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No available evidence establishes that one AI assistant lies less than all others when writing marketing copy. The evaluations available test different models, versions, prompts, tools and tasks—not a current, independent, head-to-head marketing-copy test. To choose reliably, compare the exact assistants you plan to use on the same source-backed assignments, then verify every factual claim before publishing.
Why there is no universal winner
“Lies less” is not a single benchmark result. A model may avoid unsupported answers by refusing more often, while another may answer more readily but make more factual errors. Both behaviors matter when you need usable copy that stays within approved product facts.
OpenAI’s pilot cross-lab evaluation compared Claude Opus 4 and Sonnet 4 with GPT-4o, GPT-4.1, o3 and o4-mini in a difficult hallucination evaluation without browsing. It found a tradeoff: the Claude models refused more often, while o3 and o4-mini refused less often but had higher hallucination rates. The post describes model-specific strengths and weaknesses; it is a vendor-published pilot, not a universal ranking or a marketing-copy test. OpenAI’s evaluation and discussion
OpenAI’s GPT-5 system card reports lower hallucination rates for GPT-5 variants than for specified earlier OpenAI models on its own evaluations. Those results depend on the prompts and whether browsing is enabled. They are a within-OpenAI comparison, not evidence that GPT-5 outperforms current competitors or writes more accurate marketing copy. Read the GPT-5 system card
#1 Best Overall
What factuality benchmarks can—and cannot—tell you
Benchmarks measure a defined task, dataset and scoring method. Their results can help explain model behavior, but they do not directly predict the truthfulness of every product description, ad or email.
| Evaluation | What it tests | How to interpret it for marketing copy |
|---|---|---|
| SimpleQA | Short, fact-seeking questions designed to be comparatively easy to grade. Its authors note that factuality is difficult to measure. OpenAI reported less than 40% for GPT-4o on this benchmark when introducing it in 2024. | Useful context about short-answer factuality in the release-era models tested; it is not a marketing-copy benchmark or a current assistant ranking. SimpleQA introduction |
| FACTS Grounding | Long-form answers grounded in supplied documents, using 1,719 examples and three model families as judges. It checks both whether the request is answered and whether the response stays grounded in the provided text. | Relevant to source-constrained writing, but still one defined benchmark rather than a test of all marketing tasks. Google DeepMind’s benchmark description |
| FACTS Benchmark Suite | Google DeepMind reports a 68.8% overall score for Gemini 3 Pro and says all evaluated models scored below 70% overall accuracy on the suite. | These are scores for the suite’s evaluated models and tasks, not the percentage of truthful marketing copy in ordinary use. FACTS Benchmark Suite report |
| TruthfulQA | Introduced in 2021 with 817 questions across 38 categories, using model generations tested at that time. | Historical research context, not a current comparison of writing assistants. TruthfulQA paper |
Differences in model versions, prompts, tool access and scoring prevent these results from being combined into one dependable ranking. Even the most relevant source-grounding benchmark does not show which assistant will make fewer unsupported claims in your specific campaign.
How to compare assistants for your own work
Run a small, controlled comparison using real copy tasks and approved product information. Keep the factual scoring separate from style preferences so polished prose does not disguise unsupported claims.
- Choose the exact candidates. Record each assistant’s model or version, the date, and whether browsing or other tools are enabled. These settings can change the result.
- Prepare one approved product brief and supporting material. Use the same current product brief and supporting material for every candidate. Include constraints, such as features that are unavailable, claims that are prohibited, and details that are unknown.
- Give every assistant the same prompt. Specify the audience, format, length and required benefits. Ask the assistant to use only the supplied sources, flag missing evidence, and attach source support to each factual claim.
- Blind the outputs before scoring. Remove model names where practical so reviewers assess the copy rather than their expectations about a provider.
- Check claims against original sources. Score each factual claim, number, quotation and citation for accuracy and support. Count unsupported claims separately from incorrect claims, and note whether the assistant appropriately abstained when evidence was missing.
- Score usefulness separately. Rate coverage of required benefits and constraints, clarity, and editability after factual accuracy has been assessed.
- Report the limits of the comparison. Keep the source set, tool access, number and types of claims checked, and abstentions with the results. Treat a small in-house sample as guidance for your workflow, not a universal accuracy percentage.
What to verify before publishing AI-written copy
OpenAI’s help guidance warns that ChatGPT can produce incorrect or misleading output and may sound confident while wrong. It recommends treating responses as drafts and checking important information. OpenAI Help Center: Does ChatGPT tell the truth?
- Product attributes: Confirm features, compatibility, availability, ingredients, terms and limitations against current approved materials.
- Numbers and performance claims: Check figures, percentages, comparisons and stated outcomes against their original evidence, including the conditions and date that qualify them.
- Quotes and citations: Open the cited source and confirm the wording, attribution and context. A citation that looks plausible is not proof that it supports the claim.
- Missing or ambiguous facts: Remove or qualify a claim if the source material does not settle it. Do not let fluent phrasing turn uncertainty into a definite promise.
As OpenAI’s guidance puts it, “Use ChatGPT as a first draft, not a final source.” The same practical caution applies when reviewing AI-generated marketing copy: confidence and polish are not evidence of accuracy.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing a workflow, not just a model
For source-constrained marketing, the safer choice is the assistant that performs best on your own controlled tasks while clearly identifying gaps and supporting its factual claims. Supplied sources make claims easier to audit, but they do not eliminate the need to check whether the output accurately reflects those sources. Re-run the comparison when the model, tools, brief or approved product information changes.
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