Vision Language Models Compared to Image Recognition

Can advanced Vision Language Models (VLMs) replace traditional image recognition models? To find out, we benchmarked 16 leading models across three paradigms: traditional CNNs (ResNet, EfficientNet), VLMs ( such as GPT-4.1, Gemini 2.5), and Cloud APIs (AWS, Google, Azure).

 Mean Average Precision (mAP) served as our primary accuracy metric, supplemented by latency, cost and class-specific performance analysis.

You can see benchmark methodology from here.

Accuracy vs latency benchmark

In our benchmark, we evaluated models along four dimensions: latency, mean average precision (mAP), price, and success rate. Latency measures the time a model takes to process a single image, while mAP reflects the overall classification accuracy. Success rate tracks whether a model returned a valid JSON output, particularly relevant for vision language models, which interpret images in natural language rather than structured data.

Traditional image recognition models, such as EfficientNet, ResNet18, ResNet50, ResNet101, and DenseNet121, consistently show both low latency (0.03–0.2 seconds) and competitive accuracy (mAP 0.75–0.81). Among them, DenseNet121 and ResNet18 achieve the highest mAP scores (0.81 and 0.80 respectively), while EfficientNet follows closely (0.78). ResNet50 and ResNet101 show moderate performance within this group (0.75 and 0.77), but all traditional models significantly outperform cloud-based image recognition tools like AWS Rekognition, Google Cloud Vision, and Azure Vision, which achieve moderate accuracy (mAP 0.61–0.64) with latencies between 2–3.5 seconds. This demonstrates that traditional models dominate in both speed and precision.

For vision language models, including OpenAI GPT-4.1, Claude Opus 4.1, X-AI Grok 2 Vision, Meta-Llama/LLama-3.2-11B Vision Instruct, and Google Gemini 2.5 Flash, latencies are significantly higher, ranging from 1 to 12 seconds, with mAP values between 0.60 and 0.75. Google Gemini 2.5 Flash achieves 0.75 mAP, making it the most accurate VLM in our test. Among other VLMs, GPT-4.1 performs strongly with a mAP of 0.73, followed by Claude Opus 4.1 (0.71) and X-AI Grok 2 Vision (0.70). GPT-4o-mini shows moderate performance (0.66 mAP), while Meta-Llama Vision Instruct trails significantly (0.60 mAP).

Most vision language models reliably return JSON outputs with near 100% success, except for Meta-Llama Vision Instruct, which succeeded only 36% of the time and Gemini 2.5 Pro, which consistently failed (0% success), severely limiting their practical applicability in automated pipelines.

While vision language models generally lag behind traditional image recognition models in raw speed, the top-performing VLMs such as Google Gemini 2.5 Flash (0.75 mAP) and GPT-4.1 (0.73 mAP) achieve classification accuracy that approaches traditional CNN performance and significantly exceeds cloud APIs like AWS Rekognition and Azure Vision. In terms of latency, most vision language models cluster around 3-4 seconds, except Meta-Llama, which is notably slower at 12 seconds, highlighting the impact of model architecture and optimization.

Overall, traditional image recognition models still excel in both speed and accuracy. VLMs, however, show promise for multimodal reasoning and structured outputs, with latency consistently higher but the best models achieving accuracy that approaches traditional CNNs and surpasses cloud-based image recognition services.

Class-specific performance: where models excel and struggle

Our evaluation used seven overlapping classes that test different aspects of object detection:

• face: Represents only the face region. The model needs to detect a person’s face, which can be challenging due to its small size and fine details.
• head: Covers the entire head excluding the face. Focuses on detecting the shape and structure of the head.
• head_with_helmet: Represents the head wearing a helmet. The model must detect both the head and the helmet together, testing its ability to recognize their relationship.
• helmet: Represents only the helmet, regardless of the presence of a person or head. Important for equipment detection.
• person: Detects the presence of a person, with or without a helmet. Serves as a general human detection class.
• person_no_helmet: Represents a person who is not wearing a helmet. The model must identify both human presence and helmet absence.
• person_with_helmet: Represents a person wearing a helmet. Requires distinguishing both human presence and helmet usage, closely related to person_no_helmet.

These overlapping and closely related classes can be challenging for vision language models, as they interpret visual information through natural language rather than directly capturing fine-grained pixel-level differences.

Traditional CNN performance

1. Face class
   • Best performance: EfficientNet and DenseNet121 (100%)
   • Lowest: ResNet101 (95%)Face detection is highly accurate across CNNs, outperforming most VLMs.
2. Head class
   • Best: ResNet18 and DenseNet121 (69%)
   • Lowest: ResNet50 (50%)Moderate performance; CNNs struggle more with head detection than face and helmet classes.
3. Head and Head_with_helmet
   • Best performance: EfficientNet and ResNet18 (Head_with_helmet 98%, Head 65–69%)
   • Lowest: ResNet50 (Head 50%, Head_with_helmet 96%)CNNs perform very well on helmeted heads, achieving 96–98% accuracy across all models. Detection of bare heads is more challenging, with lower accuracy (50–69%), indicating that CNNs distinguish prominent objects like helmets better than less distinct regions like unhelmeted heads.
4. Person class
   • All models: 0% accuracy
5. Person_no_helmet
   • Best: DenseNet121 (72%)
   • Lowest: ResNet50 (53%)CNNs handle this challenging class better than VLMs, highlighting their ability to capture fine-grained details.
6. Person_with_helmet
   • Best: EfficientNet (98%)
   • Lowest: DenseNet121 (96%)High accuracy across all models; helmeted persons are consistently recognized.

Vision language model performance

1. Face class (face detection)
   • Best performance: Claude Opus 4.1 (83%)
   • Weakest: Meta-Llama Vision Instruct (4%) and GPT-4o-mini (12%)VLMs generally perform worse on small and detailed objects like faces; Meta-Llama and GPT-4o-mini struggle with fine details.
2. Head and Head_with_helmet
   • Head: Claude Opus 4.1 (96%) highest, Meta-Llama (30%) lowest
   • Head_with_helmet: GPT-4.1 (99%) and Gemini 2.5 Flash (98%) highest, Meta-Llama (50%) lowestModels perform well on head detection with or without helmets; most reach 90%+ accuracy except Meta-Llama.
3. Helmet class
   • Highest: Grok 2 Vision (100%), GPT-4.1 (99%), Gemini 2.5 Flash (98%)
   • Lowest: Meta-Llama (52%)Distinguishing helmeted vs. non-helmeted objects is generally easier, but Meta-Llama underperforms.
4. Person class
   • All models achieve 100%, likely due to large and clear objects.
5. Person_no_helmet
   • Best: GPT-4.1 and Gemini 2.5 Flash (58%)
   • Lowest: Meta-Llama (18%) and GPT-4o-mini (29%)Detecting fine details like helmet absence is challenging; some models excel on prominent objects but lag on nuanced classes.
6. Person_with_helmet
   • Highest: GPT-4.1 (98%) and Gemini 2.5 Flash (98%)
   • Lowest: Meta-Llama (55%)Most models perform very well here.

Cloud API performance

• Face class
  • Best: AWS Rekognition (22%)
  • Lowest: Google Cloud Vision (0%)Face detection is generally poor across Cloud APIs; fine-grained distinctions like faces are challenging.
• Head and Head_with_helmet
  • Head: AWS Rekognition (24%) best, Azure Vision lowest (0%)
  • Head_with_helmet: AWS Rekognition (10%) best, Azure Vision (1%) lowest Detection of heads, especially helmeted or unhelmeted, is limited; Cloud APIs focus on broader objects rather than fine details.
• Helmet class
  • Best: AWS Rekognition (94%)
  • Lowest: Azure Vision (37%)Helmet detection is moderately successful for some APIs (AWS), but inconsistent across providers.
• Person class
  • All models: 100% Large and clear objects like full persons are reliably detected by all Cloud APIs.
• Person_no_helmet
  • Best: Azure Vision (78%)
  • Lowest: Google Cloud Vision (26%)Performance varies widely; some APIs can handle challenging classes moderately well.
• Person_with_helmet
  • Best: AWS Rekognition (94%)
  • Lowest: Azure Vision (37%) Helmeted persons are detected reliably by AWS but inconsistently by other providers.

For faces, CNNs achieve the highest accuracy, followed by VLMs, while Cloud APIs perform poorly. In head and head_with_helmet classes, CNNs remain strong, VLMs perform well on helmeted heads but less consistently on bare heads, and Cloud APIs struggle with both. For helmets, CNNs and VLMs generally perform very well, whereas Cloud APIs show variable success. In the person class, all paradigms detect full persons reliably. For person_no_helmet, CNNs outperform both VLMs and Cloud APIs, demonstrating superior handling of fine-grained details. Finally, for person_with_helmet, CNNs and VLMs maintain high accuracy, while Cloud APIs show inconsistent performance depending on the provider.

Precision, recall and F1-score

Precision measures how many of a model’s positive predictions are actually correct. In other words, it answers the question: “Of the predictions the model labeled as positive, how many are truly correct?”

Recall measures how many of the actual positive instances the model successfully identifies. It answers the question: “Of all the true positive cases, how many did the model detect?”

F1-Score is a balanced summary of precision and recall. It provides a single metric reflecting both accuracy and coverage, particularly useful when you want to balance precision and recall.

CNN-based models (ResNet50, ResNet101, DenseNet121) show high performance in both precision (0.93–0.95) and recall (0.91–0.94), resulting in high F1-scores (0.92–0.93). This indicates that they are both highly accurate in their predictions and able to capture the majority of true positive instances. EfficientNet also shows a high F1-score (0.92), offering consistent and reliable performance.

Cloud APIs (AWS Rekognition, Google Cloud Vision, Azure Vision) have lower precision and recall, with F1-scores ranging from 0.32 to 0.58. This suggests that while cloud services are optimized for general-purpose tasks, their accuracy in fine-grained class distinctions is limited.

Vision-language models show more variable performance. GPT-4.1, X-AI Grok 2 Vision, and Claude Opus 4.1 achieve exactly 0.76 F1-scores, while Google Gemini 2.5 Flash performs slightly better with an F1-score of 0.80. Although these models demonstrate strong performance in some classes, they generally trail behind CNNs in overall accuracy. Meta-Llama Vision Instruct has an F1-score of 0.47, with both low precision and recall, meaning the model struggles in both making correct predictions and capturing true positives.

So which one you should choose?

Traditional CNNs are ideal for speed-critical applications where millisecond response times matter, such as real-time video processing, autonomous vehicles, or industrial safety systems. With their superior accuracy (mAP 0.75–0.81) and lightning-fast inference (0.03–0.2s), these traditional AI models excel when you need reliable, consistent performance without the overhead of natural language processing or model complexity. CNNs focus on visual data and image classification tasks like object detection, offering both vision accuracy and efficiency without needing fine tuning across multimodal models.

Vision Language Models (VLMs) shine when you need contextual understanding and flexible outputs. These vision language models work across both vision and textual modalities, allowing large language models to process image input together with text descriptions. Perfect for applications requiring natural language explanations, image captioning, visual reasoning tasks, or even visual question answering, they leverage vision encoders and cross attention layers to align image text pairs into the same dimensional space. While you accept higher latency (3–12s), the reasoning capabilities they bring to image understanding, visual elements, and visual instructions make them ideal for more specific downstream tasks such as intelligent content moderation, image generation, visual mathematical reasoning, or interactive vision assistants. By using parameter efficient fine tuning with high quality training data, vision language models (VLMs) become powerful machine learning models that unify visual and textual information under a shared embedding space.

Cloud APIs provide detailed, comprehensive responses with rich metadata and confidence scores, making them ideal when you need extensive information beyond simple classification. These APIs often rely on pretrained vision encoder components and visual encoders trained on large-scale public model datasets of conceptual captions and relevant photos. Best for applications requiring structured JSON outputs, bounding boxes, object localization, or long video understanding, they are ready-to-use solutions without the need for robust model training or infrastructure management. While their accuracy is moderate (mAP 0.61–0.66), they reduce technical details and infrastructure costs, enabling tasks like automated report generation, semantic meaning extraction, and unified framework integration with existing generative models.

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Vision language models (VLMs) – Key features and advantages

Multimodal reasoning

Vision Language Models (VLMs) are powerful multimodal models that can process both visual and textual modalities simultaneously, allowing them to interpret visual and textual information in a richer, context-aware way. By aligning image input with natural language prompts, they enable advanced tasks such as automatic image captioning, helmet detection in security footage, visual reasoning tasks, visual question answering, and even explaining visual content in natural language. Unlike traditional AI models that focus only on visual data, VLMs combine vision capabilities with large language model reasoning, making them ideal for complex downstream tasks.

Structured output and JSON generation

Many vision language models can generate structured outputs such as JSON, which is valuable for automated pipelines and applications requiring text descriptions alongside image features. In our benchmark, ChatGPT-5 and Gemini 2.5 Pro consistently failed, while Meta-Llama Vision Instruct succeeded only about 36% of the time. Structured outputs are particularly useful for vision assistants, enabling tasks like object detection, object localization, and producing reliable data for machine learning models without extensive fine tuning.

Fine-tuning capabilities

VLMs support parameter efficient fine tuning with relatively small training data, enabling rapid adaptation to domain-specific visual reasoning tasks. For instance, they can be fine-tuned to distinguish helmeted vs. non-helmeted individuals or specialized safety equipment in image input scenarios. By leveraging pretrained vision encoder architectures and robust model training techniques, they can generalize better with fewer conceptual captions or image text pairs.

Limitations of vision language models

Latency and speed

Compared to traditional CNNs or simpler vision models, vision language models typically have higher latency, which can limit real-time applications such as long video understanding. Some multimodal models, like X-AI Grok 2 Vision and Google Gemini 2.5 Flash, are closer to cloud APIs in speed, but Meta-Llama is notably slower. The trade-off comes from their model end to end design and cross attention layers, which improve reasoning capabilities but increase inference time.

Class-wise challenges

Vision language models sometimes struggle with overlapping classes and fine-grained object recognition, such as differentiating between a “head” and a “head_with_helmet” or between “person_no_helmet” and “person_with_helmet.” While some models perform well on helmeted classes, they underperform in other visual reasoning tasks like detecting faces or subtle visual elements. This highlights the importance of high quality training data and careful fine tuning when targeting more specific downstream tasks.

Structured output reliability

The consistency of structured outputs such as JSON varies widely. While some VLMs reliably generate valid outputs, others fail in particular use cases, limiting their usefulness in fully automated pipelines. Even with pretrained vision encoder backbones and shared embedding space approaches, some models still fail to maintain semantic meaning in structured output. This inconsistency underscores the need for robust model training, relevant photos in the dataset, and continued improvements in generative models for vision and language modalities.

Benchmark methodology

We conducted our comprehensive evaluation using the SHEL5K safety helmet detection dataset, specifically utilizing the first 500 images to ensure consistent comparison across all model architectures. The dataset contains seven overlapping classes designed to test fine-grained object detection capabilities: face, head, head_with_helmet, helmet, person, person_no_helmet, and person_with_helmet.

Data preprocessing

The original SHEL5K dataset annotations were provided in XML format. We developed a preprocessing pipeline to convert these annotations into a multi-label CSV format suitable for systematic evaluation:

Each image was mapped to its corresponding ground truth labels, creating a standardized evaluation framework. For traditional CNNs, images were preprocessed to 224×224 resolution with standard normalization. Vision language models and cloud APIs received images in their original format to preserve contextual information.

Traditional CNN evaluation protocol

Traditional convolutional neural networks (EfficientNet, ResNet variants, DenseNet121) underwent supervised fine-tuning using established best practices:

Training configuration:

• Architecture: Pre-trained models with modified classification heads
• Loss function: BCEWithLogitsLoss for multi-label classification
• Optimizer: Adam with learning rate 1e-4
• Training epochs: 5
• Data split: 80% training, 20% validation
• Batch size: 16

Vision language model testing framework

VLMs were evaluated through carefully structured prompts designed to elicit consistent, machine-readable responses. Our prompt engineering approach requested JSON-formatted confidence scores for each class.

API configuration:

• Temperature: 0.1 (low temperature for consistency)
• Max tokens: 800
• Models tested via OpenRouter API integration
• JSON parsing with error handling and format validation

Success rate tracking: We monitored the percentage of valid JSON responses, as VLMs sometimes generate natural language explanations instead of structured output. This metric proved crucial for evaluating practical deployment feasibility.

Cloud API integration and label mapping

Cloud APIs presented unique challenges due to their general-purpose nature and different taxonomies. We developed comprehensive mapping strategies for each service:

Label mapping strategy:

Cloud APIs present a fundamental challenge: they were not designed for our specific seven-class taxonomy. These services return general-purpose labels like “person,” “helmet,” “construction worker,” or “safety equipment” rather than the precise combinations we need to evaluate (such as “person_with_helmet” or “head_with_helmet”).

To address this limitation, we developed comprehensive mapping dictionaries for each cloud service based on their outputs. Azure Computer Vision mapping included 50+ label variants covering different ways the API might describe people (person, man, woman, worker, individual), helmets (helmet, hard hat, safety helmet, cap), and facial features (face, human face, portrait). Similar extensive mappings were created for AWS Rekognition and Google Cloud Vision, each tailored to that service’s specific vocabulary and labeling patterns.

Combined class inference logic:

The most sophisticated aspect of our cloud API evaluation involved inferring combined classes that the APIs don’t explicitly recognize. We implemented rule-based logic to detect when multiple basic elements appear together:

When both “person” and “helmet” are detected in the same image with sufficient confidence, the system infers “person_with_helmet” using the minimum confidence score between the two detections (conservative approach). Similarly, detecting “head” and “helmet” simultaneously triggers “head_with_helmet” classification.

For negative classifications, when a person is detected but no helmet is found, the system infers “person_no_helmet” with slightly reduced confidence (90% of the original person confidence) to account for the uncertainty inherent in negative inference.

This approach acknowledges that cloud APIs excel at detecting individual objects but struggle with relational reasoning about object combinations—a key limitation when evaluating fine-grained, context-dependent classification tasks.

Evaluation metrics and statistical analysis

Primary metrics:

• Mean Average Precision (mAP): Primary accuracy measure using macro-averaging across classes
• Precision, Recall, F1-Score: Micro-averaged for overall performance assessment
• Class-wise Accuracy: Individual class performance for detailed analysis
• Latency: End-to-end processing time per image
• Success Rate: Percentage of valid outputs (particularly relevant for VLMs)

Threshold selection: A classification threshold of 0.5 was applied consistently across all models, with VLMs using confidence scores and traditional models using sigmoid-activated logits.

Statistical robustness: Each model was evaluated on identical image sets with consistent preprocessing to ensure fair comparison. Latency measurements averaged over multiple runs to account for system variance.

Experimental controls and limitations

Controls implemented:

• Identical 500-image test set across all models
• Consistent evaluation metrics and thresholds
• Standardized error handling and timeout procedures
• Multiple API key rotation to handle rate limits

Principal Analyst

Cem Dilmegani

Principal Analyst

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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