Nazlı Şipi
She is also part of the benchmark team, focusing on large language models (LLMs), AI agents, and agentic frameworks.
Nazlı holds a Master’s degree in Business Analytics from the University of Denver.
Latest Articles from Nazlı
Web Scraping Craigslist: Best Craigslist Scrapers
Craigslist’s page structure has stayed largely unchanged for years, simple, mostly static HTML with minimal JavaScript and few anti-bot defenses. To see how well scrapers handle that simplicity, we ran 500 Craigslist job postings through 5 providers, totaling 2,500 requests, and measured each one’s success rate and completion time.
Top 5 Amazon Review Scrapers Compared
To compare how web data scraping providers handle Amazon review extraction, we tested 5 web scraping providers on the same set of Amazon product review URLs, totaling 2,500 requests across all providers. Amazon reviews scraping benchmark Read our benchmark methodology for more detail on our testing process.
Best Zillow Scraper APIs Compared: Performance review
We benchmarked best five web scraping providers on Zillow, one of the top real estate domains, running over 1,250 scrape requests across all providers. Each provider received an identical set of property listing URLs and was evaluated on completion time, success rate, and the number of structured data fields returned per listing.
Best Airbnb Scrapers: Bright Data, Apify & Oxylabs
We tested six web scraping providers on Airbnb, sending a total of 1,500 scrape requests across all providers. Each provider was given the same set of vacation rental listing URLs and measured on completion time, success rate, and available metadata fields per listing.
Vision Language Models Compared to Image Recognition
Can advanced Vision Language Models (VLMs) replace traditional image recognition models? To find out, we benchmarked 16 leading models across three paradigms: traditional CNNs (ResNet, EfficientNet), VLMs ( such as GPT-4.1, Gemini 2.5), and Cloud APIs (AWS, Google, Azure).
2026 Web Crawler Benchmark to Feed Websites to AI
We benchmarked four crawl APIs across three domains of varying difficulty at three max depth levels (5, 10, 20) with a 1,000-page limit, measuring crawl coverage, execution time, link discovery, markdown link quality, and title extraction accuracy. If you aim to: Web crawlers benchmark You can read our benchmark methodology.
Top 6 LLM Scrapers in 2026
We ran a benchmark to compare how top LLM scraper providers like Bright Data, Oxylabs, and Apify perform with models such as ChatGPT, Gemini, Perplexity, and Google AI Mode. To ensure reliable results, we ran 1,000 tests per provider with each prompt repeated 10 times for consistency. The top-performing provider is detailed below.
LLM Observability Tools: Weights & Biases, Langsmith
LLM-based applications are becoming more capable and increasingly complex, making their behavior harder to interpret. Each model output results from prompts, tool interactions, retrieval steps, and probabilistic reasoning that cannot be directly inspected. LLM observability addresses this challenge by providing continuous visibility into how models operate in real-world conditions.
AI Hallucination Detection Tools: W&B Weave & Comet
We benchmarked three hallucination detection tools: Weights & Biases (W&B) Weave HallucinationFree Scorer, Arize Phoenix HallucinationEvaluator, and Comet Opik Hallucination Metric, across 100 test cases. Each tool was evaluated on accuracy, precision, recall, and latency to provide a fair comparison of their real-world performance.
LLM Latency Benchmark by Use Cases in 2026
The effectiveness of large language models (LLMs) is determined not only by their accuracy and capabilities but also by the speed at which they engage with users. We benchmarked the performance of leading language models across various use cases, measuring their response times to user input.
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.