ChatGPT vs Gemini for Architects: We Ran 12 Real Tasks

In our ChatGPT vs Gemini for architects test of 12 real tasks on 20 October 2026, Gemini (Gemini 4 Argon) wrote the deeper reviews of briefs, RFPs and outline specs that day, while ChatGPT (GPT-6 Astra) followed instructions more tightly and caught a calendar error that Gemini copied through. Seven of the twelve tasks tied. For most practices the choice comes down to working style, whether your office already uses Google Workspace, and which plan tier actually contains the model we tested.

The twelve tasks are the same fixtures we used two days earlier in our Claude vs ChatGPT test: a community-centre brief with planted conflicts, a library RFP with an uncapped indemnity, a 2021 IBC egress lookup, an outline spec, drawing notes that contradict their spec, meeting notes, a fee structure, area arithmetic, and client writing under word caps. We read all 24 transcripts in full and scored them against answers we knew in advance. The Gemini side is new material, and the ChatGPT vs Gemini for architects question is the natural one for an office that already lives in Google Workspace.

How did we run the test - and what can it not tell you?

We sent each of the 12 tasks to both model families on 20 October 2026, run programmatically from a command-line client, with a single run per task. The Gemini side was Gemini 4 Argon at medium effort; the ChatGPT side was GPT-6 Astra. Each task had a prepared document with errors we planted, so we graded the answers against known results. Treat the result as a working comparison with no statistical weight.

Three method notes.

1. Models only, no apps. Nothing here touched chatgpt.com or gemini.google.com; we ran the models underneath them from a command-line client. Upload handling, image generation, Deep Research, Gmail and Docs integration, Gems and custom GPTs all sat outside the test; we compare them from vendor documentation in the app feature table, labelled as untested.
2. Citation behaviour was uneven, and we kept it. The GPT transcripts contain linked citations on two tasks (the fee structure and the clause review, 6 and 11 inline citations pointing at 3 and 4 distinct pages); the Gemini transcripts contain no URLs in any of the 12 outputs, and we report that as a finding rather than correcting for it.
3. One run per task. When we say a model “edged” a task, we mean its transcript was the better one that day on material we controlled.

All 24 runs completed, with outputs from 138 to 2,217 words. Medium is the middle of Gemini’s three effort settings, and we did not test the others.

The 12 tasks: results at a glance

Gemini edged three tasks (the brief audit, the RFP go/no-go and the outline spec), ChatGPT edged two (the drawing-vs-spec check and the meeting minutes), and seven were even. Both models were exact on arithmetic and both cited the correct 2021 IBC section numbers. On this set, Gemini’s edges came from depth on open-ended documents; ChatGPT’s from precision and date reasoning.

#	Task	What we checked	Gemini (4 Argon, medium)	ChatGPT (GPT-6 Astra)	Verdict
1	Client brief audit	3 planted conflicts (area, budget, parking)	All 3 found, 17 spaces computed, plus a 4th unplanted conflict and net-to-gross expansion to 1,075-1,140 m2	All 3 found, arithmetic right, declined to flag anything beyond the text	Gemini, on depth
2	RFP go/no-go	Uncapped liability, free interview work, fit for a 6-person firm	Flagged the indemnity as likely uninsurable, priced the $5M E&O floor to firm size, tied three of its five actions to the questions deadline	Same core risks, counsel and insurer review, “conditional pursuit”	Gemini, narrowly
3	Design statement, 400-word cap	Required topics, no invention	197 words, all 5 topics, [NEEDS INPUT] honoured	145 words, all 5 topics, [NEEDS INPUT] honoured	Even
4	IBC 2021 egress lookup	3 values + exact section numbers	All 3 values and all 3 sections right; warned about 2018-to-2021 renumbering; 1,308 words	All 3 values and all 3 sections right; noted Exceptions 2 and 3 of 1020.5; 410 words	Even on accuracy
5	CSI outline spec 08 11 13	3-part structure, standards flagged, no invented products	Full outline, 54 [VERIFY EDITION] tags, widest reference list	Correct compact outline, 21 tags	Gemini
6	Drawing vs spec check	5 planted contradictions + 1 dead reference	6 of 6, dead ref folded into the contradictions table, one extra unplanted flag	6 of 6, dead ref separated, non-contradictions listed, zero false positives	ChatGPT
7	Client delay email, 250-word cap	Structure, tone, limit	229 words, plan-first	195 words, plan-first	Even
8	Meeting minutes from raw notes	Accuracy, TBC discipline	Clean minutes; copied “Fri 24 Oct” unflagged	Clean minutes; caught that 24 October 2026 is a Saturday	ChatGPT
9	Fee proposal structure	No fee benchmarks quoted	Complied; 2,217 words, the longest output of the set	Complied; cited AIA basic-services resources with URLs	Even
10	Area program arithmetic	5 calculations	All exact (549 net, 741.15 gross, 21.15 over, cut 15.67, 74.07%)	All exact, identical numbers	Even
11	Client explanation, 150-word cap	Plain language, limit	142 words, within cap	138 words, within cap	Even
12	Indemnification clause review	Risk spotting, not-legal-advice framing	Duty to defend, insurability gap, no cap, one-way transfer; zero URLs	Same map in 11 distinct risks, linked AIA resources	Even

Tested: 20 October 2026, versions: Gemini 4 Argon (medium effort) and GPT-6 Astra, each run programmatically from a command-line client, single run per task.

Gemini’s outputs ran about 50% longer by median, and where the sibling test two days earlier split 3-3-6 (Claude, ChatGPT, even), this one split 3-2-7 (Gemini, ChatGPT, even).

Where did Gemini pull ahead? Depth on briefs, RFPs and specs

Gemini won where the document was open-ended and the value lay in what a reviewer notices beyond the question asked. On the brief audit it found all three planted conflicts, then surfaced a fourth we had not planted. On the RFP it priced the insurance requirement to the firm’s size. On the outline spec it flagged more than twice as many reference standards for edition checks.

The brief fixture hides a room schedule totalling 912 m2 inside a stated 850 m2 gross floor area, a EUR 2.1M budget against a EUR 1.8M financing ceiling, and 12 parking spaces where the zoning ratio demands 17. Both models caught all three and got 17 right.

Gemini kept going. It expanded the net schedule to a gross of 1,075 to 1,140 m2 and questioned how a foyer-connected hall could be let independently in the evenings. ChatGPT stopped at the text, stating that no other definite conflict could be established from it.

The RFP review split the same way. Gemini called the broad indemnification plus uncapped liability likely uninsurable, estimated what a $5M E&O requirement means for a six-person firm, and tied three of its five actions to the RFP’s 31 October questions deadline. ChatGPT flagged the identical core risks and recommended a conditional pursuit after counsel and insurer review.

On the outline spec, Gemini produced 54 [VERIFY EDITION] tags to ChatGPT’s 21 and a wider reference set (ASTM B633, C518, C1363 and E90, plus NFPA 105); neither invented a product number. A tagged outline is still a draft for the specifier: before anything is issued, every referenced standard and edition has to be checked against the project’s current master and the products actually specified. Our spec-writing guide covers turning a tagged outline into a section you can issue.

A practitioner described the same split in a December 2025 r/ChatGPT thread: “Gemini is great at information retention for long documents. ChatGPT will just partially read something and then lie, Gemini reads it all. The downside is that ChatGPT can creatively interpret the document whereas Gemini tends to look at it very literally” (u/Aztecah). Our transcripts support the first half; on the brief it was Gemini that interpreted and ChatGPT that stayed literal.

The brief-audit prompt follows, generalised for your own document.

You are reviewing a client brief for a [building type] on behalf of the architect. Read the full brief pasted below, then return four things. 1) The space schedule as a table with the stated area for each room. 2) Every numeric requirement in the brief (areas, budgets, financing limits, dates, parking, occupancy) with the exact sentence it comes from. 3) Any two requirements that cannot both be true, with the arithmetic that proves it. 4) Decisions the brief leaves open that the client must settle before schematic design starts. Quote the brief verbatim for anything you flag, and keep figures you calculated separate from figures the brief states.

BRIEF:
[paste the brief here]

The recommendation does not apply if your week consists of short-form writing. On the three writing tasks with word caps, Gemini’s extra depth bought nothing; both models landed inside every limit with the same coverage.

Where did ChatGPT pull ahead? Precision and checkable facts

ChatGPT won on the two tasks where the correct answer was narrow and any addition counted against you. On the drawing-vs-spec check it found all five planted contradictions plus the dead reference, separated the two kinds of error, listed what was consistent, and raised no false positive. On the meeting minutes it caught that “Fri 24 Oct” cannot exist in 2026.

Gemini found the same six issues on the consistency check and then added noise. The dead 09 91 23 reference went into a six-row contradictions table. Gemini also flagged the need to verify that a Finish Schedule exists, an issue we had not planted in the fixture. In a coordination review that extra row is one more item a junior has to investigate and close.

Our meeting-notes fixture contains an accidental error, “Fri 24 Oct”, and 24 October 2026 falls on a Saturday. ChatGPT wrote that 24 October 2026 is a Saturday, named Friday 23 October as the alternative, and marked the date TBC, while Gemini copied the date into its action table without comment. That is the cleanest single result in the set.

The code lookup result differs from our Claude vs ChatGPT test two days earlier. Both models returned the correct egress values for a business occupancy under the 2021 IBC (44 inches corridor width at an occupant load of 60, 7 feet 6 inches ceiling height, 20 feet dead end unsprinklered) and both cited the correct 2021 sections, 1020.3, 1003.2 and 1020.5, as we confirmed against the ICC’s published code text (checked 18 October 2026). ChatGPT added Exceptions 2 and 3 of 1020.5 in 410 words. Gemini warned about the 2018-to-2021 renumbering and ran to 1,308 words, pulling in A117.1, the IEBC and section 1006.3.4, each of which you would also have to verify.

Professional liability. Neither model knows which amendments your authority has adopted, which edition is in force on your site, or what your plan checker will accept. Our 18 October Claude run returned a right value under a 2018 section number; this run both models were correct, and that proves nothing about the next question or your code book. The failure mode is a confident wrong section number that reads exactly like a right one. AI output here is a starting point: you must verify it, as the licensed professional, against the currently adopted code edition in the applicable jurisdiction. Our guide to AI building-code checks sets out a verification routine.

The precision pattern extends to citations. ChatGPT linked AIA and AIA Contract Documents resources on the fee structure (6 inline citations to three pages) and the clause review (11 citations to four AIA pages); Gemini linked nothing on any task. We have not audited every link, and an open link is still something you can check. An architect wrote in a February 2025 r/architecture thread: “I have a client tell my I’m wrong about CBC 11a code requirement because chat gpt said something different.” (u/studiotankcustoms).

Consistency-check prompt, generalised for your own drawing set.

Below are the drawing notes and the specification excerpt for a [building type]. Compare them and list every point where the two documents disagree, as a table with these columns: topic, what the drawing notes say, what the specification says, which document is more likely wrong. Separately, list any reference in either document to a section or document that is absent from the document list I provide. Then list the items you checked that are consistent, so I can see what you covered. Do not report a contradiction unless both statements are present in the text.

DRAWING NOTES:
[paste]

SPECIFICATION EXCERPT:
[paste]

DOCUMENT LIST:
[paste the list of sections and drawings that exist]

Reverse the pick for open-ended review: on the brief and the RFP, ChatGPT’s strictness is what kept it from finding the fourth conflict and from pricing the insurance clause to the firm.

The seven ties - and the one real difference behind them

Seven tasks produced interchangeable results: the design statement, the egress lookup, the delay email, the fee structure, the area arithmetic, the client explanation and the clause review. Both models returned exact arithmetic on all five calculations and both stayed inside every word cap. What separated the tied transcripts was length and formatting.

Gemini’s median output was 581 words against ChatGPT’s 390. On the fee proposal structure Gemini wrote 2,217 words, the most exhaustive version of that task we have seen on this fixture; ChatGPT wrote a complete template and linked AIA phase definitions. Which you prefer depends on whether you read AI output or skim it, and for routine work that preference is a sound basis for choosing between them.

A formatting quirk showed only in the raw transcripts. In four tasks (the brief audit, the RFP, the egress lookup and the area arithmetic) Gemini wrote units and sums in LaTeX-style markup, with $\text{m}^2$ and $$ blocks around formulas. The Gemini app renders this; pasted into an email or a Word document it arrives as symbols; ChatGPT’s transcripts carried no such markup.

Both models handled the meeting-minutes prompt well apart from the date, and we now keep this version in the practice library alongside our 50-prompt collection. The last instruction, the calendar check, is our addition after the run; we have not retested either model with it.

Convert the raw notes from a [meeting type] below into three outputs. 1) Formal minutes with project, date, attendees and items discussed. 2) A numbered list of decisions taken. 3) An action table with columns for action, owner and due date. Use only facts present in the notes. Where an owner or a date is missing, write TBC. Check every date against the calendar for [year] and flag any date whose weekday does not match what the notes say.

RAW NOTES:
[paste]

The tied clause reviews need one further qualification. Both models mapped the duty to defend before fault, the insurability gap, the missing cap and the one-way transfer of other parties’ negligence, and both framed the output as a list of questions for counsel, which is where this review should stop. Take the list to the lawyer and the hour gets shorter.

What we did not test: the app feature gap

Our runs compared the model families and never opened either chat app. The products a practice buys are chatgpt.com and gemini.google.com, and most of what differentiates them day to day (uploads, image generation, Deep Research, Workspace integration, custom assistants) sat outside our test. The table below compares those features from vendor documentation only, all read on 20 October 2026.

Criterion	Gemini app	ChatGPT app
Stated context window	32K tokens without a plan, 128K on AI Plus, 1M on AI Pro and Ultra, per Google’s limits page; Google’s own equivalence is “1,500 pages of text”	27K / 54K / 54K / 128K Instant and Varies / 256K / 256K / 400K Reasoning on Free / Go / Plus / Pro, per OpenAI’s pricing page
File uploads	Up to 10 files per prompt, 100 MB per non-video file, video to 2 GB, per Google’s upload page	512 MB per file, text files to 2M tokens, 80 files per 3 hours, Free capped at 3 uploads a day, per OpenAI’s file FAQ
Native CAD/BIM files	Google lists no formats (“most file types”); no CAD format named	Published format list has no DWG, RVT or IFC
Image generation	Nano Banana 2 featured on the plans page; Nano Banana Pro listed in the paid-plan comparison table, per Google’s plans page	Limited on Free, included from Go, unlimited on Pro, per the pricing page
Deep research	Listed on every plan card; metered against the 5-hour usage allowance	Available on every plan; allowance varies by plan with an in-product counter
Email and documents	AI Pro and Ultra include Gemini in Gmail, Docs and Sheets; AI Plus includes Gmail proofreading only, per the plans page	No native mail or document integration on the pricing page; uploads and Projects instead
Custom assistants	Gems, becoming “skills” for personal accounts from November 2026, per Google’s notice (17 November per the in-app message reported by 9to5Google)	Custom GPTs retire 11 December 2026 (Enterprise deferral to 11 February 2027), migrating to plugins; model choice and custom actions do not carry over, per OpenAI’s notice
Ads	None listed	Testing since 9 February 2026 on Free and Go only; paid plans ad-free, per OpenAI’s ads page

Feature comparison from vendor documentation read on 20 October 2026; none of these rows was tested by us.

A practice that built a code-lookup custom GPT has a December migration deadline with no guarantee its model setting survives, and a Gemini Gem built this month becomes a “skill” next month. OpenAI also retired GPT-5.5 from ChatGPT on 14 October 2026, after about a month’s notice according to press and community reports, so keep your prompts in your own files and your custom-assistant setups documented outside the app.

Plans and prices in October 2026

Gemini’s paid plans cost $4.99, $19.99 and $99.99 or $199.99; ChatGPT’s cost $8, $20 and $100 to $500. The model we tested on the Gemini side sits on the $19.99 AI Pro plan. The model we tested on the ChatGPT side powers the Pro reasoning option on plans from $100. Prices below were read on 20 October 2026, exclude tax and change at vendor discretion.

Plan	Gemini (Google AI)	ChatGPT
Free	$0, Auto routing to mostly Flash-Lite since 9 October 2026, 32K context	$0, GPT-6 Luna in chat per the pricing page (OpenAI’s model help page still names GPT-5.6 Luna as the Free and Go default), 27K Instant context, 3 uploads a day, may include ads
Entry paid	AI Plus, $4.99 a month, Flash-Lite and Flash, 128K context, 2x usage, Gmail proofreading	Go, $8 a month, still GPT-6 Luna, 54K Instant context, may include ads
Standard paid	AI Pro, $19.99 a month, adds the Pro model and Gemini 4 Argon, 1M context, 4x usage, Gemini in Gmail, Docs and Sheets	Plus, $20 a month, GPT-6 Sol in the chat tab, 54K Instant and 256K Reasoning context
Heavy use	AI Ultra, $99.99 (5x AI Pro usage) or $199.99 (20x)	Pro, $100 / $200 / $500 a month, Pro reasoning powered by GPT-6 Astra, 128K Instant and 400K Reasoning context

Sources: Google’s AI plans page, Google’s model-access page, OpenAI’s pricing page and OpenAI’s Pro-tier notes, all read 20 October 2026.

Mapping our test onto those rows: our Gemini runs used Gemini 4 Argon, which Google’s announcement (13 October 2026) brought to the Gemini app for AI Pro and AI Ultra subscribers, so a $19.99 subscriber can pick the model we tested. Our ChatGPT runs used GPT-6 Astra, which powers the chat app’s Pro reasoning option on Pro plans from $100 a month.

OpenAI’s pricing table also lists Astra on Plus, and its model help page scopes that to the ChatGPT Work and Codex surfaces; the $20 Plus chat tab runs GPT-6 Sol, which we did not test. The $19.99 Gemini plan includes the model we tested. ChatGPT at $20 would run a different model, and we cannot tell you how far Sol’s results would differ from Astra’s.

If you are deciding this month: according to Google’s email to subscribers, as reported by 9to5Google on 14 October 2026, AI Plus subscribers lose Pro-model selection on 13 November 2026, which leaves the $4.99 plan with Flash-Lite and Flash, per Google’s model-access page (read 20 October 2026).

What you can do for free - and is it enough?

Both free tiers have shrunk this month. Gemini without a plan routes most prompts to Flash-Lite since 9 October 2026, with a 32K-token context. ChatGPT Free runs GPT-6 Luna with a 27K Instant context, three uploads a day and possible ads. Neither free model is the one we tested, so our table does not predict free-tier results.

Tool	Free tier	Hard limit	Enough for
Gemini, no plan	Auto routing, mostly Flash-Lite (Flash-Lite for everything if smart selection is off), image generation, Deep Research, Gems (becoming skills), per Google’s model-access page	32K context (a short spec section, a few pages of brief); compute-based limits refreshing every 5 hours	Drafting emails and minutes, rewriting a paragraph, a quick first pass on a short document
ChatGPT Free	GPT-6 Luna, unlimited text chat subject to abuse guardrails, per OpenAI’s pricing page	27K Instant context (about 12 pages in OpenAI’s own equivalence); 3 uploads a day; ads possible since 9 February 2026	Text-only drafting, plain-language client explanations, one uploaded document a day

Read from both vendors’ pages on 20 October 2026.

If you want to run your own ChatGPT vs Gemini for architects comparison on the models we tested, the free tiers will not do it. Gemini’s free Auto routing never reaches Argon, and ChatGPT Free never reaches Astra.

What a free trial of both can settle is the working-style question. Run the minutes prompt and the delay email through both for two weeks; if you read Gemini’s longer answers to the end, AI Pro at $19.99 is the natural step, and if you keep trimming them, ChatGPT’s tighter output is the signal. Turn off training in both accounts before the first client email goes in.

Where both tools will hurt you

The ChatGPT vs Gemini for architects choice comes with risks common to both tools. Both models answer jurisdiction-sensitive questions with full confidence, and both train on consumer-plan conversations by default. Both have also retired or renamed features on short notice this quarter. A correct code answer today, as in our task 4, says nothing about tomorrow’s.

The RIBA AI Report 2026 (22 July 2026, 1,100+ respondents) found 74% of UK practices using AI on at least some projects, up from 41% in 2024 and 59% in 2025. In the US, an AIA study (11 March 2025, 500+ respondents) found 6% of architects regularly using AI and 90% concerned about inaccuracy; our data page on how architects use AI tracks both series.

Five risks to manage on day one, whichever tool you pick.

1. Plausible wrong answers. In a three-model floor-plan reading test, ArchitectsWhoCode (21 April 2026) found ChatGPT most consistent on door counts and Gemini least stable, adding room interpretations that were not in the drawing. Naveen Maria Fleming’s conclusion: “hallucination does not always look absurd. Sometimes it looks plausible. And that is exactly why it is risky.”
2. Training defaults. OpenAI’s consumer plans train on your content unless you opt out under Settings, Data controls, “Improve the model for everyone”, per OpenAI’s data page (read 20 October 2026). Gemini personal accounts train by default through “Keep Activity”, and human-reviewed chats are retained up to 3 years even after deletion, per Google’s Privacy Hub (updated 19 October 2026).
3. Context marketing versus recall. In a January 2026 r/GeminiAI thread, u/OptimismNeeded wrote: “Gemini forgets early parts of the chat waaaaay before hitting a million tokens or even 300k.” Plant a known fact mid-document and test recall before trusting a long upload.
4. Judgment is still yours. On architects’ GPT-6 Astra tests with BIM tools, Martyn Day wrote in AEC Magazine (11 October 2026): “These demonstrations establish useful capabilities, but do not yet establish dependable judgement about circulation, clearances or construction.”
5. Accountability does not move. The AIA’s responsible-AI guidance (updated 22 October 2025) states: “Architects remain accountable for all work products, decisions, and representations made to clients, agencies, and the public - including work produced with AI assistance.”

Add product churn to that list: custom GPTs, Gems and AI Plus Pro access all change between 13 November and 11 December. A workflow built on a vendor’s convenience feature has a shelf life measured in weeks this autumn.

FAQ

Which is better for architects, ChatGPT or Gemini?

Neither won our set. In our ChatGPT vs Gemini for architects test (20 October 2026), Gemini 4 Argon edged three tasks on depth (brief audit, RFP review, outline spec), GPT-6 Astra edged two on precision (consistency check, meeting minutes with a calendar error), and seven tied. Pick Gemini if you read long reviews and live in Google Workspace; pick ChatGPT for concise output that follows instructions closely (it alone linked sources, on two tasks).

Can ChatGPT or Gemini check building code compliance?

No. Both models returned correct 2021 IBC values and section numbers in our test. That result carries no guarantee for a different question, edition or jurisdiction, and neither knows your local amendments. Use them to locate sections and draft questions, then verify every citation in the currently adopted edition yourself. The licensed professional remains accountable for compliance, as the AIA’s guidance states.

Which Gemini model does a free user actually get?

Since 9 October 2026, users without a Google AI plan are on Auto routing, which sends most prompts to Flash-Lite, with a 32K-token context, per Google’s model-access page. Gemini 4 Argon, the model we tested, requires AI Pro ($19.99) or AI Ultra, and AI Plus ($4.99) loses the Pro model on 13 November 2026, so free-tier results will differ from ours.

Does Google AI Pro’s 1M-token window help with drawing sets and spec PDFs?

On paper it holds about 1,500 pages, in Google’s own equivalence, against ChatGPT Plus’s 256K Reasoning context. We did not test long uploads, and practitioners report recall degrading well before the limit. Neither app reads DWG, RVT or IFC natively. Treat the number as an upload ceiling, test recall with a planted fact, and keep critical checks to documents you can read yourself.

Should a small practice pay for both?

Only if the free tiers keep interrupting real work and your week splits between long-document review and short-form drafting. AI Pro at $19.99 contains the Gemini model we tested; ChatGPT Plus at $20 runs GPT-6 Sol, which we did not test, and the Astra tier we ran starts at $100. For most small practices, one paid plan matched to the dominant task type is enough.

Sources

• OpenAI - ChatGPT pricing (read 20 October 2026)
• OpenAI - About ChatGPT Pro tiers (read 20 October 2026)
• OpenAI - GPT-6 and other models in ChatGPT (read 20 October 2026)
• OpenAI - Custom GPTs retirement notice (read 20 October 2026)
• OpenAI - Data controls and training (read 20 October 2026)
• Google - Google AI plans (read 20 October 2026)
• Google - Changes to Gemini model access and limits (read 20 October 2026)
• Google - Gemini Apps limits and feature availability (read 20 October 2026)
• Google - Gemini Apps Privacy Hub (updated 19 October 2026)
• Google - “Gemini 4 Argon is now available in the Gemini app”, The Keyword (13 October 2026)
• 9to5Google - Gemini AI Plus subscribers will lose access to Pro models next month (14 October 2026)
• RIBA - AI Report 2026 (22 July 2026)
• AIA - New research explores perceptions and opportunities of AI (11 March 2025)
• AIA - Guidance for the Responsible Use of AI by Architecture and Design Firms (updated 22 October 2025)
• AEC Magazine - GPT-6 Astra and the future of BIM modelling (11 October 2026)
• ArchitectsWhoCode - Can AI really read a floor plan? (21 April 2026)
• ICC - 2021 IBC Section 1020.3 (read 18 October 2026)

Start with the two-week free-tier trial above, training turned off in both accounts. We re-run these twelve tasks whenever a vendor ships a new model, and the next transcript set goes to the newsletter first, before the article updates. Subscribe.