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

Head to head · October 2026 cycle

Stealth Writer vs Undetectable.ai

Undetectable.ai finished 5.2 points ahead of Stealth Writer in the October 2026 cycle, 71.18 to 65.98, ranking #3 against #8 in a field of 14. Undetectable.ai scored higher on 2 of 6 detectors and 5 of 6 writing categories. Stealth Writer came out ahead on penalties (−3.0 vs −5.0), Originality.ai (99.9 vs 88.7) and blog post prompts (68.3 vs 58.1). The bypass-rate gap, 71.3 to 74.6, sits inside both tools' 95% confidence intervals, so treat bypass as a draw.

65.98 /100
overall score
#8 /14
rank ▼ 6
+5.20
Undetectable.ai leads →
Detectors
4 – 2
Categories
1 – 5
Components
2 – 3
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 CIs overlap Undetectable.ai by 3.3
71.3 74.6
Meaning preservation Undetectable.ai by 7.6
71.6 79.2
Readability Undetectable.ai by 22.2
41.8 63.9
Consistency Stealth Writer by 1.7
94.6 93.0
Penalties fewer is better Stealth Writer by 2.0
−3.0 −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. Stealth Writer took 4 of 6.

GPTZero Undetectable.ai by 44.1
40.9 85.0
Originality.ai Stealth Writer by 11.2
99.9 88.7
Copyleaks Stealth Writer by 8.8
96.7 87.9
Winston AI Stealth Writer by 0.9
99.4 98.5
ZeroGPT Stealth Writer by 6.9
90.7 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: Stealth Writer 66.8 · Undetectable.ai 69.0 Application Essay: Stealth Writer 66.1 · Undetectable.ai 76.4 Blog Post: Stealth Writer 68.3 · Undetectable.ai 58.1 Marketing Copy: Stealth Writer 57.4 · Undetectable.ai 71.5 Discussion Board: Stealth Writer 54.2 · Undetectable.ai 69.4 News Article: Stealth Writer 55.4 · Undetectable.ai 61.4
Stealth Writer Undetectable.ai Outer ring = 100
Academic Essay 6 prompts Undetectable.ai by 2.3
66.8 69.0
Application Essay 6 prompts Undetectable.ai by 10.3
66.1 76.4
Blog Post 6 prompts Stealth Writer by 10.2
68.3 58.1
Marketing Copy 6 prompts Undetectable.ai by 14.1
57.4 71.5
Discussion Board 3 prompts Undetectable.ai by 15.1
54.2 69.4
News Article 3 prompts Undetectable.ai by 6.0
55.4 61.4

Head-to-head history

Overall score in every cycle both tools were tested. Stealth Writer has finished ahead in 3 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: Stealth Writer 84.47 (#1) July 2026: Undetectable.ai 72.17 (#2) July 2026: Stealth Writer 68.07 (#4) August 2026: Undetectable.ai 61.49 (#8) August 2026: Stealth Writer 69.83 (#3) September 2026: Undetectable.ai 71.75 (#6) September 2026: Stealth Writer 75.39 (#2) October 2026: Undetectable.ai 71.18 (#3) October 2026: Stealth Writer 65.98 (#8) 66.0 71.2
Stealth Writer Undetectable.ai
Cycle Stealth Writer Undetectable.ai Margin
October 2026 65.98 #8 71.18 #3 → 5.2
September 2026 75.39 #2 71.75 #6 ← 3.6
August 2026 69.83 #3 61.49 #8 ← 8.3
July 2026 68.07 #4 72.17 #2 → 4.1
June 2026 84.47 #1 80.09 #5 ← 4.4

Where each one wins

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

Stealth Writer

7 wins
  • Penalties Score −3.0 vs −5.0
  • Consistency Score 94.6 vs 93.0
  • Originality.ai Detector 99.9 vs 88.7
  • Copyleaks Detector 96.7 vs 87.9
  • ZeroGPT Detector 90.7 vs 83.8
  • Winston AI Detector 99.4 vs 98.5
  • Blog Post Category 68.3 vs 58.1

Undetectable.ai

10 wins
  • Readability Score 63.9 vs 41.8
  • Meaning preservation Score 79.2 vs 71.6
  • Bypass rate Score 74.6 vs 71.3
  • GPTZero Detector 85.0 vs 40.9
  • Pangram Detector 3.5 vs 0.0
  • Discussion Board Category 69.4 vs 54.2
  • Marketing Copy Category 71.5 vs 57.4
  • Application Essay Category 76.4 vs 66.1
  • News Article Category 61.4 vs 55.4
  • Academic Essay Category 69.0 vs 66.8

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.

News Article · News Article · written by gpt-6-sol · 429 words

**Digital Privacy Laws Face a Borderless Data Economy**

Digital privacy laws promise people more control over their personal information. But as data moves between apps, advertisers, brokers and governments, those protections can be difficult to understand, enforce and exercise.

Rules such as the European Union’s General Data Protection Regulation and California’s privacy legislation require companies to disclose data practices and give people certain rights, including the ability to access or delete information. Their reach, however, depends on where a person lives, which organization holds the data and what exceptions apply.

That patchwork can leave users with different protections for similar activity. A person may be able to ask one company to erase a profile while finding that another organization can retain related records for legal, security or business purposes. Deleting data from one service also does not necessarily remove copies held by its partners.

Consent presents another limit. Privacy notices may explain how information will be used, but they can be long and difficult to compare. People often need a service for work, school or daily life, making a choice to “agree” less meaningful than it appears. Even when a company follows disclosure rules, users may not grasp what can be inferred from their location, purchases or browsing history.

Enforcement is challenging, too. Regulators must investigate technical systems that change quickly, sometimes across national borders. Large firms may have legal teams and compliance budgets that smaller companies lack, while penalties generally arrive after data has already been collected or exposed.

New technologies add pressure. Artificial intelligence systems can draw conclusions from datasets that appear harmless in isolation. Data brokers can combine information from multiple sources, making it hard for individuals to know who holds details about them. Some privacy laws also contain exemptions for government activity or sectors regulated under separate rules.

That does not mean privacy legislation is ineffective. Access rights, limits on data collection and penalties for misuse can change corporate behavior and give regulators tools to act. But legal rights alone cannot guarantee privacy when products are designed to gather extensive information by default.

The debate increasingly centers on whether lawmakers should rely less on individual consent and place stronger duties on organizations to minimize collection, secure data and justify how they use it. Clearer rules, effective oversight and privacy-conscious design could narrow the gap between the rights people have on paper and the control they experience online.

For now, digital privacy law remains an important safeguard with a basic constraint: information can travel, multiply and reveal more than the person who supplied it ever intended.

Show the full input

510 words (input 429)

Digital Privacy Law is up against an economy of information that is borderless.

Digital privacy laws will give people greater access to their own information. However, while those data are in transit between apps, advertisers, brokers, and governments, those protections can be opaque, challenging to adhere to, and hard to assert.

The European Union's GDPR and California privacy laws mandate certain rules for companies: It must tell people what it does with their data and provides some rights to access or delete. The extent to which they can reach depends on geographic location, the custodian of the data and exceptions.

Due to this patchwork, users can have varying levels of protection for comparable activities. One company may be able to wipe out a profile, another organization might be able to hold onto related records for legal, security or business reasons. Nor does deletion from one service de facto mean deletion from its partners' services.

A consent can be another restriction. A privacy notice might outline the ways in which information is being used, it can be lengthy, and sometimes hard to compare. However, often times, people require one and it may be a matter of work, school or daily life, so the act of “agreeing” is less of a meaningful gesture than it sounds. Even if a company observes the current rules about disclosure, consumers may not understand what information can be gleaned from their location, buying patterns or browsing history.

They are difficult to enforce, too. Governments and other authorities have to keep abreast of evolving technical systems, sometimes even crossing national boundaries. For many larger companies, legal advice might be available and budgets made for compliance, but penalties tend to come long after the data has been gathered and/or released.

Introduction of new technologies puts pressure on. In essence, AI systems have the ability to make sense of seemingly innocuous sets of data. Data brokers are also potentially able to consolidate information from several sources making it difficult for individuals to determine who are holding information about them. There are also exemptions for some privacy laws, for government activity or sectors that have their own law.

However, that does not mean that privacy law doesn't work. If access rights, restrictions on data gathering and punishments for misuse are altered, this can help influence corporate conduct and provide regulators with the tools to intervene. However, if the products are created to collect a lot of information by default, then legal rights cannot guarantee privacy.

There is growing focus on reducing reliance on individual consent and imposing greater responsibility on organisations to keep collection to a minimum, to protect data, and explain how data is used. Righter rules, better supervision and privacy-friendly design might help to bridge the chasm between people's rights in theory and their control in practice.

At least for the time being, privacy law applies to digital communications as well, and it has some fundamental limitations: information that is given can be moved, copied and made available to others in ways that are unexpected by the giver.

Show the full output
  • GPTZero0.40caught
  • Pangram0.00caught
  • Winston AI1.00passed
  • ZeroGPT0.93passed
  • Copyleaks1.00passed
  • Originality.ai1.00passed

Passed 4 of 6 detectors.

Every Stealth Writer run in October 2026 →

473 words (input 429)

**Digital Privacy Laws Face a Borderless Data Economy**

Digital privacy laws promise users greater control of their data online but implementing and exercising these rights when data is spread across apps, advertisers, data brokers and governments is far from simple.

Many countries including the EU and California have set out in laws such as the General Data Protection Regulation and the California Consumer Privacy Act what information companies have to disclose to consumers about their data practices and what rights consumers have over their personal information, such as the right to access, correction, deletion etc.

Current data protection regimes differ as to the extent to which users have the right to have their data removed from different services. In most cases deleting data from one service does not lead to all copies of that data being deleted by third parties.

But other factors also mitigate the impact of the consent granted in private and in public contexts, such as the lengthy notice of the terms of use of a service online, which are not always easy to compare with each other, and the various interests that can drive a person to access online a service which he/she/it needs for work, school or for many reasons of daily life.

Additionally, it is challenging for governments to monitor rapidly evolving systems, in many cases spread over the world. Because companies with large legal and compliance departments have a heavy burden placed on them, smaller companies are at a disadvantage. By the time severe action is taken by authorities, data has already been collected and shared, and been compromised.

New technologies are constantly being developed to process data about individuals. For example, the increasing use of Artificial Intelligence (AI) to analyze datasets comprising apparently innocuous information, raised fresh concerns regarding the processing of personal data. Meanwhile, data brokers who process thousands of datasets comprising a myriad of information about individuals may be unknown to the data subject and the scope of personal information held is not easily determinable.

But all the rights and restrictions on the use of information now pledged to respect individuals’ privacy are of little use when the basic conditions for their effective exercise are not met, especially when information can multiply without bounds when digitized and spread among various services in the form of default settings designed to extract information in greatest quantity and of widest variety.

The main debates today revolve around whether more emphasis should be put on placing more obligations on organizations to limit and protect data, as opposed to relying on individual rights and the need for consent in advance of the use of personal information.

Digital privacy laws – even at their best – are a frail defence against information that travels far and fast, multiplying to disclose secrets that the donor never intended to be revealed.

Show the full output
  • GPTZero1.00passed
  • Pangram0.00caught
  • Winston AI1.00passed
  • ZeroGPT1.00passed
  • 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 Stealth Writer better than Undetectable.ai?

Undetectable.ai finished 5.2 points ahead of Stealth Writer in the October 2026 cycle, 71.18 to 65.98, ranking #3 against #8 in a field of 14. Undetectable.ai scored higher on 2 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, Stealth Writer or Undetectable.ai?

Undetectable.ai posted the higher bypass rate, 74.6 vs 71.3, and scored higher on 2 of 6 detectors. Stealth Writer led on Originality.ai, Copyleaks, Winston AI and ZeroGPT; Undetectable.ai led on GPTZero and Pangram. The overall bypass gap is inside both 95% confidence intervals, so it is not a reliable difference.

Do Stealth Writer and Undetectable.ai keep the original meaning?

Undetectable.ai preserved meaning better, 79.2 vs 71.6 on our 0–100 similarity scale. On readability, Undetectable.ai rated higher, 63.9 vs 41.8. Penalties this cycle: Stealth Writer −3.0, Undetectable.ai −5.0.