[whatmodel.sh](../../README.md) / Qwen3-VL 30B-A3B Instruct # Qwen3-VL 30B-A3B Instruct Alibaba · released 2025-09 · 31.1B params (3B active) · apache-2.0 **score 57.5** ±0.7 · #235 of 732 · from 26 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Qwen3-VL 30B-A3B Instruct among every model on that task or test, worst to best.}

task                vs the others          rank  counts  tests
agent tasks         ▁▁▃▆●▄▅▅▄▄▂▁   #195 of 313*    2.1%      1
coding              ▁▁▁▃▅●█▇▅▄▃▁   #319 of 469*    4.2%      1
chat & writing      ▁▁▁▃▆▄▆█●▇▇▃   #178 of 402*    2.8%      1
math                ▁▁▃▅█▆●█▄▂▁▁   #168 of 431*    9.5%      2
vision & documents  ▁▁▂▂▁▃●▂▆▇█▄    #103 of 153     47%     13

agent tasks · #195 of 313 · 2.1% of its score

Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [DeepSeek R1 0528](deepseek-r1-0528.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Mini](jamba-1-5-mini.md), [Mellum2 12B-A2.5B Instruct](mellum2-12b-a2-5b-instruct.md) here, though they rank lower overall.

test             result  vs the others in its run  difficulty        counts  why
BFCL v3 (maker)   66.3%  █▄··▄█·●▄█▄█ beats 7/13   medium · avg 59%    2.1%  blunt · at its level

coding · #319 of 469 · 4.2% of its score

Beats [Solar Open 100B (102B-A12B)](solar-open-100b.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Code Tulu 2 34B](code-tulu-2-34b.md) here, though they rank lower overall.

test                                result  vs the others in its run  difficulty      counts  why
LiveCodeBench v6 (window unspecif…   42.6%  ▃▅█●▅▃▅···▃█ beats 6/16   hard · avg 35%    4.2%  at its level

chat & writing · #178 of 402 · 2.8% of its score

Beats [MiniCPM5 2B](minicpm5-2b.md), [Seed-OSS 36B Instruct](seed-oss-36b.md), [GPT-5 nano](gpt-5-nano.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md) here, though they rank lower overall.

test                                result  vs the others in its run  difficulty      counts  why
IFEval (aggregation unspecified) …   85.8%  ▃··▃···██▅●▃ beats 10/11  easy · avg 77%    2.8%  typical

math · #168 of 431 · 9.5% of its score

Beats [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md), [MiniMax-M2.7](minimax-m2-7.md), [MiniMax-M2.1](minimax-m2-1.md) here, though they rank higher overall. Trails [Olmo 3 7B Think](olmo-3-7b-think.md), [Nemotron Nano 9B v2](nemotron-nano-9b-v2.md), [Qwen3-VL 4B Thinking](qwen3-vl-4b-thinking.md) here, though they rank lower overall.

test                          result  vs the others in its run  difficulty        counts  why
MathVista (testmini) (maker)   80.1%  ▃··▃▅·▃█▅●▅█ beats 11/16  medium · avg 69%    5.0%  at its level
AIME 2025 (maker)              69.3%  ▃·▃▃█··▃●▃·█ beats 8/12   medium · avg 44%    4.5%  sharp · at its level

vision & documents · #103 of 153 · 47% of its score

Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md), [Magistral Small 1.2](magistral-small-2509.md) here, though they rank higher overall. Trails [Llama 4 Maverick (402B-A17B)](llama-4-maverick.md), [Qwen3-VL 8B Thinking](qwen3-vl-8b-thinking.md) here, though they rank lower overall.

test                                result  vs the others in its run  difficulty           counts  why
CharXiv (reasoning questions) (ma…   48.9%  ▄▂▄●▄▄▆··▄█▂ beats 6/21   medium · avg 38%        10%  at its level
MMMU (val) (maker)                   74.2%  ▂···▆▂▂▂▄▄●█ beats 12/16  medium · avg 61%       7.5%  at its level
CharXiv descriptive questions (ma…   85.5%  ▄··▂▂▂·▂●▄▂█ beats 7/14   easy · avg 74%         5.9%  typical
MMStar (maker)                       72.1%  ▂··▄▂▂·▄●▂▄▆ beats 10/16  medium · avg 65%       5.0%  at its level
MathVista (testmini) (maker)         80.1%  ▃··▃▅·▃█▅●▅█ beats 11/16  medium · avg 69%       5.0%  at its level
MMMU Pro (maker)                     60.4%  ▃·█·▅▃▃▃▅●▃█ beats 12/16  medium · avg 46%       4.5%  at its level
HallusionBench (maker)               61.5%  ▃·▃·▅▃▅▅▃●█▅ beats 10/16  medium · avg 54%       2.1%  blunt · at its level
ChartQA (maker)                      86.8%  ▂··▂·▂▃·●▂▆█ beats 6/17   easy · avg 85%         1.5%  blunt
RealWorldQA (maker)                  73.7%  ▅▃·▃▅▅·●·▃▅█ beats 10/16  easy · avg 70%         1.4%  blunt
DocVQA (maker)                       95.0%  ▃··▅▃▅●█▃█▅▅ beats 6/17   very easy · avg 94%    1.3%  blunt · maxed out
ScreenSpot-Pro (maker)               60.5%  ▂····▂▄··▂█● beats 9/11   medium · avg 46%       1.2%  blunt · at its level
CC-OCR (maker)                       80.7%  ▃··▃▅·▅▅·█●▃ beats 12/13  easy · avg 74%         1.0%  blunt
OCRBench (maker)                     90.3%  ▅█·▃▃█▃▅▅●·▃ beats 15/16  easy · avg 84%         0.5%  blunt

other tests (9)

test                                result  vs the others in its run  difficulty        counts  why
GPQA (subset unspecified) (maker)    70.4%  ▂····▂·▂▂█●█ beats 10/14  hard · avg 49%      8.8%  sharp · at its level
MMLU-Pro (maker)                     77.8%  ▄·▄·▄▄▄▄█▄●█ beats 9/12   medium · avg 63%    6.7%  at its level
MMLU-Redux (maker)                   88.4%  ▃··▃·▃·█▃●▃▅ beats 7/11   easy · avg 79%      5.0%  typical
WebCompass (overall)                 28.1%  ●·▃▃▃···▃·▃█ beats 0/9    hard · avg 18%      4.8%  at its level
SuperGPQA (maker)                    53.1%  ▄··▄·▄▄▄█▄●█ beats 8/11   medium · avg 37%    4.2%  at its level
AndroidWorld (maker)                 54.3%  ▃··▃▅▃▃●▃▃·█ beats 6/11   medium · avg 43%    3.1%  at its level
LVBench (maker)                      62.5%  ▅··▃·▅▃▃·●·▃ beats 7/10   hard · avg 51%      3.0%  at its level
Video-MME (without subtitles) (ma…   74.5%  ▃··█··▅█▃●▅▃ beats 10/13  medium · avg 66%    2.8%  blunt · at its level
MVBench (maker)                      72.3%  ▂▂▂·▂█··●▄·▂ beats 9/13   medium · avg 67%    1.2%  blunt · at its level

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md)

source                                    results  share of the score
Qwen (submitted / maker-reported)              25   95% ▇▇▇▇▇▇▇▇▇▇▇
WebCompass: Towards Multimodal Web Codi…        1    5% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 57.5

_{Licences: WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Models ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/))} ## How to run it ### Through an API api (Alibaba Cloud Model Studio) · $0.35/M blended · $0.2 in / $0.8 out per 1M tokens · [alibabacloud.com](https://www.alibabacloud.com/help/en/model-studio/model-pricing) _{[price source](https://www.alibabacloud.com/help/en/model-studio/model-pricing): USD per 1M tokens, Alibaba Cloud Model Studio, Singapore (International deployment scope), API model qwen3-vl-30b-a3b-instruct (Non-Thinking mode only): input $0.2, output $0.8. Chinese-mainland/Global (US) deployments priced differently and not used, following the International convention of other Alibaba rows; free quota, batch and context-cache discounts excluded. Observed 2026-10-09. · [context source](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct/blob/9c4b90e1e4ba969fd3b5378b57d966d725f1b86c/README.md): Official Qwen model card: 'Native 256K context, expandable to 1M'. Observed 2026-10-09.} ### llama.cpp (recommended) 1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) ```sh # macOS / Linux curl -LsSf https://llama.app/install.sh | sh # Windows irm https://llama.app/install.ps1 | iex ``` 2. Run Qwen3-VL 30B-A3B Instruct (Q4_K_M, needs ~18.6 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF/tree/f54435e6cc31258f04b0969105c3f6badb197931)) ```sh llama cli -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M ``` Other versions: Q8_0 (~32.5 GB) `llama cli -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q8_0` ### LM Studio 1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download) 2. Run Qwen3-VL 30B-A3B Instruct (Q4_K_M, needs ~18.6 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF/tree/f54435e6cc31258f04b0969105c3f6badb197931)) ```sh lms get https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: Q8_0 (~32.5 GB) `lms get https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF@Q8_0 -y` · 4bit (~18.3 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen3-VL-30B-A3B-Instruct-MLX-4bit -y` · 8bit (~33.5 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen3-VL-30B-A3B-Instruct-MLX-8bit -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run Qwen3-VL 30B-A3B Instruct (Q4_K_M, needs ~20 GB, from [ollama.com](https://ollama.com/library/qwen3-vl/tags)) ```sh ollama run qwen3-vl:30b-a3b-instruct ``` Other versions: Q8_0 (~34 GB) `ollama run qwen3-vl:30b-a3b-instruct-q8_0` ### MLX (Mac) 1. Install MLX (Mac). Apple silicon Macs only, text only. ```sh # with uv uv tool install mlx-lm # or pip install mlx-lm ``` 2. Run Qwen3-VL 30B-A3B Instruct (4bit, needs ~18.3 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen3-VL-30B-A3B-Instruct-MLX-4bit/tree/61c11f42d7bc01e00f5ea7f2e667c0a216f48397)) ```sh mlx_lm.chat --model lmstudio-community/Qwen3-VL-30B-A3B-Instruct-MLX-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model lmstudio-community/Qwen3-VL-30B-A3B-Instruct-MLX-4bit ``` Other versions: 8bit (~33.5 GB, Mac only) `mlx_lm.chat --model lmstudio-community/Qwen3-VL-30B-A3B-Instruct-MLX-8bit` Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct) [interactive version](https://whatmodel.sh/model/qwen3-vl-30b-a3b-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)