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October 2026 results: WriteHuman holds #1, StealthGPT jumps to #2. Read the analysis →
HumanizerBench

Head to head · October 2026 cycle

Super Humanizer vs Undetectable.ai

Undetectable.ai finished 3.6 points ahead of Super Humanizer in the October 2026 cycle, 71.18 to 67.58, ranking #3 against #7 in a field of 14. Undetectable.ai scored higher on 5 of 6 detectors and 4 of 6 writing categories. Super Humanizer came out ahead on penalties (−1.0 vs −5.0), blog post prompts (70.3 vs 58.1) and news article prompts (70.7 vs 61.4).

67.58 /100
overall score
#7 /14
rank ▲ 4
+3.60
Undetectable.ai leads →
Detectors
1 – 5
Categories
2 – 4
Components
1 – 4
71.18 /100
overall score
#3 /14
rank ▲ 3

30 prompts · Last tested · Methodology v1.3.0

Score components

What the overall score is made of: bypass rate weighs 42%, meaning preservation 32%, readability 16%, consistency across categories 10%. Output-quality penalties come off the total. How scoring works →

Bypass rate Undetectable.ai by 12.8
61.8 74.6
Meaning preservation CIs overlap Undetectable.ai by 2.6
76.6 79.2
Readability Undetectable.ai by 7.3
56.6 63.9
Consistency Undetectable.ai by 2.5
90.4 93.0
Penalties fewer is better Super Humanizer by 4.0
−1.0 −5.0

CIs overlap marks a gap that sits inside both tools' published 95% confidence intervals for that component; it may not survive another cycle.

Detector by detector

Share of each tool's outputs that a detector classified as human-written. Undetectable.ai took 5 of 6.

GPTZero Undetectable.ai by 38.4
46.6 85.0
Originality.ai Super Humanizer by 6.3
95.0 88.7
Copyleaks Undetectable.ai by 4.6
83.3 87.9
Winston AI Undetectable.ai by 20.5
77.9 98.5
ZeroGPT Undetectable.ai by 15.8
68.0 83.8
Pangram Undetectable.ai by 3.5
0.0 3.5

Writing categories

Category score per writing context: bypass credit only for real rewrites, blended with the category's own meaning preservation and readability. Small categories swing; the prompt count is shown on each row. How category scores work →

Academic Essay Application Essay Blog Post Marketing Copy Discussion Board News Article Academic Essay: Super Humanizer 67.4 · Undetectable.ai 69.0 Application Essay: Super Humanizer 64.4 · Undetectable.ai 76.4 Blog Post: Super Humanizer 70.3 · Undetectable.ai 58.1 Marketing Copy: Super Humanizer 55.4 · Undetectable.ai 71.5 Discussion Board: Super Humanizer 66.0 · Undetectable.ai 69.4 News Article: Super Humanizer 70.7 · Undetectable.ai 61.4
Super Humanizer Undetectable.ai Outer ring = 100
Academic Essay 6 prompts Undetectable.ai by 1.6
67.4 69.0
Application Essay 6 prompts Undetectable.ai by 12.0
64.4 76.4
Blog Post 6 prompts Super Humanizer by 12.2
70.3 58.1
Marketing Copy 6 prompts Undetectable.ai by 16.1
55.4 71.5
Discussion Board 3 prompts Undetectable.ai by 3.4
66.0 69.4
News Article 3 prompts Super Humanizer by 9.3
70.7 61.4

Head-to-head history

Overall score in every cycle both tools were tested. Undetectable.ai has finished ahead in 5 of 5 shared cycles.

50 60 70 80 90 June 2026 July 2026 August 2026 September 2026 October 2026 June 2026: Undetectable.ai 80.09 (#5) June 2026: Super Humanizer 64.75 (#11) July 2026: Undetectable.ai 72.17 (#2) July 2026: Super Humanizer 54.13 (#11) August 2026: Undetectable.ai 61.49 (#8) August 2026: Super Humanizer 55.86 (#10) September 2026: Undetectable.ai 71.75 (#6) September 2026: Super Humanizer 60.97 (#11) October 2026: Undetectable.ai 71.18 (#3) October 2026: Super Humanizer 67.58 (#7) 67.6 71.2
Super Humanizer Undetectable.ai
Cycle Super Humanizer Undetectable.ai Margin
October 2026 67.58 #7 71.18 #3 → 3.6
September 2026 60.97 #11 71.75 #6 → 10.8
August 2026 55.86 #10 61.49 #8 → 5.6
July 2026 54.13 #11 72.17 #2 → 18.0
June 2026 64.75 #11 80.09 #5 → 15.3

Where each one wins

Every measure above that one tool won outright, largest margin first within each group.

Super Humanizer

4 wins
  • Penalties Score −1.0 vs −5.0
  • Originality.ai Detector 95.0 vs 88.7
  • Blog Post Category 70.3 vs 58.1
  • News Article Category 70.7 vs 61.4

Undetectable.ai

13 wins
  • Bypass rate Score 74.6 vs 61.8
  • Readability Score 63.9 vs 56.6
  • Meaning preservation Score 79.2 vs 76.6
  • Consistency Score 93.0 vs 90.4
  • GPTZero Detector 85.0 vs 46.6
  • Winston AI Detector 98.5 vs 77.9
  • ZeroGPT Detector 83.8 vs 68.0
  • Copyleaks Detector 87.9 vs 83.3
  • Pangram Detector 3.5 vs 0.0
  • Marketing Copy Category 71.5 vs 55.4
  • and 3 more

Same prompt, both outputs

Both tools were given this AI-written passage in the October 2026 cycle. Below it is what each one returned. This prompt was picked by a fixed rule, not by how the tools scored.

Personal Statement · Application Essay · written by gpt-6-sol · 357 words

I first became interested in human-computer interaction when I watched my grandfather struggle to book a medical appointment online. The website had all the information he needed, but its small text, confusing menus, and unclear error messages made a simple task feel impossible. I sat beside him and helped him finish the booking, but the experience stayed with me. Technology is often described as a way to make life easier. I want to understand why it sometimes does the opposite, and how to change that.

What draws me to human-computer interaction is its combination of technical problem-solving and attention to people. A system can be fast and reliable yet still fail if someone cannot understand or trust it. I am interested in the decisions behind that experience: how designers learn what users need, how they test assumptions, and how they balance accessibility, privacy, and simplicity. I want to study the methods that turn observations about everyday frustrations into better products.

I am especially interested in accessibility. My grandfather’s experience showed me that a design choice that seems minor to one person can determine whether another person can use a service at all. Studying human-computer interaction would help me learn to involve people with different abilities, backgrounds, and levels of digital confidence throughout the design process. I want to become better at listening before proposing solutions, and at evaluating whether those solutions work outside a classroom or lab.

I also enjoy the creative side of the field. Improving an interface means asking questions, trying ideas, and being willing to revise them when evidence challenges an initial plan. That process appeals to me because it treats mistakes as opportunities to learn rather than reasons to stop.

I hope to study human-computer interaction so I can help build technology that respects the people who rely on it. Whether I work on healthcare services, educational tools, or products I cannot yet imagine, I want to make digital experiences clearer and more inclusive. For me, success would mean someone like my grandfather could complete an important task independently, without needing to ask for help—and feel confident doing it again the next time.

Show the full input

378 words (input 357)

How did you first get involved in the subject? An experience with my grandfather inspired my interest in human-computer interaction. He wanted to book a medical appointment online, and the site contained all the necessary information but getting there was impossible.

Small text, unclear menus, and unread error messages hindered his progress.

I was able to sit with him and help him complete the booking, but the memory of the task stayed with me. Technology is supposed to make our lives easier. I want to know how we start getting backwards.

That I love the interface I love it for what it was - the technical solving of a problem along with a sensitivity to the human elements. A system can be swift, efficient and trusted, but ultimately will still be ineffective if people don't understand it or don't believe in it. I want to analyze how designers understand end-user behavior, how they evaluate what it is that they think they want, and how they decide what matters most - ease of use, privacy, openness.

I am particularly interested in accessibility. My grandfather's story made me realize how a simple design decision might be insignificant to one individual, but critical to someone else's ability to access a service. Studying human-computer interaction would teach me how to include people with diverse digital experience, abilities, and levels of confidence during design. I would like to improve at listening more before I start designing, and at assessing whether solutions are effective in "the real world."

I also love the creative aspect of the discipline. Making an interface better is about asking questions, testing ideas, and being unafraid to change course if the data shows your original approach isn't working. I like that aspect because it reminds me that when something goes wrong, it doesn't have to stop you.

I want to learn human-computer interaction so that I can help develop technology that considers the people who use it. Whether I develop new healthcare services, educational systems, or unknown products, I want to make digital services more user friendly and accessible. To me, success would be being able to simply do something by yourself, like what my grandfather did, without having to ask for help and being able to do it again.

Show the full output
  • GPTZero0.99passed
  • Pangram0.00caught
  • Winston AI1.00passed
  • ZeroGPT0.86passed
  • Copyleaks1.00passed
  • Originality.ai1.00passed

Passed 5 of 6 detectors.

Every Super Humanizer run in October 2026 →

432 words (input 357)

I am interested in studying human-computer interaction. Recently I had the opportunity to sit next to my grandfather as he tried to book a medical consultation online. After what seemed like an age we finally managed to complete the online booking. During this time I realized that the website did in fact contain all of the information my grandfather required; however the online interface contained small text, confusing menus, and vague error messages that made a seemingly simple task extremely difficult. I am interested in technology because it should make our lives easier, and I am interested in finding out why technology does not always have this effect.

What I find most fascinating about Human-Computer Interaction is the interplay of technical problem solving and human centered design. A system can be really fast and efficient but fail to deliver because of tiny design choices that make it hard for users to understand what is going on. I’m interested in how designers arrive at the decisions they do and how different methods and techniques are used to gather information about users and their problems, and then to design solutions to those problems.

One of the biggest interests I have within the field of human-computer interaction is accessibility. The experience I described above shows just how much one seemingly minor design decision can affect a person’s ability to use a service. In studying human-computer interaction, I hope to explore methods of including people of all abilities, backgrounds, and levels of experience with computers in the design process. This means learning to listen to users and to test solutions outside of the classroom or lab.

On a more positive note, I am very interested in the creative side of this field. Designing interfaces to improve human interaction with computers is all about asking questions and testing out different solutions. Even when those solutions fail, there is always something to learn from them.

Human-computer interaction could be a field that I am particularly interested in because I can help design technology that is more respectful to the people who use it. This could be to design healthcare services, educational tools, or other technology that can help make people’s lives easier. The ultimate goal for me is to help design technology that is clear and inclusive for people of all backgrounds and abilities. My goal would be for my grandfather to be able to do online something important to him in a way that is easy and he feels confident that he completed the task online and that he can do it again online in the future.

Show the full output
  • GPTZero1.00passed
  • Pangram0.00caught
  • Winston AI1.00passed
  • ZeroGPT0.92passed
  • Copyleaks1.00passed
  • Originality.ai1.00passed

Passed 5 of 6 detectors.

Every Undetectable.ai run in October 2026 →

Each chip is one detector's score for that output, from 0 (AI) to 1 (human). A score of 0.50 or higher counts as a pass and shows green; below that the detector caught it and the chip shows red.

Questions people ask

Is Super Humanizer better than Undetectable.ai?

Undetectable.ai finished 3.6 points ahead of Super Humanizer in the October 2026 cycle, 71.18 to 67.58, ranking #3 against #7 in a field of 14. Undetectable.ai scored higher on 5 of 6 detectors and 4 of 6 writing categories. Both tools ran the same 30 prompts, scored by the same 6 detectors, under methodology v1.3.0; every input, output and verdict is published.

Which bypasses AI detectors better, Super Humanizer or Undetectable.ai?

Undetectable.ai posted the higher bypass rate, 74.6 vs 61.8, and scored higher on 5 of 6 detectors. Super Humanizer led on Originality.ai; Undetectable.ai led on GPTZero, Copyleaks, Winston AI, ZeroGPT and Pangram.

Do Super Humanizer and Undetectable.ai keep the original meaning?

Undetectable.ai preserved meaning better, 79.2 vs 76.6 on our 0–100 similarity scale, though the confidence intervals overlap. On readability, Undetectable.ai rated higher, 63.9 vs 56.6. Penalties this cycle: Super Humanizer −1.0, Undetectable.ai −5.0.