# ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness

arXiv: https://arxiv.org/abs/2504.10514 · HTML: https://mlsanity.com/paper/2504.10514/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 24 of its 53 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 24 of 24)

- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities](https://mlsanity.com/paper/2308.02490/) (2023)
- [SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension](https://mlsanity.com/paper/2307.16125/) (2023)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [Are We on the Right Way for Evaluating Large Vision-Language Models?](https://mlsanity.com/paper/2403.20330/) (2024)
- [Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs](https://mlsanity.com/paper/2406.16860/) (2024)
- [MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding](https://mlsanity.com/paper/2406.09411/) (2024)
- [MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs](https://mlsanity.com/paper/2502.17422/) (2025)
- [Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders](https://mlsanity.com/paper/2408.15998/) (2024)
- [VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations](https://mlsanity.com/paper/2207.00221/) (2022)
- [NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples](https://mlsanity.com/paper/2410.14669/) (2024)
- [Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions](https://mlsanity.com/paper/2404.07214/) (2024)
- [VHELM: A Holistic Evaluation of Vision Language Models](https://mlsanity.com/paper/2410.07112/) (2024)
- [A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges](https://mlsanity.com/paper/2501.02189/) (2025)
- [Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional Understanding](https://mlsanity.com/paper/2306.08832/) (2023)
- [IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models](https://mlsanity.com/paper/2403.15952/) (2024)
- [GRIT: General Robust Image Task Benchmark](https://mlsanity.com/paper/2204.13653/) (2022)
- [Visual Robustness Benchmark for Visual Question Answering (VQA)](https://mlsanity.com/paper/2407.03386/) (2024)
- [ColorSwap: A Color and Word Order Dataset for Multimodal Evaluation](https://mlsanity.com/paper/2402.04492/) (2024)
- [VLM's Eye Examination: Instruct and Inspect Visual Competency of Vision Language Models](https://mlsanity.com/paper/2409.14759/) (2024)
- [ColorFoil: Investigating Color Blindness in Large Vision and Language Models](https://mlsanity.com/paper/2405.11685/) (2024)
- [AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis](https://mlsanity.com/paper/2502.01785/) (2025)
- [Evaluating Model Perception of Color Illusions in Photorealistic Scenes](https://mlsanity.com/paper/2412.06184/) (2024)
- [Decoding Emotions in Abstract Art: Cognitive Plausibility of CLIP in Recognizing Color-Emotion Associations](https://mlsanity.com/paper/2405.06319/) (2024)

## Built on by (most cited first, 28 of 28)

- [Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models](https://mlsanity.com/paper/2505.04921/) (2025)
- [Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception](https://mlsanity.com/paper/2602.11858/) (2026)
- [TIR-Bench: A Comprehensive Benchmark for Agentic Thinking-with-Images Reasoning](https://mlsanity.com/paper/2511.01833/) (2025)
- [Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation](https://mlsanity.com/paper/2506.07214/) (2025)
- [VTC-Bench: Evaluating Agentic Multimodal Models via Compositional Visual Tool Chaining](https://mlsanity.com/paper/2603.15030/) (2026)
- [Staying VIGILant: Mitigating Visual Laziness via Counterfactual Visual Alignment in MLLMs](https://mlsanity.com/paper/2606.26387/) (2026)
- [CaughtCheating: Is Your MLLM a Good Cheating Detective? Exploring the Boundary of Visual Perception and Reasoning](https://mlsanity.com/paper/2507.00045/) (2025)
- [White-Balance First, Adjust Later: Cross-Camera Color Constancy via Vision-Language Evaluation](https://mlsanity.com/paper/2605.19613/) (2026)
- [From Image Generation to Infrastructure Design: a Multi-agent Pipeline for Street Design Generation](https://mlsanity.com/paper/2509.05469/) (2025)
- [GenColorBench: A Color Evaluation Benchmark for Text-to-Image Generation Models](https://mlsanity.com/paper/2510.20586/) (2025)
- [Diagnosing Vision Language Models' Perception by Leveraging Human Methods for Color Vision Deficiencies](https://mlsanity.com/paper/2505.17461/) (2025)
- [Color Names in Vision-Language Models](https://mlsanity.com/paper/2509.22524/) (2025)
- [MSPL: Multi-Step Pseudo-Labeling for Open-Vocabulary Object Detection](https://mlsanity.com/paper/2510.14792/) (2025)
- [SonicBench: Dissecting the Physical Perception Bottleneck in Large Audio Language Models](https://mlsanity.com/paper/2601.11039/) (2026)
- [AceTone: Bridging Words and Colors for Conditional Image Grading](https://mlsanity.com/paper/2604.00530/) (2026)
- [Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarios](https://mlsanity.com/paper/2604.14041/) (2026)
- [A Stereotype Content Analysis on Color-related Social Bias in Large Vision Language Models](https://mlsanity.com/paper/2505.20901/) (2025)
- [Robust Driving QA through Metadata-Grounded Context and Task-Specific Prompts](https://mlsanity.com/paper/2510.19001/) (2025)
- [What Color Is the Text? A Benchmark for Hallucination Induced by Image-Embedded Prompt](https://mlsanity.com/paper/2511.13400/) (2025)
- [Shedding Light: A Benchmark for Evaluating Lighting Understanding in Generative Image Models](https://mlsanity.com/paper/2609.10787/) (2026)
- [The Percept-V Challenge: Can Multimodal LLMs Crack Simple Perception Problems?](https://mlsanity.com/paper/2508.21143/) (2025)
- [V-REX: Benchmarking Exploratory Visual Reasoning via Chain-of-Questions](https://mlsanity.com/paper/2512.11995/) (2025)
- [Benchmarking Attribute Discrimination in Infant-Scale Vision-Language Models](https://mlsanity.com/paper/2512.18951/) (2025)
- [Exploring the AI Obedience: Why is Generating a Pure Color Image Harder than CyberPunk?](https://mlsanity.com/paper/2603.00166/) (2026)
- [Position: The Systemic Lack of Agency in Visual Reasoning](https://mlsanity.com/paper/2606.14795/) (2026)
- [Beyond Color Geometry: Evaluating Human-Like Color Representations in Vision Models](https://mlsanity.com/paper/2607.13647/) (2026)
- [How Much Does It Cost to Answer My Question? Benchmarking Cloud VLM-based VQA Systems](https://mlsanity.com/paper/2608.07861/) (2026)
- [Canonical Color as a Lens into Concept Decodability in Vision Encoders and VLMs](https://mlsanity.com/paper/2609.09124/) (2026)

## Abstract (arXiv)

Color plays an important role in human perception and usually provides critical clues in visual reasoning. However, it is unclear whether and how vision-language models (VLMs) can perceive, understand, and leverage color as humans. This paper introduces ColorBench, an innovative benchmark meticulously crafted to assess the capabilities of VLMs in color understanding, including color perception, reasoning, and robustness. By curating a suite of diverse test scenarios, with grounding in real applications, ColorBench evaluates how these models perceive colors, infer meanings from color-based cues, and maintain consistent performance under varying color transformations. Through an extensive evaluation of 32 VLMs with varying language models and vision encoders, our paper reveals some undiscovered findings: (i) The scaling law (larger models are better) still holds on ColorBench, while the language model plays a more important role than the vision encoder. (ii) However, the performance gaps across models are relatively small, indicating that color understanding has been largely neglected by existing VLMs. (iii) CoT reasoning improves color understanding accuracies and robustness, though they are vision-centric tasks. (iv) Color clues are indeed leveraged by VLMs on ColorBench but they can also mislead models in some tasks. These findings highlight the critical limitations of current VLMs and underscore the need to enhance color comprehension. Our ColorBenchcan serve as a foundational tool for advancing the study of human-level color understanding of multimodal AI.