[whatmodel.sh](../../README.md) / Qwen3-VL 8B Instruct # Qwen3-VL 8B Instruct Alibaba · released 2025-10 · 8.77B params · apache-2.0 **score 55.1** ±0.7 · #281 of 732 · from 31 benchmark results across 5 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 8B Instruct among every model on that task or test, worst to best.}

task                vs the others          rank  counts  tests
agent tasks         ▁▁▃▆●▄▅▅▄▄▂▁   #204 of 313*    3.0%      2
coding              ▁▁▁▃▅●█▇▅▄▃▁   #340 of 469*    3.9%      1
chat & writing      ▁▁▁▃▆▄▆●▆▇▇▃   #195 of 402*    2.8%      1
math                ▁▁▃▅█▆●█▄▂▁▁    #188 of 431     12%      4
vision & documents  ▁▁▂▂▁▃●▂▆▇█▄    #110 of 153     50%     16

agent tasks · #204 of 313 · 3.0% 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 6/13   medium · avg 59%    2.0%  blunt · at its level
BFCL (version not stated)   66.2%  ▄▆●▂·█▆·▄··▂ beats 6/17   medium · avg 63%    1.0%  blunt · at its level

coding · #340 of 469 · 3.9% of its score

Trails [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md) here, though they rank lower overall.

test                                result  vs the others in its run  difficulty      counts  why
LiveCodeBench v6 (window unspecif…   39.3%  ▃▅●▅▅▃▅···▃█ beats 5/16   hard · avg 35%    3.9%  at its level

chat & writing · #195 of 402 · 2.8% of its score

Beats [Seed-OSS 36B Instruct](seed-oss-36b.md), [Gemma 2 9B IT SimPO](gemma-2-9b-simpo.md), [Granite 4.2 8B](granite-4-2-8b.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) …   83.7%  ▃··▃···█●▅▃▃ beats 7/11   easy · avg 77%    2.8%  typical

math · #188 of 431 · 12% of its score

Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 0324](deepseek-v3-0324.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md) here, though they rank higher overall. Trails [Phi-4-mini-reasoning 3.8B](phi-4-mini-reasoning.md), [OpenThinker3 7B](openthinker3-7b.md), [Olmo 3 7B Think](olmo-3-7b-think.md) here, though they rank lower overall.

test                          result  vs the others in its run  difficulty        counts  why
MathVista (testmini) (maker)   77.2%  ▃··▃▅·▃█●▅▅█ beats 9/16   medium · avg 69%    5.0%  at its level
AIME 2025 (maker)              45.9%  ▃·▃▃●··▃▅▃·█ beats 3/12   medium · avg 44%    4.5%  sharp · at its level
OMEGA (math generalisation)    32.3%  █···▄·▄·▄··● beats 6/6    hard · avg 19%      1.5%  at its level
MATH (subset unspecified)      91.6%  ██····██·██● beats 6/6    easy · avg 70%      0.8%  noisy

vision & documents · #110 of 153 · 50% 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), [GPT-4 Turbo (2024-04-09)](gpt-4-turbo-2024-04-09.md) here, though they rank higher overall. Trails [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank lower overall.

test                                result  vs the others in its run  difficulty           counts  why
CharXiv (reasoning questions) (ma…   46.4%  ▄▂▄●▄▄▆··▄█▂ beats 5/21   medium · avg 38%       9.1%  at its level
MMMU (val) (maker)                   69.6%  ▂···▆▂▂▂●▄▄█ beats 8/16   medium · avg 61%       7.3%  at its level
CharXiv descriptive questions (ma…   83.0%  ▄··▂▂▂·●▄▄▂█ beats 5/14   easy · avg 74%         6.3%  typical
MathVista (testmini) (maker)         77.2%  ▃··▃▅·▃█●▅▅█ beats 9/16   medium · avg 69%       5.0%  at its level
MMStar (maker)                       70.9%  ▂··▄▂▂·▄●▂▄▆ beats 9/16   medium · avg 65%       4.8%  at its level
MMMU Pro (maker)                     55.9%  ▃·█·▅▃▃▃●▅▃█ beats 9/16   medium · avg 46%       4.2%  at its level
ChartArena chart parsing (English…   37.3%  ▁·▁·▃▃▁●▃█·▄ beats 11/22  hard · avg 27%         2.4%  blunt · at its level
HallusionBench (maker)               61.1%  ▃·▃·▅▃▅▅●▅█▅ beats 9/16   medium · avg 54%       2.0%  blunt · at its level
BEAR-Bench (bilingual RU/EN enter…   25.4%  ▃▃·●·▃·█·█▅▅ beats 3/14   hard · avg 24%         1.5%  at its level
ChartQA (maker)                      89.6%  ▂··▂·▂▃·▅▂▆● beats 15/17  easy · avg 85%         1.5%  blunt
RealWorldQA (maker)                  71.5%  ▅▃·▃▅●·█·▃▅█ beats 7/16   easy · avg 70%         1.4%  blunt · at its level
DocVQA (maker)                       96.1%  ▃··▅▃▅▃█▃●▅▅ beats 11/17  very easy · avg 94%    1.3%  blunt · maxed out
ScreenSpot-Pro (maker)               54.6%  ▂····▂▄··●█▆ beats 4/11   medium · avg 46%       1.2%  blunt · at its level
CC-OCR (maker)                       79.9%  ▃··▃▅·▅▅·●▅▃ beats 10/13  easy · avg 74%         0.9%  blunt
OCRBench (maker)                     89.6%  ▅█·▃▃█▃▅●▃·▃ beats 14/16  easy · avg 84%         0.5%  blunt
PureDocBench document parsing (Av…   69.1%  ▂·▁·▂▂▅●·▁▂▂ beats 11/22  medium · avg 62%       0.1%  blunt · at its level

other tests (8)

test                                result  vs the others in its run  difficulty        counts  why
GPQA (subset unspecified) (maker)    61.9%  ▂····▂·▂▂●▆█ beats 4/14   hard · avg 49%      8.2%  sharp · at its level
MMLU-Pro (maker)                     71.6%  ▄·▄·▄▄▄●█▄██ beats 5/12   medium · avg 63%    6.9%  at its level
MMLU-Redux (maker)                   84.9%  ▃··▃·▃·●▃▅▃▅ beats 4/11   easy · avg 79%      5.3%  typical
SuperGPQA (maker)                    44.5%  ▄··▄·▄▄●█▄██ beats 4/11   medium · avg 37%    3.9%  at its level
AndroidWorld (maker)                 47.6%  ▃··▃●▃▃▃▃▃·█ beats 3/11   medium · avg 43%    2.9%  at its level
LVBench (maker)                      58.0%  ▅··▃·▅●▃·█·▃ beats 5/10   hard · avg 51%      2.7%  at its level
Video-MME (without subtitles) (ma…   71.4%  ▃··█··▅●▃▃▅▃ beats 7/13   medium · avg 66%    2.7%  blunt · at its level
MVBench (maker)                      68.7%  ▂▂▂·▂●··▆▄·▂ beats 4/13   medium · avg 67%    1.1%  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   92% ▇▇▇▇▇▇▇▇▇▇▇
allenai/Olmo-3-7B-Instruct model card           3    3% ▇
ChartArena: Benchmarking Chart Parsing …        1    2% ▇
BEAR-Bench: A Bilingual Enterprise and …        1    2% ▇
How Far Is Document Parsing from Solved…        1    0% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 55.1

_{Licences: allenai/Olmo-3-7B-Instruct model card ([Apache-2.0](../../licenses/allenai--Olmo-3-7B-Instruct.txt)) · ChartArena: Benchmarking Chart Parsing across Languages ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · BEAR-Bench: A Bilingual Enterprise and Academic Reasoning Benchmark for Multimodal Models ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · How Far Is Document Parsing from Solved? PureDocBench: A Source-Traceable Benchmark across Clean ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))} ## How to run it ### Through an API api (Alibaba Cloud Model Studio) · $0.31/M blended · $0.18 in / $0.7 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-8b-instruct (Non-Thinking mode only): input $0.18, output $0.7. 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-8B-Instruct/blob/0c351dd01ed87e9c1b53cbc748cba10e6187ff3b/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 8B Instruct (Q4_K_M, needs ~5 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct-GGUF/tree/f982a07559d4a2f6c8744d840bf6fccab30eea96)) ```sh llama cli -hf Qwen/Qwen3-VL-8B-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-8B-Instruct-GGUF:Q4_K_M ``` Other versions: Q8_0 (~8.7 GB) `llama cli -hf Qwen/Qwen3-VL-8B-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 8B Instruct (Q4_K_M, needs ~5 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct-GGUF/tree/f982a07559d4a2f6c8744d840bf6fccab30eea96)) ```sh lms get https://huggingface.co/Qwen/Qwen3-VL-8B-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 (~8.7 GB) `lms get https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct-GGUF@Q8_0 -y` · 4bit (~5.8 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen3-VL-8B-Instruct-MLX-4bit -y` · 8bit (~9.9 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen3-VL-8B-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 8B Instruct (Q4_K_M, needs ~6.1 GB, from [ollama.com](https://ollama.com/library/qwen3-vl/tags)) ```sh ollama run qwen3-vl:8b-instruct ``` Other versions: Q8_0 (~9.8 GB) `ollama run qwen3-vl:8b-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 8B Instruct (4bit, needs ~5.8 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen3-VL-8B-Instruct-MLX-4bit/tree/a0afc48efd9308fb14b4d58bbd49d382f7d4f845)) ```sh mlx_lm.chat --model lmstudio-community/Qwen3-VL-8B-Instruct-MLX-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model lmstudio-community/Qwen3-VL-8B-Instruct-MLX-4bit ``` Other versions: 8bit (~9.9 GB, Mac only) `mlx_lm.chat --model lmstudio-community/Qwen3-VL-8B-Instruct-MLX-8bit` Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) [interactive version](https://whatmodel.sh/model/qwen3-vl-8b-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)