Head to head · November 2026 cycle
Rephrasy vs WriteHuman
WriteHuman finished 17.6 points ahead of Rephrasy in the November 2026 cycle, 70.11 to 52.50, ranking #1 against #16 in a field of 16. WriteHuman scored higher on 5 of 6 detectors and 6 of 6 writing categories. Rephrasy came out ahead on consistency across categories (96.8 vs 92.4). The bypass-rate gap, 73.9 to 81.1, sits inside both tools' 95% confidence intervals, so treat bypass as a draw.
- Detectors
- 0 – 5 (1 tie)
- Categories
- 0 – 6
- Components
- 1 – 4
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. WriteHuman took 5 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 →
Where each one wins
Every measure above that one tool won outright, largest margin first within each group.
Rephrasy
1 win- Consistency Score 96.8 vs 92.4
WriteHuman
15 wins- Penalties Score −1.0 vs −11.0
- Meaning preservation Score 59.2 vs 44.9
- Bypass rate Score 81.1 vs 73.9
- Readability Score 55.4 vs 52.7
- Pangram Detector 68.9 vs 48.4
- ZeroGPT Detector 87.3 vs 75.8
- Originality.ai Detector 36.5 vs 31.0
- GPTZero Detector 99.9 vs 96.2
- Winston AI Detector 97.2 vs 95.3
- Application Essay Category 74.3 vs 48.1
- and 5 more
Same prompt, both outputs
Both tools were given this AI-written passage in the November 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 gemini-3-8-flash · 328 words
Standard economic theory told me that humans are rational optimizers—cold, calculating machines operating within a frictionless world of supply and demand. However, watching the world around me, particularly during the economic turbulence of recent years, revealed a starkly different reality. People panic-buy during crises, succumb to the sunk cost fallacy in failing investments, and systematically choose short-term gratification over long-term financial security. It was this glaring disconnect between the elegance of classical models and the messy reality of human decision-making that first ignited my passion for behavioral economics.
During my undergraduate studies in economics, I found myself constantly questioning the *Homo economicus* assumption. While I respected the predictive power of quantitative modeling, the numbers felt hollow without the human element. My intellectual turning point came when I encountered Richard Thaler’s concept of "mental accounting." Suddenly, abstract financial behaviors—such as treating tax refunds differently from regular paychecks—made intuitive sense. I realized that to truly fix economic inefficiencies, we cannot merely assume how people *should* behave; we must study how they *actually* behave.
This fascination led me to research the intersection of choice architecture and public policy. I became deeply interested in how subtle interventions, or "nudges," could yield profound societal benefits—from increasing retirement savings rates to encouraging sustainable energy consumption. Behavioral economics represents the democratization of economic theory, humanizing data through the lens of psychology, neuroscience, and sociology.
At your institution, I want to bridge the gap between theoretical research and applied policy design. I am particularly drawn to your focus on experimental economics and empirical field research, where I hope to investigate how cognitive biases impact wealth inequality and financial inclusion.
I do not want to study economics merely to interpret markets from a detached perspective; I want to understand the psychological architecture that drives them. A graduate degree in behavioral economics will equip me with the interdisciplinary tools required to design policies, products, and systems that work with the grain of human nature, rather than against it.
Show the full input Show less
323 words (input 328)
For years, I thought that I wanted to go into economics, because economics attempts to answer questions about human decisions. Then I realized that it wasn't really about human decisions, it was all about the phenomenon of supply and demand. Supply and demand are really helpful to understand how markets work, but they can't really answer questions about why humans decide anything at all. Yet, the study of economics is so pervasive and successful, that I wondered if there could be a way to incorporate "how we make decisions" into a system.
In my undergraduate studies, I delved into the burgeoning literature around decision theory, the psychology of how people view risk and reward, and the simple mathematical models that ascribe numbers to the former and the latter. Then I encountered actual people, who proved to be stubborn and messy, consistently making decisions that did not adhere to the models. Rather than assuming people are rational and that they only need more and better information, I found the best way to understand people's decisions is to put them under the microscope.
That curiosity brought me into the world of behavioral economics, where economics and psychology meet in a quest to understand and explain human behaviors that, at first glance, seem strange and irrational. Such subtle nudges affect how we save for the future, spend our money, and make choices in our day-to-day life. Behavioral economics brings about a new theoretical model of how people behave.
Behavioral economics appeals to me because it steps beyond the imaginary blue box of traditional economics. I hope to study human behavior because each individual is more than a statistic, existing in a complex web of cultural and social realities that traditional economics neglects.
A graduate degree, I believe, will train me to measure and predict behaviors and then translate my research into solutions. That lets me respond to fundamental questions: In which contexts do these behaviors arise?
Show the full output Show less
- GPTZero1.00passed
- Pangram0.39caught
- Winston AI1.00passed
- ZeroGPT0.84passed
- Copyleaks1.00passed
- Originality.ai0.10caught
Passed 4 of 6 detectors.
Every Rephrasy run in November 2026 →381 words (input 328)
I’ve learned that traditional economics is based on rational, utility-maximizing agents. This led me to believe that people are logical and derive their actions based on expected and fine-tuned trade-offs. Quite the opposite is true. For example, people hoard supplies during a recession. Investors hold on to losing investments. Employees forgo retention bonuses. All of these illustrate people’s propensity to take sub-optimal actions andderive utility in the short-run at the expense of the long-run. People often prefer to receive a small utility today over a larger utility tomorrow. Again, behavioral economics is rich with examples of these discrepancies and inequities. From an academic standpoint, behavioral economics fills gaps found in traditional economics.
My interest in behavioral economics was sparked during my undergraduate years with my increasing frustration with economics being a science where assumptions could not be tested and validated. People aren’t perfect utility maximizers. Thaler’s mental accounting provides insights into individuals’ financial choices that are otherwise puzzling. People view financial wealth differently based on its source. A paycheck and tax refund are considered differently. In the same vein, if we are to understand, model and alleviate market inefficiencies, we must understand the true nature of people’s financial decisions as opposed to prescribing and projecting our preconceived notions.
This interest has Deepened my analysis of integration of behavioral economics and policy. The usage of behavioral economics has the potential to help and benefit the public in several ways such as optimizing the level of financial savings and also altering public energy consumption to more sustainable sources.
At your institution, my main objective will be to adopt several behavioral economics tools to assess public policy. I will particularly focus on public health and welfare policies. Your emphasis on experimental economics and field research will give me an opportunity to assess the role of behavioral economics and cognitive biases in public policy to reduce wealth inequality and promote financial accessibility.
My main interest in economics is to look beyond the traditional assessment of public welfare and health. Behavioral economics is integrated with a number of allied sciences. Therefore, I believe that furthering my education in behavioral economics will help in understanding several unexplored public policies and benefits. Ultimately, behavioral economics will assist in public policy formulation to positively alter public welfare and health.
Show the full output Show less
- GPTZero1.00passed
- Pangram1.00passed
- Winston AI1.00passed
- ZeroGPT1.00passed
- Copyleaks1.00passed
- Originality.ai0.45caught
Passed 5 of 6 detectors.
Every WriteHuman run in November 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 Rephrasy better than WriteHuman?
WriteHuman finished 17.6 points ahead of Rephrasy in the November 2026 cycle, 70.11 to 52.50, ranking #1 against #16 in a field of 16. WriteHuman scored higher on 5 of 6 detectors and 6 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, Rephrasy or WriteHuman?
WriteHuman posted the higher bypass rate, 81.1 vs 73.9, and scored higher on 5 of 6 detectors. WriteHuman led on GPTZero, Originality.ai, Winston AI, ZeroGPT and Pangram. The overall bypass gap is inside both 95% confidence intervals, so it is not a reliable difference.
Do Rephrasy and WriteHuman keep the original meaning?
WriteHuman preserved meaning better, 59.2 vs 44.9 on our 0–100 similarity scale. On readability, WriteHuman rated higher, 55.4 vs 52.7. Penalties this cycle: Rephrasy −11.0, WriteHuman −1.0.