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Şevval Alper

Şevval Alper

AI Researcher
17 Articles
Stay up-to-date on B2B Tech
Şevval is an AI researcher at AIMultiple. She has previous research experience in pseudorandom number generation using chaotic systems.

Research interests

Şevval focuses on AI coding tools, AI agents, and quantum technologies.

She is part of the AIMultiple benchmark team, conducting assessments and providing insights to help readers understand various emerging technologies and their applications.

Professional experience

She contributed to organizing and guiding participants in three “CERN International Masterclasses - hands-on particle physics” events in Türkiye, working alongside faculty to facilitate learning.

Education

Şevval holds a Bachelor's degree in Physics from Middle East Technical University.

Latest Articles from Şevval

Agentic AIApr 24

MCP Benchmark: Top MCP Servers for Web Access

We benchmarked 8 MCP servers across web search and extraction, as well as browser automation tasks, by running 4 different tasks 5 times on all suitable MCPs. We also performed a load test involving 250 concurrent AI agents. MCP servers with web access capabilities ProductSuccess rate for web search and extractSuccess rate for browser automationWeb…

AIMar 14

AI Code Review Tools Benchmark

With the increased use of AI coding tools, codebases have become more prone to vulnerabilities, which increased the need for effective code reviews. To address this, we introduce RevEval (AI Code Review Eval), which benchmarks the top four AI code review tools across 309 pull requests from repositories of varying sizes and evaluates their performance…

AIJan 28

OCR Benchmark: Text Extraction / Capture Accuracy

OCR accuracy is critical for many document processing tasks, and SOTA multi-modal LLMs are now offering an alternative to OCR. We benchmarked leading OCR services in DeltOCR Bench to identify their accuracy levels in different document types: Handwriting: GPT-5 (%95) stands out as the strongest performer, closely followed by olmOCR-2-7B (%94) and Gemini 2.5 Pro…

Agentic AIJan 28

Code Execution with MCP: A New Approach to AI Agent Efficiency

Anthropic introduced a method in which AI agents interact with Model Context Protocol (MCP) servers by writing executable code rather than making direct calls to tools. The agent treats tools as files on a computer, finds what it needs, and uses them directly with code, so intermediate data doesn’t have to pass through the model’s…

AIJan 22

LLM Parameters: GPT-5 High, Medium, Low and Minimal

New LLMs, such as OpenAI’s GPT-5 family, come in different versions (e.g., GPT-5, GPT-5-mini, and GPT-5-nano) and with various parameter settings, including high, medium, low, and minimal. Below, we explore the differences between these model versions by gathering their benchmark performance and the costs to run the benchmarks. Price vs. success: Key takeaways We used…

AIJan 22

Speech-to-Text Benchmark: Deepgram vs. Whisper

We benchmarked the leading speech-to-text (STT) providers, focusing specifically on healthcare applications. Our benchmark used real-world examples to assess transcription accuracy in medical contexts, where precision is crucial. Speech-to-text benchmark results Based on both word error rate (WER) and character error rate (CER) results, GPT-4o-transcribe demonstrates the highest transcription accuracy among all evaluated speech-to-text systems.…

Agentic AIOct 18

Top 4 AI Search Engines Compared

Searching with LLMs has become a major alternative to Google search. We benchmarked the following AI search engines to see which one provides the most correct results: Deepseek Search Andi ChatGPT Search with GPT-4o Perplexity Search Pro Benchmark results Deepseek is the leader of this benchmark, by correctly providing 57% of the data in our…