Multimodal Reasoning Capabilities

Unlike older models that used a separate vision model to describe an image, Claude is natively multimodal. It doesn't just see pixels; it understands concepts across text and vision simultaneously.

Cross-Modality Reasoning Tasks

1. Visual Proof

Query: "Is the signature on the contract valid?"

• Context: The text of a legal contract + a photo of the signature.
• Reasoning: Claude checks the text for the required signer's name and looks at the photo to confirm if the signature matches the name or looks authentic.

2. Diagram Analysis

Query: "Looking at the system diagram, what happens if Node B fails?"

• Context: Architectural diagram (image).
• Reasoning: Claude identifies the nodes, traces the lines (connections), and logically simulates the failure based on the visual flow.

3. Spatial Localization

Claude can identify where something is in an image. You can ask for coordinates or "bounding boxes" for objects.

Practical Example: Receipt Processing

You can provide a photo of a messy receipt and a text list of "Policy Rules" (e.g., "No alcohol allowed on expense reports").

• Task: Claude reads the items on the receipt (OCR) and compares them against the policy list.

Implementation with Vision

# Conceptual example sending an image to Claude
content = [
    {
        "type": "image",
        "source": {
            "type": "base64",
            "media_type": "image/jpeg",
            "data": image_base64_data,
        },
    },
    {
        "type": "text",
        "text": "What is the total amount on this receipt?"
    }
]

Limitations to Consider

1. Resolution: Extremely small text (under 10px) may be misread.
2. Counting: While improving, Claude may struggle to count 50+ identical objects in a complex photo.
3. Logic Flaws: It might "see" what it expects to see if the text context is very persuasive.

Exercises

1. Upload an architectural diagram. Ask Claude to explain the "single point of failure."
2. Can Claude identify "Emotions" in a photo?
3. What is the benefit of providing a text description alongside an image for a RAG system?