison with the three ranked models immediately above and below this model.
Recommendations prioritize the same model type and family, then the closest published LLMBoard score.
Common questions about Qwen3 VL 8B.
Qwen3 VL 8B's default version was released on Sep 22, 2025.
No official standard PAYG price is currently available for Qwen3 VL 8B. The lowest tracked third-party offer starts at $0.117 input and $0.455 output via OpenRouter.
Qwen3 VL 8B is published under Alibaba Cloud / Qwen Team in the model registry.
The default version has a 262.1K token context window.
No. The default version is not marked as having publicly available weights.
1 published provider offerings are linked to the default version.
Nearby ranked alternatives include Pixtral 12B, Qwen2 7B, Gemma 4 E2B.