Grok vs ChatGPT: Which One Is Easier for an AI Detector to Catch?

As generative AI takes the world by storm, trying to find a way to use it without excluding human involvement in the content has become a real legal, ethical, and professional issue. One solution is governance, made possible by AI content detector technology that also helps set clear boundaries for users. The question is, is there an AI tool that sounds or reads more like a machine, and are there tools that are easier for an AI detector to catch?

After searching the web with a fine-tooth comb, we couldn’t find a definitive answer and decided to take on the challenge. To that end, we analyzed sample outputs from two leading generative AIs, Grok vs ChatGPT. Our goal was simple: to determine if one model’s training data makes it easier to detect than the other. 

The results might surprise you, but before we present them, we want to take a step back and understand the fingerprints these tools leave on every piece of content they generate.

What Makes AI Text Detectable in the First Place

AI is not an independent thinker. To create its textual outputs, it uses statistical probability to predict the next word in a sentence. This process generates two types of signals known as perplexity and burstiness. 

• Perplexity measures how well an AI model is able to predict the next word in a sentence. Low perplexity means highly predictable while high perplexity is very unpredictable. Think of it like a friend who always orders the same three dishes at restaurants and never orders anything outside their typical taste. Their behavior has low perplexity because it’s highly predictable. AI models are like that friend and tend to always choose the most likely next word from the same subset. This makes it easy for an AI content detector to spot, mainly due to the limited word choice and outputs these tools use.
• Burstiness measures how much sentence length and structure change throughout the text. Typically, humans are naturally bursty and move between short and long sentences. We could write a punchy sentence. Then, we might follow it with a longer, more complex idea, writing a few clauses before ending with a conclusion. AI does the opposite and tends to stick with a narrow subset of sentence length and structure that’s easy to detect. So, lower burstiness is likely AI-generated. 

Another term that’s worth mentioning in this context is structural uniformity. According to research, AI tools depend on “fillers, repetitive stereotypical expressions, and structured but reduced linguistic subtlety”, referring to words like “in addition”, “furthermore” and “it is important to note”. 

What’s interesting is that while both Grok and ChatGPT use these transition words, each uses them in different patterns, creating a unique digital signature of sorts. 

How Grok and ChatGPT Actually Write and the Differences Detection Tools Catch

The Grok vs ChatGPT comparison cannot be limited to features, because the two models differ in many other ways. 

To begin with, they have very different styles. xAI’s Grok was created as a witty friend who grew up on the internet and is meant to be funny, using inside jokes in the form of memes to talk to you with a bit of an edge. OpenAI’s ChatGPT is all about being a helpful, neutral professional who keeps things clear and sticks to facts. This basic difference is also mirrored in the models’ writing fingerprints. 

Grok’s Writing Fingerprint

As an internet native who was partly trained on the social network X’s (formerly Twitter) data, Grok’s style is more conversational, blunt, and sometimes sarcastic. Its more informal responses and framing result from the specific system instruction to be maximally truthful and avoid what is considered to be woke answers. 

Since Grok can access live data from X and the web, it has available, up-to-date knowledge about public sentiment, trending topics and news, which makes its outputs more current. 

But, if you try to ask Grok to write something more formal, like an academic essay, its edgy personality can’t save it from falling into the same traps as other models, and you will still find low perplexity patterns that detection tools are built to find. For the human eye, the Grok writing style might sound more human. Yet, Grok still uses the statistical distribution detectors can identify. 

ChatGPT’s Writing Fingerprint

ChatGPT has been trained to be clear and neutral, and produce outputs that are polished, structured, and balanced. The aim was to give it a professional reliability suitable for corporations and academic usage. Funnily enough, this is its biggest telltale. 

ChatGPT is notorious for playing it safe, meaning it leans heavily toward hedging language. Instead of saying “it’s going to rain today”, ChatGPT will say “it might rain today”, instead of  “the sky is blue” it will say “the sky is typically perceived as blue”, and so on. To this list you can also add fillers that avoid direct statements, like “it’s worth noting,” “on the other hand,” and “that said.”

Its structure is also extremely templated and it follows a clear and consistent format: introduction → point → elaboration → transition. 

Research on the difference between how humans and AI write summaries found that human writing is “far richer in terms of emotions, authenticity, and personal references”, while texts generated by ChatGPT were much more uniform “with formulaic expressions.”

We Ran Both Through an AI Detector and This is What Happened

We decided to put it to the test and come back with a solid answer.

The Methodology

We gave Grok and ChatGPT the exact same prompt: write a 500-word essay on the impact of social media on academic integrity. We did not ask them to sound human or make an effort to avoid detection. We wanted to get the raw, default output of each.

Then, we analyzed both outputs with Copyleaks AI content detector. 

The Results

Model	AI probability score	Flagged AI phrases
ChatGPT	100%	58
Grok	100%	47

The Analysis

Although both models were detected with absolute certainty, the specific patterns the detector flagged were different.

	ChatGPT	Grok
Detection trigger	Structural uniformity	Perplexity patterns
Linguistic fingerprint	Rigid and predictable from start to finish	Repetitions hidden behind a witty, supposedly human tone
Main indicators	Riddled with fillers and transition clusters like Furthermore	Word choices with a high probability of being used for an edgy tone
Non-human phrasing	Phrase frequency 2,200 times higher than human writing	Low burstiness, despite punchier sentence lengths
Visual indicators	Detected sections are most heavily concentrated in the introduction and summary	Detected sections are scattered throughout all conversational paragraphs

The most important conclusion from this test is that changing the style and personality of the output won’t change the digital fingerprints. An AI can sound like a witty friend or a neutral professional, but the mathematical probability of its word and phrasing choices remains detectable. Although Grok did manage to produce higher burstiness than ChatGPT, the Copyleaks detector wasn’t fooled and still found a high concentration of AI phrasing – 47 instances, to be precise. 

And this raises another question we know many are asking: would paraphrasing work?

Well, after following up by running these essays through both AI models, prompting them to vary the word choice, the Copyleaks detector still flagged the content. The reason: detection is not about vocabulary; it’s about AI logic. So, although sentence structures change, it is still the same predictable, machine-generated reasoning. 

What All This Means for Those Trying to Detect AI Content

Bottom line: the model is insignificant. It’s all about the mathematical patterns of generative AI.

Here’s what it means for different entities that use detection: 

• Educators 

These findings show that Grok is not a successful option for students. Both models are detectable by quality detection tools. The challenge for academic integrity is not the AI model, but the risk of AI-generated content that was manually edited in an attempt to avoid those mathematical patterns. 

•  Enterprise teams

Any AI governance policy must cover all AI models – whether it’s a more informal model like Grok or a professional assistant like ChatGPT. The organization will face the same risks if AI-generated content is not disclosed. 

• Content managers

Whether it’s Grok, ChatGPT, or another model – what matters is the sophistication of the prompt. Any generic output will result in a 100% detection score. When the prompt aims to mimic a specific style, detection scores are likely to be lower. 

Detectors like Copyleaks are trained across a massive range of models to identify the universal signals of machine-generated content. Since they are not designed to specifically find ChatGPT or Grok texts, changing models has no impact on detection results.

Quick Comparison: Grok vs ChatGPT

Grok and ChatGPT may have similar signatures, but their roles are different.

Here’s a quick comparison of the two to sum it all up:

	ChatGPT	Grok
Writing style	Professional and neutral	Witty and conversational
Real-time data	Web browsing (Bing)	Live access to X (Twitter)
Policy	Strict safety rules	Anti-woke approach with minimal guardrails
Best for	Academic and professional content	Trends, news, and informal interactions