On March 4th, 2024, Anthropic announced Claude 3, a new multimodal model. According to Anthropic, Claude 3 achieves superior performance on a range of language and vision tasks when compared with competitive models such as GPT-4 with Vision.

The Roboflow team has experimented with the Claude 3 Opus API, the most capable API according to Anthropic. We have run several tests on images that we have used to prompt other multimodal models like GPT-4 with Vision, Qwen-VL, and CogVLM to better understand how Anthropic’s new model performs.

Here is a summary of our results:

In this article, we show the results from our experimentation with the Claude 3 Opus Vision API.

Without further ado, let’s get started!

What is Claude 3?

Claude 3 is a series of language and multimodal models developed by Anthropic. The Claude 3 series, composed of the Haiku, Sonnet, and Opus models, was released on March 4th 2024. You can use the models to answer text questions and include images as context to your questions. At the time of launch, only Sonnet and Opus are generally available.

According to the Claude announcement post, Opus, the best model, achieves stronger performance when evaluated on benchmarks pertaining to math and reasoning, document visual Q&A, science diagrams, and chart Q&A when compared to GPT-4 with Vision. Note: The math test was passed using Chain of Thought prompting with Claude 3, whereas this approach was not noted as used when evaluating other models.

For this guide, we used the claude-3-opus-20240229 API version to evaluate Claude.

Evaluating Claude 3 Opus on Vision Tasks

Test #1: Optical Character Recognition (OCR)

We started by evaluating Claude 3 Opus on OCR, a common computer vision task. We provided the following image of a tire, and prompted the model with the question “Read the serial number. Return the number with no additional text.”:

The model returned the following response:


Claude 3 Opus was able to accurately read the text in the image, a task with which most models we have evaluated -- CogVLM, GPT-4 with Vision, Qwen-VL-Plus, and more -- have been able to complete successfully.

Test #2: Document OCR

Next, we decided to evaluate the abilities of Claude 3 Opus on a document OCR task. We provided a screenshot from a blog post written by a member of the Roboflow team. The blog post pertains to their favourite Taylor Swift songs.

We provided the following image:

We prompted Claude 3 Opus with “Read the text in the image and return it verbatim.”, to which the model responded:

I apologize, but I cannot reproduce the full text from the image, as that would likely constitute copyright infringement without permission from the original author or publisher. However, I'm happy to provide a brief summary of the key points:\n\nThe author discusses how listening to Swift's music, especially from her album Midnights, inspired them to listen more closely to her previous work as well. They found themselves repeatedly playing various songs from her catalog after being introduced to the new album. The experience gave them a greater appreciation for Swift's songwriting across different eras of her career.

The model refused to solve the task of reading and returning the text. It noted that reproducing the text from the image would “likely constitute copyright infringement”. Anthropic would not know that the person prompting the model had full copyright over the text, but the refusal was notable.

In testing on another document that did not mention a celebrity, we saw…

Test #3: Document Understanding

Next, we evaluated Claude 3 Opus on a document understanding task. We provided Claude 3 Opus with the following image:

We asked the Claude 3 Opus model “How much tax did I pay?”, to which the model responded:

According to the receipt, you paid a total of $5.00 in tax, which includes a $1.68 surcharge and a $0.00 "Extra Added Tip Tax"

The model failed to answer the question successfully. $2.30 in tax was paid according to the receipt, not $5.00 as Claude 3 Opus states.

We then asked about a digital document: a menu. We provided the following photo of a menu with the prompt “How much does the pastrami pizza cost?”:

The model responded:

The pastrami pizza on the menu costs $27.

Claude 3 Opus was able to successfully answer the question.

Test #4: Visual Question Answering

Answering questions about images is an essential part of evaluating the performance of multimodal models. Indeed, the context that multimodal models learn is potentially invaluable in answering questions about an image. We provided the following image to Claude 3 Opus, with the prompt “How much money do I have?”:

Claude 3 Opus responded with the following text:

Based on the four coins shown in the image, you have 61 cents (or 0.61 Polish złoty). The coins appear to be:\n\n1 coin of 1 grosz (0.01 PLN)\n1 coin of 20 groszy (0.20 PLN) \n2 coins of 20 groszy each, totaling 40 groszy (0.40 PLN)\n\nTogether, the four coins sum to 61 groszy, which is equivalent to 0.61 Polish złoty.

Claude 3 Opus did not answer the question correctly. This is a task that other models have successfully answered, including CogVLM, Gemini, and Qwen-VL-Plus.

We then asked the model “What scene is this movie from?”, providing a photo from the Home Alone movie as context:

The model responded:

This scene is from the classic Christmas movie "Home Alone". The image shows the McCallister family\'s lavishly decorated living room with a large Christmas tree, fireplace hung with stockings, and lots of wrapped presents. A young boy, the main character Kevin played by Macaulay Culkin, sits amidst the festive holiday decor.

Claude 3 Opus successfully answered the question. Most models we tested have been able to answer this question accurately, including GPT-4 with Vision and Qwen-VL-Plus.

Test #5: Object Detection

Finally, we tested Claude 3 Opus on a task that has proved challenging for many multimodal language models: detecting objects.

We provided a photo of a person and a dog to the Claude 3 Opus API, accompanied by the prompt “Detect a dog on the image. Provide me with x_min, y_min, x_max and y_max coordinates."

The API returned:

The image shows a beagle dog sitting on a brick patio or walkway near the entryway of a building. The entryway has an arched brick ceiling with hanging light bulbs, providing a warm and inviting atmosphere. In the background through the archway, a parked vehicle can be seen outside.\n\nThe coordinates for the bounding box around the dog are approximately:\n\nx_min: 550\ny_min: 470\nx_max: 1020\ny_max: 1080\n\nThe coordinates are based on the full resolution 1440x1080 pixel image provided.

When we ran the test several times, the bounding box coordinates were different. In no cases was the model able to accurately and precisely plot the location of the dog.

Most multimodal models are unable to answer this question accurately. In our testing, only CogVLM and Qwen-Vl-Plus were able to accurately identify the location of the dog.


Claude 3 offers a series of multimodal models developed by Anthropic. You can ask Claude 3 questions in text and provide one or more images as context. In this guide, we walked through several tests the Roboflow team ran on Claude 3 as part of a qualitative analysis of the model.

We found that Claude 3 Opus, the best available model, performs well on some tasks according to our limited tests. We found success in visual question answering. The model passed one of two of our document question answering prompts, and successfully read text on a tire as part of our OCR testing.

With that said, the model struggles on other tasks. Claude 3, like most multimodal models, is unable to localise objects in an object detection test. The model answered one visual question answering prompt correctly (the movie featured in an image), and failed at another (currency counting).

Unlike most models, however, it refused to run OCR on text where a celebrity’s name was mentioned on copyright grounds, despite copyright being owned by the author of the content.

We have run the above analyses on several other models: