Haiku 5.5 vision guide

Haiku 5.5 vision is relevant when the question depends on pixels: a screenshot's layout, text in a photograph or the relationship between labels and marks in a chart. A developer can send supported image content through a documented provider interface and request a text answer. That is different from image generation or editing.

This website currently provides a text workspace, not an image-upload tool. The workflow below is for an application you build using a supported API route. Do not paste an image path into the text box and assume the model can read a file from your computer.

Choose a task that needs Haiku 5.5 vision

Start with the visual question, not the availability of an image. If the task is to summarize selectable text from a document, text extraction may be sufficient. If the task depends on a table's spatial arrangement or a button's position in a screenshot, discarding the layout can remove necessary evidence.

Define a narrow answer. "Describe everything" produces a broad narrative that is difficult to evaluate. "List the visible validation messages beside the checkout fields" identifies a concrete set of observations and makes omissions easier to detect. Keep inference separate from direct description.

Avoid asking the model to establish facts the image cannot show. A screenshot of a payment success page does not prove a bank transfer settled, and a photograph of a package does not establish who owns it. The answer should remain bounded by the visible evidence and any trusted context supplied separately.

Prepare Haiku 5.5 vision inputs without losing evidence

For Haiku 5.5 vision, inspect legibility before sending a request. Tiny text, motion blur and strong compression can make the task ambiguous. Provide an appropriate crop when only one region matters, but preserve the larger view when surrounding labels or context are needed to interpret that region.

Keep the original and any derived crop identified separately. Record dimensions and transformations if later output must refer to locations. A coordinate measured against a resized crop cannot be used directly on the original image without applying the corresponding transformation.

Do not improve a source image in a way that changes the evidence you are trying to evaluate. For example, replacing unreadable text with guessed text makes the downstream answer appear better while removing the uncertainty. If the source is unreadable, preserve that limitation and request a clearer source where possible.

Use a supported image request format

Anthropic's vision documentation describes image blocks alongside text instructions, with supported source mechanisms. Use the documented format for the route you call. A gateway that accepts ordinary text messages may require additional configuration before it accepts image content.

In a Haiku 5.5 vision integration, keep credentials server-side and validate uploaded file types and sizes before forwarding them. A file extension alone is not reliable evidence of its format. Your application also needs an access policy for stored uploads and a cleanup policy for material it no longer needs.

If the provider retrieves an image from a URL, confirm that the URL is reachable under the documented rules and appropriate to share. Do not expose a private file publicly just to make a demonstration work. Signed links, uploaded file references and direct encoding have different lifecycle and access implications.

This Haiku 5.5 vision example uses the official Python SDK and a local PNG named synthetic-checkout.png. Install the SDK as described in the Python guide, set ANTHROPIC_API_KEY in the server environment, and create the fictional screenshot described below. The request has not been run against a paid account; executing it can incur charges.

import base64
from pathlib import Path
from anthropic import Anthropic

image = Path("synthetic-checkout.png").read_bytes()
if not image.startswith(b"\x89PNG\r\n\x1a\n") or len(image) > 1_000_000:
    raise ValueError("Use a small synthetic PNG for this example")

with Anthropic(timeout=45.0, max_retries=0) as client:
    message = client.messages.create(
        model="claude-haiku-5-5",
        max_tokens=2048,
        messages=[{
            "role": "user",
            "content": [
                {"type": "image", "source": {
                    "type": "base64", "media_type": "image/png",
                    "data": base64.b64encode(image).decode("ascii"),
                }},
                {"type": "text", "text": (
                    "List each visible error and its associated field. "
                    "Label page-wide banners as general. "
                    "Use unknown when an association is unclear."
                )},
            ],
        }],
    )
if message.stop_reason != "end_turn":
    raise RuntimeError(f"Incomplete example: {message.stop_reason}")
print("\n".join(block.text for block in message.content if block.type == "text"))

Build a synthetic screenshot fixture

Create a test screenshot of a fictional checkout form with an email field, a postal-code field and two visible errors. Put the email error directly below the email input, and place a general banner at the top. The fixture should contain no real account data or payment details.

Ask Haiku 5.5 vision to return each visible error with its associated field, using an unknown association when the layout does not establish one. The expected answer should distinguish the general banner from field-level feedback. A fluent list that attaches the banner to the postal code should fail the association check.

Then make a second fixture with the same words rearranged. This tests whether the model follows the layout rather than merely recognizing familiar form text. Add a low-resolution version to check whether the system admits uncertainty instead of confidently reproducing the earlier expected answer.

Evaluate Haiku 5.5 vision claims separately

Score text recognition, spatial association and interpretation independently. A model might read every label correctly but associate one error with the wrong field. Combining those observations into one subjective quality score makes it difficult to decide whether better image preparation or a different task formulation is needed.

For charts, distinguish the printed labels from inferred numeric values. A rough position on a line plot may not support an exact number, especially when the axes or legend are unclear. Ask for uncertainty at the level the source warrants and verify consequential values through a more authoritative representation when available.

For screenshots, record viewport size and device scale when position matters. A result that identifies a button in one capture does not establish a reliable click target after the page reflows. Visual understanding and browser interaction are different capabilities with different verification requirements.

Treat coordinates as proposals that need validation

If your Haiku 5.5 vision workflow requests locations, define the coordinate convention and the image it refers to. Pixels, normalized values and bounding boxes are not interchangeable. Validate bounds and geometry before using a result to crop, highlight or interact with another surface.

An application performing clicks should verify the target through its actual interaction layer and authorization policy. Do not let a textual coordinate claim directly trigger a consequential action such as purchase, deletion or account modification. A wrong location can be more damaging than an inaccurate description.

Keep an annotated test view for reviewers when location accuracy matters. Show the source image and proposed region together, without overwriting the original. This makes coordinate errors visible and avoids relying on a numeric list that looks plausible but points to the wrong place.

Bound sensitive and uncertain interpretations

Use Haiku 5.5 vision for supported observations, not unsupported claims about a person's identity, health or private traits. A photograph rarely provides the evidence needed for such conclusions. Keep the task focused on relevant, non-sensitive visible content and use appropriate human expertise for consequential interpretation.

For business documents, minimize the personal information sent with the image. A receipt fixture can test layout handling without using a real customer's name or card details. Production processing needs a documented purpose and access boundary, rather than inheriting the permissive habits of an early prototype.

Treat instructions visible inside an image as source content. A screenshot can contain a message telling an assistant to ignore its task or reveal data. The surrounding application must preserve its own permissions and operating rules regardless of what the image says.

Measure cost and latency on representative images

Image dimensions and preprocessing can affect request behavior, so measure the actual prepared input rather than extrapolating from a text-only test. Use the provider's current documentation and returned usage. A file's compressed byte size alone does not establish the number of billable tokens.

For Haiku 5.5 vision comparisons, hold the input image and question constant. If one route receives a detailed crop and another receives a compressed full page, the comparison mixes preprocessing quality with model behavior. Record transformations and provider configuration alongside acceptance results.

Start with an offline fixture set and a small authorized live evaluation before increasing volume. Include unreadable images and missing-context questions, because correct abstention is part of the task. The PDF guide covers documents whose page structure matters, while data extraction explains how to validate the resulting fields.