Head to head · October 2026 cycle
Humbot vs Walter Writes
Walter Writes finished 1.8 points ahead of Humbot in the October 2026 cycle, 63.53 to 61.74, ranking #10 against #13 in a field of 14. Walter Writes scored higher on 1 of 6 detectors and 5 of 6 writing categories. Humbot came out ahead on application essay prompts (74.0 vs 66.1), ZeroGPT (77.3 vs 71.6) and Copyleaks (93.3 vs 89.3). The bypass-rate gap, 71.4 to 72.8, sits inside both tools' 95% confidence intervals, so treat bypass as a draw.
- Detectors
- 4 – 1 (1 tie)
- Categories
- 1 – 5
- Components
- 2 – 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 →
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. Humbot took 4 of 6, with 1 tied.
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 →
Head-to-head history
Overall score in every cycle both tools were tested. Humbot has finished ahead in 3 of 4 shared cycles.
| Cycle | Humbot | Walter Writes | Margin |
|---|---|---|---|
| October 2026 | 61.74 #13 | 63.53 #10 | → 1.8 |
| August 2026 | 66.95 #5 | 60.83 #9 | ← 6.1 |
| July 2026 | 66.42 #5 | 62.84 #7 | ← 3.6 |
| June 2026 | 80.28 #4 | 67.24 #10 | ← 13.0 |
Where each one wins
Every measure above that one tool won outright, largest margin first within each group.
Humbot
7 wins- Readability Score 65.0 vs 63.5
- Consistency Score 94.0 vs 92.7
- ZeroGPT Detector 77.3 vs 71.6
- Copyleaks Detector 93.3 vs 89.3
- Originality.ai Detector 94.0 vs 91.5
- Winston AI Detector 98.1 vs 96.5
- Application Essay Category 74.0 vs 66.1
Walter Writes
9 wins- Penalties Score −9.0 vs −10.0
- Meaning preservation Score 70.4 vs 68.6
- Bypass rate Score 72.8 vs 71.4
- GPTZero Detector 87.8 vs 65.6
- Academic Essay Category 71.4 vs 40.8
- Blog Post Category 61.2 vs 37.1
- News Article Category 56.0 vs 44.9
- Discussion Board Category 37.9 vs 32.7
- Marketing Copy Category 53.2 vs 48.7
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.
AI-written input
Every tool on this prompt →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 Show less
461 words (input 357)
My initial interest in human computer interaction began when I observed my grandfather attempting to schedule a medical appointment online. He was able to obtain the necessary information from the site, however; the site's small print, complicated navigation systems, and unclear error notifications made what should have been a relatively easy process seem nearly impossible. I assisted him in completing the reservation process, however; the experience remained with me. It is commonly believed that technology will assist people in living their lives with greater ease. I wish to determine why technology can often create the opposite effect and how to rectify this situation.
Human-computer interaction has a unique combination of technical problem solving and user-centered design which attracts me to the field. While a system can be high performing and reliable, if a user cannot use the system or does not find the system trustworthy, then the system may have failed. I am drawn to the decision-making process involved in creating the user's experience: how designers discover what users require, how designers evaluate their hypotheses, and how designers consider the trade-offs between accessibility, privacy, and simplicity when designing products. I wish to explore the processes used to transform observations of daily frustrations into improved products.
I have a special interest in making products accessible. The experience of my grandfather demonstrated to me how a simple design decision made by someone else can affect whether or not another individual can access a service at all. Learning about Human-Computer Interaction will teach me how to include individuals with diverse abilities, backgrounds and levels of digital confidence during each phase of the design process. Additionally, I want to grow as an active listener prior to developing solution ideas, and as an evaluator of how well those solutions perform in real-world settings rather than just within the confines of a classroom or laboratory.
In addition to the analytical aspect of the job, I like the creative part as well. When you improve an interface you ask questions; you come up with new ways of doing things; and you are willing to change your mind if facts challenge your first idea. This method works for me as it views mistakes as learning experiences and not reasons to quit.
I want to learn about Human Computer Interaction in order to create technologies which are designed with the user in mind. Regardless of whether I am working in Health Care, Education, or a product that I have not thought of at this point; I want to create user friendly and inclusive digital environments. To me, being successful would mean my Grandfather would be able to do something important on his own, without having to ask for assistance and would be able to continue doing it on his own.
Show the full output Show less
- GPTZero0.00caught
- Pangram0.00caught
- Winston AI0.99passed
- ZeroGPT0.91passed
- Copyleaks1.00passed
- Originality.ai0.98passed
Passed 4 of 6 detectors.
Every Humbot run in October 2026 →523 words (input 357)
I got interested in Human-Computer Interaction (HCI) after seeing my Grandfather have trouble to book a doctor's appointment using his computer. He was able to find all the information he needed; however, his eyesight was not good enough to read the fine print, and there were many "choices" to navigate through to get to where he wanted to go. On top of this, some of the "error messages" did not clearly tell him why the application failed, which made a simple task seem almost impossible.
He asked me to assist him in completing the appointment, and while I completed the appointment, the experience stuck with me.
People are continually told that technology makes their lives easier. While this may be true most times, there are many times when technology makes things difficult. And this is exactly what I want to understand – why technology fails us at times -- and how we can prevent this from happening.
I am drawn to HCI because it involves both the technical aspects of solving problems and understanding people. Just because a system works quickly and efficiently does not mean that it will always be successful. It may never succeed if no one understands it or trusts it. I want to know about the decision-making processes that lead to these user experiences. Specifically, I want to know how designers gather data about what users need, how they evaluate their own assumptions based upon data collected during usability testing, and how they weigh the importance of issues such as accessibility, privacy, and simplicity. Ultimately, I want to apply the methods used by researchers in HCI to take everyday complaints and turn them into better products.
Accessibility is something that I am particularly interested in. The fact that my Grandfather's frustration with using the web-based medical scheduling tool was due to something as seemingly inconsequential as font size demonstrated to me just how much a designer's decision regarding an element of the product can either allow or deny a potential customer access to the service. By studying HCI I believe that I can develop my ability to include users with disabilities, users with limited backgrounds and/or little exposure to digital systems within the entire design cycle. Furthermore, I want to improve my ability to listen before making recommendations and to assess whether the solution proposed is effective beyond the confines of a classroom or laboratory setting.
In addition to the analytical nature of HCI, I also appreciate the creative aspect of this discipline. Designing interfaces requires questioning what is working and what needs to be changed. In addition, designing interfaces requires developing ideas, and having the willingness to abandon ideas when data contradicts your original intent. This process resonates with me because it views errors as learning opportunities as opposed to a reason to discontinue development efforts.
Ultimately, I hope that my studies in HCI will enable me to create technology that acknowledges people who depend on technology. Regardless of whether I contribute toward creating tools for medical services, educational platforms or applications I have not thought of yet; I want to create more accessible and understandable digital experiences.
Show the full output Show less
- GPTZero0.97passed
- Pangram0.00caught
- Winston AI1.00passed
- ZeroGPT0.71passed
- Copyleaks1.00passed
- Originality.ai1.00passed
Passed 5 of 6 detectors.
Every Walter Writes 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 Humbot better than Walter Writes?
Walter Writes finished 1.8 points ahead of Humbot in the October 2026 cycle, 63.53 to 61.74, ranking #10 against #13 in a field of 14. Walter Writes scored higher on 1 of 6 detectors and 5 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, Humbot or Walter Writes?
Walter Writes posted the higher bypass rate, 72.8 vs 71.4, and scored higher on 1 of 6 detectors. Humbot led on Originality.ai, Copyleaks, Winston AI and ZeroGPT; Walter Writes led on GPTZero. The overall bypass gap is inside both 95% confidence intervals, so it is not a reliable difference.
Do Humbot and Walter Writes keep the original meaning?
Walter Writes preserved meaning better, 70.4 vs 68.6 on our 0–100 similarity scale, though the confidence intervals overlap. On readability, Humbot rated higher, 65.0 vs 63.5. Penalties this cycle: Humbot −10.0, Walter Writes −9.0.