Author
Emily Napier
GPTZero Tops Accuracy on Chicago Booth Benchmark in 2026
GPTZero outperforms other AI detectors like Pangram on Chicago Booth Benchmark, achieving ~99% accuracy and industry-leading recall for AI detection.
GPTZero vs Pangram: AI Detector Accuracy Comparison
See how GPTZero compares to Pangram in 2025. Our benchmark shows GPTZero leading in accuracy, recall, and classroom reliability.
GPTZero performance on Claude Sonnet 4.5
GPTZero achieved 97% recall on Claude Sonnet 4.5 at 1% false positives, surpassing its initial 95% on GPT-5. Detection remains strong across new models, with recall now over 99%.
GPTZero vs Copyleaks vs Originality: AI Detector Accuracy Comparison
Being able to tell if a document has been written by AI (or not) has become more essential, while also becoming trickier, as generative AI only becomes more sophisticated. Plus, AI detection is always shifting, as what worked a few months ago might quickly have become outdated. This inevitably leads to asking: what is the most accurate AI detector? After all, what never changes is the importance of accuracy. But with so many options out there, what are the best AI detectors to choose from? In t
GPTZero detects GPT-5 even better in our August update
In GPTZero's August 2025 update, we improved our GPT-5 detection rate to over 97%, adapting quickly to the latest AI models.
GPTZero’s Massive AI Detector Update for Summer 2025
GPTZero’s new Model 3.7b improves drastically on the latest language models from the top providers, just in time for the new 2025/2026 school year.
Behind the Scenes: Introducing “AI Polished”
With our latest model update, GPTZero introduces higher granularity in our AI detector by adding a new classification called “AI Polished”. This tag identifies text originally written by a human and later improved by AI.
GPTZero AI detection supports German and Portuguese
We’ve released a major update to our multilingual model that outperforms competitors across the board. By training on more challenging human and AI data, we've achieved a less than 1% false positive rate on all supported languages (Model 3.2m).