Helonic vs ChatGPT and Claude for construction drawing review

For one sheet and one question, yes. Paste in a detail and ask what a callout means, or which code section a note is pointing at, and you'll usually get a useful answer. Our longer piece on whether ChatGPT can review construction drawings walks through those cases.

Reviewing a set is a different job. A reviewer works through every sheet, compares plans to sections, schedules, and details, and ends up with a list of issues someone can assign. A chat assistant answers the question you typed about the files in that conversation. If you don't ask about the door schedule, it won't check the door schedule.

The caps are generous on file size and tight on how drawings get read. OpenAI's file uploads help page lists 512 MB per document, 2 million tokens per text or document file, 20 MB per image, and 80 uploads every 3 hours. Free accounts get 3 uploads a day.

Anthropic's Claude upload guide allows 500 MB per file, 20 files per chat, and PDFs up to 1,000 pages. The catch for drawings: Claude analyzes images and graphics only in PDFs of 100 pages or fewer. From 101 to 1,000 pages, it reads the text and skips the visual content, which is where linework lives.

Resolution is the other limit. Anthropic's vision documentation for its API says images are downscaled to a maximum long edge of 2,576 pixels on its newest models, and warns that resizing can make text less legible. A 36-inch sheet rendered at 150 dpi is 5,400 pixels wide, so it gets cut to under half its width before the model reads it.

Many drawing errors only show up when you compare two sheets. A duct on M-201 runs through a beam on S-301. A detail callout on A-501 points at a detail that was never drawn. A door tag on the plan doesn't match the hardware set in the schedule. Each sheet looks fine alone.

Helonic reads the set as a set and reports the conflict on the sheets where it shows up. With a chat tool, you'd have to know which two sheets to compare and ask about them, which assumes you've already found the problem. The guide to reviewing construction drawings covers the cross-checks a full review needs.

Helonic returns the same finding format on every review. Each finding has one of ten issue categories (coordination, code compliance, missing information, structural, MEP, fire safety, accessibility, constructability, dimensions, and QA/QC), a high, medium, or low severity, and the page coordinates of the cited spot.

A chat answer is shaped by the prompt. Ask twice with different wording and you can get a different structure, different labels, and a different idea of what counts as high severity. Anthropic's own docs say Claude's coordinate and localization outputs are approximate and should be verified before you rely on them. That matters when the next step is sending a sub to the right grid line.

Helonic checks the model codes IBC, NEC, NFPA, and ADA Standards plus state and local adopted codes such as the CBC, FBC, and NYC Building Code, and each finding names the sheet and code section. Your reviewer still confirms the edition in force for the jurisdiction.

ChatGPT and Claude will cite code sections if you ask. Treat each citation as a lead to verify against the adopted edition, since a general model can quote a section number with confidence that the text doesn't support. Helonic's code compliance checks run as part of the same review as coordination.

In Helonic, a reviewer accepts or edits each finding before anything is created in Procore or Autodesk Construction Cloud. Approved findings become RFIs with the sheet reference attached. See RFI generation and the Procore integration.

With a chat tool, the output is text. Someone copies it into the RFI log, finds the sheet, and adds the markup by hand. That's fine for two issues and slow for two hundred.

Check the plan before you upload. OpenAI says it doesn't use content from business offerings like ChatGPT Enterprise and the API to improve models; for consumer accounts it depends on your data settings. Anthropic's consumer terms update says Free, Pro, and Max chats are used for training only if the user allows it, with 5-year retention when allowed and 30 days otherwise. Team, Enterprise, and API use are excluded.

Helonic is SOC 2 compliant, encrypts data in transit with TLS 1.2 or higher, and encrypts it at rest with AES-256. The details are on the Helonic security page. For the questions to ask any vendor, read the data security questions for AI drawing review and is AI drawing review software secure?

Plenty of drawing work is a single question. A chat assistant earns its keep on these:

• Explaining an unfamiliar symbol, abbreviation, or detail you pasted in.
• Drafting or tightening an RFI, a transmittal, or an email to the architect.
• Summarizing a spec section or a code passage you supplied as text.

Helonic hasn't published a head-to-head benchmark against ChatGPT or Claude, so run your own. Use a set you've already reviewed by hand, so you have an answer key.

1. Pick one finished set. Use one with a markup log or RFI log you trust, and list the real issues before you start.
2. Give every tool the same files. If a chat tool's page or size cap forces you to split the PDF, record that as part of the result.
3. Use the same instructions. Write one scope (disciplines, code families, what counts as an issue) and give it to each tool word for word.
4. Count verified findings. A finding counts only if a reviewer confirms it on the sheet.
5. Count false alarms. Note anything that isn't on the drawings, including invented dimensions or code sections.
6. Check location accuracy. Open each cited sheet and see whether the location lands on the problem.
7. Compare against your answer key. List what each tool missed, and time how long verification took.

The accuracy guide for AI drawing review explains precision and recall if you want to score the results. The pillar on AI for construction drawings covers what purpose-built review does differently.