Claude vs GPT-4o for long-form: the numbers that actually decide it

Ask which AI model writes better and you get an argument. Ask which model can produce a 25,000-character article in one pass, stay inside a supplied set of sources, and hold its register to the last paragraph, and you get a short list of measurable answers.

Smart-Copy.ai runs on Anthropic's models. This post explains the reasoning, using the comparison that settled it at the time: Claude Sonnet 4.5 against GPT-4o. Most of what decided it never appears in the headline benchmarks.

Two models from different eras

An honest caveat first: these are not contemporaries. GPT-4o shipped on 13 May 2024; Claude Sonnet 4.5 on 29 September 2025. Sixteen months is a long time in this field. The point of the comparison is not to crown a winner but to show which specifications change the output when the output is long.

Specification	GPT-4o	Claude Sonnet 4.5
Released	13 May 2024	29 September 2025
Context window (input)	128,000 tokens	200,000 tokens
Maximum output, single response	16,384 tokens	64,000 tokens
Knowledge cutoff	October 2023	January 2025

One housekeeping note so this post does not mislead anyone reading it next year: Smart-Copy.ai currently runs on Claude Sonnet 5, with a one-million-token context window and up to 128,000 output tokens. Version numbers turn over every few months. The selection criteria do not, and those are the subject here.

Long-form is decided by the output cap, not the context window

Model marketing talks almost exclusively about context windows, because the number is large and looks impressive. But the context window is the input side: how much material the model reads before it starts writing. What governs long output is a second, far less advertised limit — the maximum tokens in a single response.

The gap between 16,384 and 64,000 tokens is not a matter of degree. It is the difference between "I will write this article" and "I will write part of it and you will ask me to continue." Every continuation costs coherence:

• Repeated arguments. The model cannot clearly distinguish what it already argued from source material it was given to summarise, so the same thesis lands twice under different headings.
• Register drift. Later sections tend to be vaguer than earlier ones, because the specifics from the brief fall further back in context with every continuation.
• Structural drift. The outline gets reconstructed from memory instead of held as a fixed reference, and heading hierarchy degrades — which matters more than most writers assume, as we covered in our piece on SEO article structure.

The cap bites harder outside English. Tokenizers are trained predominantly on English text, so inflected languages fragment into more tokens per word. A German compound, a Polish declined noun or a Finnish case form all cost more tokens than their English equivalent — meaning the same output ceiling buys measurably fewer characters. For a tool that ships in more than one language, the output limit is a multilingual constraint, not just a length one.

What the benchmarks show, and what they do not

Sonnet 4.5 posted strong numbers at launch: 77.2% on SWE-bench Verified (averaged over 10 trials with no test-time compute; 82.0% with parallel test-time compute), 61.4% on OSWorld, 86.0% on MMLU-Pro and 83.4% on GPQA Diamond.

Now look at what those measure. SWE-bench is resolving real issues in code repositories. OSWorld is operating a computer. MMLU-Pro and GPQA are multiple-choice tests of expert knowledge. None of them measures whether a model writes a good 25,000-character article. They correlate loosely with general capability; treating them as a proxy for prose quality is a category error.

The same caution applies to the widely repeated claim that Claude hallucinates less. On the earlier edition of Vectara's hallucination leaderboard (HHEM) — which measures how often a summary asserts something the source document does not support — GPT-4o scored better than Claude 3.5 Sonnet, 1.5% against 4.6%. FaithBench ranked them in the same order. Anyone selling you "model X hallucinates less" as a flat fact owes you two follow-up questions: measured how, and in what year.

With research in the loop, hallucination is a different failure

Here is the distinction that mattered to us. Those benchmarks measure fidelity to one supplied document. Smart-Copy.ai works differently: it gathers material first (searching, scraping, analysing), writes afterwards, and displays the sources it used alongside the finished text. You can add your own URLs to a single job, and an academic research mode targets scholarly publications.

Under that architecture, the risk is not that the model invents a fact from nothing. It is that the model drifts past the material — adding a sentence that reads like it came from a source but traces back to none of them. That is a test of holding to supplied context across tens of thousands of characters, which is what we actually evaluated. The same property governs generating reports from your own files, where any departure from the data is immediately visible to the person who supplied it.

A large context window does not give you good long-form

There is a further reason we do not simply push everything into one prompt, even when the window allows it. "Lost in the Middle" (Liu et al., TACL 2024) showed that models use information best when it sits at the beginning or the end of the context, with accuracy dropping by more than 30% when the relevant passage moves to the middle. The curve is U-shaped, and it holds for summarisation and long-document question answering alike.

So long texts are built in stages: research, then an outline you can approve before writing starts, then sections written against the source material assigned to them. At every step the model sees a context in which the important material is near an edge rather than buried in the middle. The keyword and internal-linking side of that pipeline is described in our post on how SEO optimisation works in Smart-Copy.ai.

How to run this test yourself

If you are choosing an engine for your own content operation, ignore the general leaderboards and test on your own material:

1. Fix one brief and one set of sources. Vary those and you are comparing noise.
2. Order the longest piece you genuinely need, and count how many characters arrived in a single pass, with no continuation prompt.
3. Count unsupported claims. Not "does it sound credible" — can you point at the sentence in the source that backs it?
4. Mark repeated arguments. Stitched-together drafts usually contain several.
5. Count how many sentences you rewrote for language rather than for substance.

Points 2 and 5 will settle the question faster than any benchmark. Whatever the result, budget for an editing pass — our editing checklist for AI-generated text covers what to look for.

The choice is a procedure, not a model

At launch, Sonnet 4.5 was clearly stronger than GPT-4o on the three things we care about: how much it writes in one pass, how tightly it stays inside supplied sources, and how much rewriting the prose needs afterwards. A year later, production runs on Sonnet 5, and GPT-4o has successors of its own.

Which is why what we actually committed to was a procedure: a fixed regression set of briefs in English and Polish, the same source material every time, the same scoring criteria. Any candidate engine runs that set before it reaches production. A model that improves on SWE-bench while degrading punctuation in a 30,000-character article is, for our purposes, a regression — whatever the leaderboard says.