This guide describes how to work with images in Claude, including best practices, code examples, and limitations to keep in mind.

​
How to use vision

Use Claude’s vision capabilities via:
  • claude.ai. Upload an image like you would a file, or drag and drop an image directly into the chat window.
  • The Console Workbench. If you select a model that accepts images (Claude 3 and 4 models only), a button to add images appears at the top right of every User message block.
  • API request. See the examples in this guide.

​
Before you upload

​
Basics and Limits

You can include multiple images in a single request (up to 20 for claude.ai and 100 for API requests). Claude will analyze all provided images when formulating its response. This can be helpful for comparing or contrasting images. If you submit an image larger than 8000x8000 px, it will be rejected. If you submit more than 20 images in one API request, this limit is 2000x2000 px.
While the API supports 100 images per request, there is a 32MB request size limit for standard endpoints.

​
Evaluate image size

For optimal performance, we recommend resizing images before uploading if they are too large. If your image’s long edge is more than 1568 pixels, or your image is more than ~1,600 tokens, it will first be scaled down, preserving aspect ratio, until it’s within the size limits. If your input image is too large and needs to be resized, it will increase latency of time-to-first-token, without giving you any additional model performance. Very small images under 200 pixels on any given edge may degrade performance.
To improve time-to-first-token, we recommend resizing images to no more than 1.15 megapixels (and within 1568 pixels in both dimensions).
Here is a table of maximum image sizes accepted by our API that will not be resized for common aspect ratios. With the Claude Sonnet 3.7 model, these images use approximately 1,600 tokens and around $4.80/1K images.
Aspect ratioImage size
1:11092x1092 px
3:4951x1268 px
2:3896x1344 px
9:16819x1456 px
1:2784x1568 px

​
Calculate image costs

Each image you include in a request to Claude counts towards your token usage. To calculate the approximate cost, multiply the approximate number of image tokens by the per-token price of the model you’re using. If your image does not need to be resized, you can estimate the number of tokens used through this algorithm: tokens = (width px * height px)/750 Here are examples of approximate tokenization and costs for different image sizes within our API’s size constraints based on Claude Sonnet 3.7 per-token price of $3 per million input tokens:
Image size# of TokensCost / imageCost / 1K images
200x200 px(0.04 megapixels)~54~$0.00016~$0.16
1000x1000 px(1 megapixel)~1334~$0.004~$4.00
1092x1092 px(1.19 megapixels)~1590~$0.0048~$4.80

​
Ensuring image quality

When providing images to Claude, keep the following in mind for best results:
  • Image format: Use a supported image format: JPEG, PNG, GIF, or WebP.
  • Image clarity: Ensure images are clear and not too blurry or pixelated.
  • Text: If the image contains important text, make sure it’s legible and not too small. Avoid cropping out key visual context just to enlarge the text.

​
Prompt examples

Many of the prompting techniques that work well for text-based interactions with Claude can also be applied to image-based prompts. These examples demonstrate best practice prompt structures involving images.
Just as with document-query placement, Claude works best when images come before text. Images placed after text or interpolated with text will still perform well, but if your use case allows it, we recommend an image-then-text structure.

​
About the prompt examples

The following examples demonstrate how to use Claude’s vision capabilities using various programming languages and approaches. You can provide images to Claude in three ways:
  1. As a base64-encoded image in image content blocks
  2. As a URL reference to an image hosted online
  3. Using the Files API (upload once, use multiple times)
The base64 example prompts use these variables: 
    # For URL-based images, you can use the URL directly in your JSON request
    
    # For base64-encoded images, you need to first encode the image
    # Example of how to encode an image to base64 in bash:
    BASE64_IMAGE_DATA=$(curl -s "https://upload.wikimedia.org/wikipedia/commons/a/a7/Camponotus_flavomarginatus_ant.jpg" | base64)
    
    # The encoded data can now be used in your API calls
Below are examples of how to include images in a Messages API request using base64-encoded images and URL references:

​
Base64-encoded image example

curl https://api.anthropic.com/v1/messages \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-sonnet-4-20250514",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "image",
            "source": {
              "type": "base64",
              "media_type": "image/jpeg",
              "data": "'"$BASE64_IMAGE_DATA"'"
            }
          },
          {
            "type": "text",
            "text": "Describe this image."
          }
        ]
      }
    ]
  }'

​
URL-based image example

curl https://api.anthropic.com/v1/messages \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-sonnet-4-20250514",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "image",
            "source": {
              "type": "url",
              "url": "https://upload.wikimedia.org/wikipedia/commons/a/a7/Camponotus_flavomarginatus_ant.jpg"
            }
          },
          {
            "type": "text",
            "text": "Describe this image."
          }
        ]
      }
    ]
  }'

​
Files API image example

For images you’ll use repeatedly or when you want to avoid encoding overhead, use the Files API: 
# First, upload your image to the Files API
curl -X POST https://api.anthropic.com/v1/files \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "anthropic-beta: files-api-2025-04-14" \
  -F "file=@image.jpg"

# Then use the returned file_id in your message
curl https://api.anthropic.com/v1/messages \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "anthropic-beta: files-api-2025-04-14" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-sonnet-4-20250514",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "image",
            "source": {
              "type": "file",
              "file_id": "file_abc123"
            }
          },
          {
            "type": "text",
            "text": "Describe this image."
          }
        ]
      }
    ]
  }'
See Messages API examples for more example code and parameter details.
It’s best to place images earlier in the prompt than questions about them or instructions for tasks that use them.Ask Claude to describe one image.
RoleContent
User[Image] Describe this image.
Here is the corresponding API call using the Claude Sonnet 3.7 model.
Python
message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": image1_media_type,
                        "data": image1_data,
                    },
                },
                {
                    "type": "text",
                    "text": "Describe this image."
                }
            ],
        }
    ],
)
In situations where there are multiple images, introduce each image with Image 1: and Image 2: and so on. You don’t need newlines between images or between images and the prompt.Ask Claude to describe the differences between multiple images.
RoleContent
UserImage 1: [Image 1] Image 2: [Image 2] How are these images different?
Here is the corresponding API call using the Claude Sonnet 3.7 model.
Python
message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Image 1:"
                },
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": image1_media_type,
                        "data": image1_data,
                    },
                },
                {
                    "type": "text",
                    "text": "Image 2:"
                },
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": image2_media_type,
                        "data": image2_data,
                    },
                },
                {
                    "type": "text",
                    "text": "How are these images different?"
                }
            ],
        }
    ],
)
Ask Claude to describe the differences between multiple images, while giving it a system prompt for how to respond.
Content
SystemRespond only in Spanish.
UserImage 1: [Image 1] Image 2: [Image 2] How are these images different?
Here is the corresponding API call using the Claude Sonnet 3.7 model.
Python
message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    system="Respond only in Spanish.",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Image 1:"
                },
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": image1_media_type,
                        "data": image1_data,
                    },
                },
                {
                    "type": "text",
                    "text": "Image 2:"
                },
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": image2_media_type,
                        "data": image2_data,
                    },
                },
                {
                    "type": "text",
                    "text": "How are these images different?"
                }
            ],
        }
    ],
)
Claude’s vision capabilities shine in multimodal conversations that mix images and text. You can have extended back-and-forth exchanges with Claude, adding new images or follow-up questions at any point. This enables powerful workflows for iterative image analysis, comparison, or combining visuals with other knowledge.Ask Claude to contrast two images, then ask a follow-up question comparing the first images to two new images.
RoleContent
UserImage 1: [Image 1] Image 2: [Image 2] How are these images different?
Assistant[Claude’s response]
UserImage 1: [Image 3] Image 2: [Image 4] Are these images similar to the first two?
Assistant[Claude’s response]
When using the API, simply insert new images into the array of Messages in the user role as part of any standard multiturn conversation structure.

​
Limitations

While Claude’s image understanding capabilities are cutting-edge, there are some limitations to be aware of:
  • People identification: Claude cannot be used to identify (i.e., name) people in images and will refuse to do so.
  • Accuracy: Claude may hallucinate or make mistakes when interpreting low-quality, rotated, or very small images under 200 pixels.
  • Spatial reasoning: Claude’s spatial reasoning abilities are limited. It may struggle with tasks requiring precise localization or layouts, like reading an analog clock face or describing exact positions of chess pieces.
  • Counting: Claude can give approximate counts of objects in an image but may not always be precisely accurate, especially with large numbers of small objects.
  • AI generated images: Claude does not know if an image is AI-generated and may be incorrect if asked. Do not rely on it to detect fake or synthetic images.
  • Inappropriate content: Claude will not process inappropriate or explicit images that violate our Acceptable Use Policy.
  • Healthcare applications: While Claude can analyze general medical images, it is not designed to interpret complex diagnostic scans such as CTs or MRIs. Claude’s outputs should not be considered a substitute for professional medical advice or diagnosis.
Always carefully review and verify Claude’s image interpretations, especially for high-stakes use cases. Do not use Claude for tasks requiring perfect precision or sensitive image analysis without human oversight.

​
FAQ

Claude currently supports JPEG, PNG, GIF, and WebP image formats, specifically:
  • image/jpeg
  • image/png
  • image/gif
  • image/webp
Yes, Claude can now process images from URLs with our URL image source blocks in the API. Simply use the “url” source type instead of “base64” in your API requests. Example:
{
  "type": "image",
  "source": {
    "type": "url",
    "url": "https://upload.wikimedia.org/wikipedia/commons/a/a7/Camponotus_flavomarginatus_ant.jpg"
  }
}
Yes, there are limits:
  • API: Maximum 5MB per image
  • claude.ai: Maximum 10MB per image
Images larger than these limits will be rejected and return an error when using our API.
The image limits are:
  • Messages API: Up to 100 images per request
  • claude.ai: Up to 20 images per turn
Requests exceeding these limits will be rejected and return an error.
No, Claude does not parse or receive any metadata from images passed to it.
No. Image uploads are ephemeral and not stored beyond the duration of the API request. Uploaded images are automatically deleted after they have been processed.
Please refer to our privacy policy page for information on how we handle uploaded images and other data. We do not use uploaded images to train our models.
If Claude’s image interpretation seems incorrect:
  1. Ensure the image is clear, high-quality, and correctly oriented.
  2. Try prompt engineering techniques to improve results.
  3. If the issue persists, flag the output in claude.ai (thumbs up/down) or contact our support team.
Your feedback helps us improve!
No, Claude is an image understanding model only. It can interpret and analyze images, but it cannot generate, produce, edit, manipulate, or create images.

​
Dive deeper into vision

Ready to start building with images using Claude? Here are a few helpful resources: If you have any other questions, feel free to reach out to our support team. You can also join our developer community to connect with other creators and get help from Anthropic experts.