Claude vs ChatGPT: which one to trust with real work

If you mostly read, analyse and write (reports, research, spreadsheets, long PDFs), Claude is the stronger choice right now. If you want one app that also makes images, talks to you and runs a coding agent, ChatGPT covers more ground.

That's our view, and it comes with two caveats. Both companies shipped a new generation of models in September 2026, so rankings that were true in the summer aren't any more. And on the question that matters most, whether the answer is right, the gap between them is much smaller than the gap between either of them and "reliable".

Prices from claude.com/pricing and learn.chatgpt.com, checked 22 October 2026.

There is no single accuracy score, and anyone who gives you one is simplifying. Three independent measures are worth knowing, and they don't agree.

Artificial Analysis Intelligence Index (a blend of ten evaluations): Claude Opus 5.5 is first at 58; OpenAI's GPT-6 Astra scores 53. AA-Omniscience, which rewards correct answers and penalises confident wrong ones on 6,000 knowledge questions: Opus 5.5 leads at 46, with GPT-6 Astra close behind at 44 (Artificial Analysis, checked 22 October 2026).

Vectara's hallucination leaderboard measures something different: how often a model adds facts that aren't in a document it's asked to summarise. Neither company's newest models are on it yet. Among the ones that are, OpenAI's GPT-5.5 hallucinated in 9.3% of summaries and Claude Opus 4.7 in 12.0% (Vectara, updated 16 October 2026). On that measure, the older ChatGPT model was slightly more faithful.

So what do you do with that? Two things. Treat a 2–5 point gap on any index as noise for your purposes: your questions aren't the benchmark's questions. And notice the size of the numbers. Even the best models get a meaningful share of answers wrong, and they sound exactly as confident when they do. More detail in AI hallucination rates and the most accurate AI.

That confidence is the problem worth solving. Count connects to Claude and labels each claim in an answer as a citation, an assumption, model knowledge or a hallucination, so a confident wrong sentence stops looking like the rest.

At $20 a month the two plans look similar on paper and differ in practice.

Claude Pro adds Research (Claude's deep research), Claude Code, unlimited Projects, Claude in Chrome and Microsoft 365, scheduled tasks and access to Opus as well as Sonnet and Haiku. Claude's newest and most capable model, Fable 5.1, is only available on Pro through paid usage credits; Max plans include it. Usage resets on a rolling five-hour window, with weekly limits on top and "no fixed message count" (claude.com/pricing).

ChatGPT Plus adds OpenAI's Codex coding agent, image generation, voice, ChatGPT credits to extend usage, and GPT-6.1 Sol and GPT-6 Luna. OpenAI publishes five-hour estimates rather than hard caps: 15–160 messages with GPT-6.1 Sol and 350–3,000 with Luna on Plus, for its Work and Codex surfaces (learn.chatgpt.com, checked 22 October 2026).

One thing to know before you buy ChatGPT: OpenAI's own pages currently disagree about which GPT-6 models reach the ordinary Chat window and which are limited to its Work and Codex surfaces. Check the model picker on your plan before you assume you're getting the flagship.

Pay more if you hit limits daily, not because you might. Claude Max costs $100 or $200 and gives five or twenty times Pro's per-session allowance, plus monthly API credits. ChatGPT Pro comes at $100, $200 and $500, and OpenAI says Pro plans currently have no five-hour limit. For most people doing analysis, a $20 plan plus good habits beats a $200 plan used carelessly. See Claude Pro vs ChatGPT Plus for the full plan breakdown.

Both can run code on a file you upload, which is what you want for any calculation: a model doing sums in its head is the riskiest thing it does. The difference is in what happens around the code.

Claude reads very long inputs well. All current Claude models have context windows of up to 1M tokens, chat uploads take up to 20 files, and PDFs can run to 1,000 pages (Claude help centre). Anthropic's own prompting guidance is built around long documents: put them first, ask the question last, quote before answering.

ChatGPT has offered code-based data analysis for longer and wraps more tools around it, including image generation and ChatGPT Work for longer, multi-step tasks. Its context window depends on which plan and model you're on, and OpenAI's current tables couldn't be checked for this page.

An illustrative test you can run on either: upload a month of campaign data and ask for ROAS by channel. Then ask which revenue column it used, whether it divided totals or averaged daily ratios, and whether refunds are in or out. Both will produce a confident number. What you're testing is whether it tells you the choices it made without being asked. Our guide to Claude vs ChatGPT for data analysis goes further, and the ROAS page shows why those choices change the answer.

Both Claude Research and ChatGPT's deep research search the web, read dozens of pages and hand back a report with citations. The citations are the part people trust and the part that deserves the most suspicion.

When Columbia's Tow Center tested eight AI search tools on identifying the source of news excerpts, ChatGPT Search misidentified 134 of 200 and never once declined to answer (Tow Center, March 2025). That study predates the current models, and Claude wasn't among the tools tested, so don't read it as a verdict on either. Read it as a reminder that a link next to a sentence doesn't mean the page says what the sentence says.

For research we'd lean Claude, mainly for how it handles long source material and because Anthropic's guidance on quoting before answering is so explicit. ChatGPT's deep research is very capable, and OpenAI was candid at launch that it can "hallucinate facts or make incorrect inferences" and "may struggle with distinguishing authoritative information from rumors" (OpenAI deep research system card, February 2025). More in Claude vs ChatGPT for research.

The benchmark that matters is your own work. Take one real task you know the answer to, a report you've already written or a number you've already checked, and give both models the same prompt and the same files.

A two-minute check

1. Count the claims. Split each answer into its individual statements. How many are there?
2. Trace three. For three of them, find where it came from: a passage in your file, a web page, a calculation. Open it.
3. Ask what it assumed. "Which parts of your answer are assumptions rather than things you found in the data?" A good answer lists them; a weak one restates the conclusion.
4. Ask for the missing piece. "What would you need to be more confident?" Models that say "nothing" are the ones to watch.
5. Run it twice. If the two runs disagree on a fact, neither run has earned your trust on that fact.

Whichever wins, keep checking. It's also the reason Count exists: connect it to Claude or ChatGPT and it splits the answer into claims and shows the reasoning chain behind each one, so you can see which parts rest on a source, which on the model's memory, and which on nothing at all. If you want to keep using both models, you don't have to choose.

We've written about why the most fluent answers get checked least in the AI trust gap.

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