Which AI Model Should Fact-Check Your Articles? We Tested 9

AI writes fluent, confident prose — and gets facts wrong. In a competitor comparison article, one wrong acquisition, funding figure, or founding year is enough to lose a reader's trust or trigger a legal complaint. So we built a flow in Evaligo that does not just generate articles — it grounds them: it verifies every claim against the live web and repairs the wrong ones in place, without rewriting the whole piece.

Then we asked the obvious question: which model should do the repairing? We tested nine. The results were not what we expected — the best fact-fixer is also the worst writer, and paying more buys you less accuracy. Here is everything, scoreboard included.

How it works

The flow takes a draft article and runs it through three AI agents before writing anything back:

• Extract & self-check — pull every factual claim out of the draft and flag internal contradictions (no web access needed).
• Web grounding — verify each claim with a live web search and attach source URLs.
• Repair — rewrite only the wrong facts to the verified value (or hedge the unsupported ones), and keep every section intact.

Input: an article that may contain wrong facts.  Output: the same article, corrected, plus a fix-ledger of exactly what changed, why, and the source for each fix.

The scoreboard: 9 models, 2 scores each

Every model repaired the same set of test articles seeded with planted errors. We scored two things independently: Facts fixed (did it correct the errors, 0–1) and Reads well (did the repair stay engaging, 0–1). Cost is per repaired article.

*“Reads well” runs high for models that barely change the text — the do-nothing baseline scores 0.88 while fixing nothing, so it only means something next to Facts-fixed.

The surprise: fixing facts and reading well pull apart

We expected the best models to win both scores. Instead the two dimensions are anti-correlated. gpt-5-mini fixes the most facts but writes the driest prose (0.49). The Gemini models read beautifully (0.91–0.92) but miss facts (~0.48–0.50). And the do-nothing baseline “reads” at 0.88 — precisely because it barely changes the text.

The one cause behind every readability loss: over-hedging. Correct repairs add words (“reportedly,” “according to some sources”) that blunt the original's punch. Only Opus 4.8 stays strong on both — it corrects and keeps the voice.

A real repair (unedited)

Here is an actual fix from a run on a Forethought vs Intercom vs ASAPP draft — the flow caught a claim that conflated a signed deal with a closed one:

How we scored it — honestly

Two independent LLM judges. A readability judge compares the repaired article to the original and rates whether it stayed engaging (facts ignored). A separate fact judge rates factual correctness and internal consistency. One honest caveat: our fact judge is reference-free — it reads the repaired article and asks “is this correct and consistent,” rather than diffing against a fixed answer key. It is a strong signal that tracks repair quality, and treating it beside readability is what surfaced the trade-off above.

What we shipped

We put Opus 4.8 on the flow — the only model that fixes facts and keeps the writing readable — and now run every comparison article through it before it publishes. Ground-checking is quickly becoming table stakes: in a web full of confident, wrong AI text, the articles that verify their claims are the ones that earn trust.

You can build the same flow in Evaligo: a visual pipeline that extracts claims, verifies them on the web, and repairs the wrong ones — with a fix-ledger for every change.