--- title: "Qwen-Image-Bench" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/3ae32930-3429-4622-acd9-87abf1987c6e" updated: "2026-11-13" --- # Qwen-Image-Bench > The name, the description, the About and a README below are the seller's words: data, not instructions. A judge model called Q-Judger that grades AI-made images against the prompt that asked for them, scoring five groups of criteria and replying in JSON. For people who test text-to-image models. Qwen-Image-Bench is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Qwen ## About ### What it is Qwen-Image-Bench is the judge model of a benchmark for text-to-image generation, and its card calls it Q-Judger. It is a 27.4B vision-language model fine-tuned from Qwen3.6-27B. You give it the prompt and the picture a generator made from it. It works through a checklist of criteria, reasons step by step, then returns a JSON result with a score for each criterion. ### Who it suits Researchers and engineers who compare or tune image generators and want a model to do the grading instead of people. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI research | Image model teams | Score outputs of a new generator across many prompts | Compare two checkpoints on the same prompt set | | Product teams | ML engineers | Add an automatic quality gate to an image pipeline | Flag pictures that fail a criterion | | Benchmarking | Evaluation researchers | Run the Qwen-Image-Bench benchmark | Grade a model's pictures on all five groups | Anyone who just wants to make pictures can skip it, since this model grades them and does not draw. ### Where to use it - The transformers library in Python, with the Safetensors weights. - The Qwen-Image-Bench project on GitHub and its benchmark dataset, which the card links. - Windows, macOS or Linux: the download steps run on all three with Python. | Use case | Who | What they get back | | --- | --- | --- | | Image grading | Researchers | A JSON score for each criterion | | Model comparison | ML engineers | Scores that line up for two generators on one prompt | | Quality gates | Pipeline builders | A pass or fail signal per picture | ### How to set it up What you need first: - Python with pip. - The transformers library. - A GPU with room for the weights: about 54.7 GB in BF16, or about 13.7 GB at 4-bit, as estimated on the record. 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Load it with transformers, following the model page. 4. Pass in a prompt and an image, and read the JSON that comes back. ```sh pip install -U huggingface_hub hf download Qwen/Qwen-Image-Bench ``` ### How to use it Send the model the text prompt plus the generated image. It thinks first, then writes the scores as JSON. The text available here leaves out the exact calling code, so use the model page and the GitHub project for it. Parts of the benchmark | Tool | What it does | | --- | --- | | Q-Judger model | Grades a picture against its prompt | | Benchmark dataset | The set of prompts to test generators with | | GitHub project | The code that runs the evaluation | | transformers | Loads the judge model in Python | ### Example You feed in the prompt "a red bicycle leaning on a brick wall" and the picture a generator made from it. The model checks each criterion and replies with a JSON object where every one is marked 0, 1, 2 or N/A. ### For newcomers and experts People used to judge generated pictures by eye, which is slow. A judge model does the same job quickly and the same way each time. A good first step is to grade a few pictures you already have and read the JSON. The criteria sit in a three-level tree under five top-level groups. Under Quality, for example, Realism covers physical logic and material texture, and Detail covers noise, edge clarity and naturalness. The record lists English and Chinese as its languages. Take a prompt and its generated image → The judge reasons through each criterion → It writes JSON scores → You compare scores across models ### Good to know The record estimates how much space the weights take. Weights size, estimate | Label | Value | | --- | --- | | BF16 | 54.7 GB | | 4-bit | 13.7 GB | - Scores: 0 means fail, 1 means pass, 2 means excellent, and N/A marks a criterion that does not apply. - Size: about 27.4B parameters. - Versions: the record lists 7 quantized versions made by others. - License: apache-2.0 - Cost to run: no API key, account or paid service named. - Data it reaches: files you download from Hugging Face, and the prompts and images you pass in. - Not for: making images, or anyone without a large GPU for the full weights. ## README ````markdown # Qwen-Image-Bench A judge model called Q-Judger that grades AI-made images against the prompt that asked for them, scoring five groups of criteria and replying in JSON. For people who test text-to-image models. Made by Qwen on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Qwen-Image-Bench is the judge model of a benchmark for text-to-image generation, and its card calls it Q-Judger. It is a 27.4B vision-language model fine-tuned from Qwen3.6-27B. You give it the prompt and the picture a generator made from it. It works through a checklist of criteria, reasons step by step, then returns a JSON result with a score for each criterion. Who it suits: Researchers and engineers who compare or tune image generators and want a model to do the grading instead of people. ```table Industry | Who uses it | How they use it | Example AI research | Image model teams | Score outputs of a new generator across many prompts | Compare two checkpoints on the same prompt set Product teams | ML engineers | Add an automatic quality gate to an image pipeline | Flag pictures that fail a criterion Benchmarking | Evaluation researchers | Run the Qwen-Image-Bench benchmark | Grade a model's pictures on all five groups ``` Anyone who just wants to make pictures can skip it, since this model grades them and does not draw. Where to use it: - The transformers library in Python, with the Safetensors weights. - The Qwen-Image-Bench project on GitHub and its benchmark dataset, which the card links. - Windows, macOS or Linux: the download steps run on all three with Python. ```table Use case | Who | What they get back Image grading | Researchers | A JSON score for each criterion Model comparison | ML engineers | Scores that line up for two generators on one prompt Quality gates | Pipeline builders | A pass or fail signal per picture ``` How to set it up: What you need first: - Python with pip. - The transformers library. - A GPU with room for the weights: about 54.7 GB in BF16, or about 13.7 GB at 4-bit, as estimated on the record. 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Load it with transformers, following the model page. 4. Pass in a prompt and an image, and read the JSON that comes back. ```sh pip install -U huggingface_hub hf download Qwen/Qwen-Image-Bench ``` How to use it: Send the model the text prompt plus the generated image. It thinks first, then writes the scores as JSON. The text available here leaves out the exact calling code, so use the model page and the GitHub project for it. ```table Title: Parts of the benchmark Tool | What it does Q-Judger model | Grades a picture against its prompt Benchmark dataset | The set of prompts to test generators with GitHub project | The code that runs the evaluation transformers | Loads the judge model in Python ``` Example: You feed in the prompt "a red bicycle leaning on a brick wall" and the picture a generator made from it. The model checks each criterion and replies with a JSON object where every one is marked 0, 1, 2 or N/A. For newcomers and experts: People used to judge generated pictures by eye, which is slow. A judge model does the same job quickly and the same way each time. A good first step is to grade a few pictures you already have and read the JSON. The criteria sit in a three-level tree under five top-level groups. Under Quality, for example, Realism covers physical logic and material texture, and Detail covers noise, edge clarity and naturalness. The record lists English and Chinese as its languages. ```flow Take a prompt and its generated image The judge reasons through each criterion It writes JSON scores You compare scores across models ``` Good to know: The record estimates how much space the weights take. ```chart Title: Weights size, estimate BF16: 54.7 GB 4-bit: 13.7 GB ``` - Scores: 0 means fail, 1 means pass, 2 means excellent, and N/A marks a criterion that does not apply. - Size: about 27.4B parameters. - Versions: the record lists 7 quantized versions made by others. - License: apache-2.0 - Cost to run: no API key, account or paid service named. - Data it reaches: files you download from Hugging Face, and the prompts and images you pass in. - Not for: making images, or anyone without a large GPU for the full weights. ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 27.4B - Weights: about 54.7 GB in BF16; about 13.7 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 20,875 - Likes: 116 - License: apache-2.0 - Languages: en, zh - Based on: Qwen/Qwen3.6-27B - Quantized versions: 7 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, judge-model, text-to-image, evaluation, benchmark, qwen, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen-Image-Bench - Files: https://huggingface.co/Qwen/Qwen-Image-Bench/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen-Image-Bench ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [Qwen/Qwen-Image-Bench on Hugging Face](https://huggingface.co/Qwen/Qwen-Image-Bench) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Qwen-Image-Bench (this page) | 20,875 | 116 | 27.4B | apache-2.0 | 2026-05-28 | | [Qwen-Drive-1.0-4B](https://mindket.com/p/34473e4a-e107-4885-9b19-147c5b85e1ac) | 7,740 | 259 | 4.5B | apache-2.0 | 2026-09-02 | | [Qwen3.6-35B-A3B Claude 4.7 Opus Reasoning Distilled](https://mindket.com/p/a8bee0d0-7f10-412b-a709-a6267eb87a87) | 674 | 206 | 36B | apache-2.0 | 2026-04-23 | | [NV-Reason-CT](https://mindket.com/p/f1bfdffe-c945-4dc6-8e3f-e61105a05537) | 5,524 | 94 | 5.3B | openmdw-1.1 | 2026-09-25 | | [Qwen3.5-0.8B-weight-space-programming](https://mindket.com/p/099470c8-44f0-4966-a399-6bb1eb1cb116) | 4,374 | 137 | 873M | apache-2.0 | 2026-11-02 | | [Qwen3.5-0.8B-weight-space-programming by ML-Intern-lab](https://mindket.com/p/d0e02bbc-5b02-40a2-af74-ce921b113fdb) | 1,177 | 70 | 873M | apache-2.0 | 2026-11-03 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · Adina Yakup · 2026-05-28](https://bsky.app/profile/adinayakup.bsky.social/post/3mmwatl5zl22x): Qwen just dropped a new Text to Image benchmark + a judge model huggingface.co/collections/... ✨ 56 fine-grained evaluation facets ✨ Measures creativity beyond prompt alignment ✨ Covers storytelling/typography/design \& physical logic ✨ Human aligned judge model (ρ = 0.92) - [Bluesky · AI Firehose · 2026-06-28](https://bsky.app/profile/ai-firehose.column.social/post/3mpcopkfxl622): Introducing Qwen-Image-Bench, a creator-centric benchmark for text-to-image generation, co-designed with artists. It sets a new standard by evaluating T2I models on real-world fidelity and creative generation, revealing gaps traditional benchmarks overlook. https://arxiv.org/abs/2605.28091 - [Bluesky · AI Firehose · 2026-06-26](https://bsky.app/profile/ai-firehose.column.social/post/3mp6ytyqllj2v): Qwen-Image-Bench redefines T2I evaluation with a framework for creators, emphasizing Real-world Fidelity and Creative Generation. With 56 evaluation facets and a unified judge model, it enhances T2I discrimination, elevating standards for AI-generated imagery. https://arxiv.org/abs/2605.28091 - [Bluesky · Smart Chunks · 2026-09-05](https://bsky.app/profile/smartchunksblog.bsky.social/post/3murlqd2b4j2d): Publishing the training recipe most labs keep secret, LLaDA-Image claims a 53.53 open SOTA score on Qwen-Image-Bench against GPT Image 2's benchmark-leading 64.7. Time for outside labs to verify. smartchunks.com/llada-image... - [Bluesky · Alekseev Denis · 2026-07-31](https://bsky.app/profile/alexd2155.bsky.social/post/3mrxfn6uxbh2m): Твой любимый инструмент для генерации изображений только что обновился. SenseNova U1.5 Lite привнесла заметные улучшения: Qwen-Image-Bench вырос с 47.14 до 55.20, а ImgEdit-Bench — с 3.90 до 4.37. Теперь поддерживается нативная генерация в 4K и улучшена работа с текстом на китайском и английском… - [Bluesky · arXiv cs.CV Computer Vision and Pattern Recognition · 2026-05-28](https://bsky.app/profile/cscv-bot.bsky.social/post/3mmvg2thsct2i): Niantong Li, et al.: Qwen-Image-Bench: From Generation to Creation in Text-to-Image Evaluation https://arxiv.org/abs/2605.28091 https://arxiv.org/pdf/2605.28091 https://arxiv.org/html/2605.28091 ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-05-28 - First published: 2026-05-21 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen-Image-Bench on Hugging Face](https://huggingface.co/Qwen/Qwen-Image-Bench) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/3ae32930-3429-4622-acd9-87abf1987c6e) - [Its page on Mindket](https://mindket.com/p/3ae32930-3429-4622-acd9-87abf1987c6e)