Şevval Alper
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
Best AI Code Editor: Cursor vs Windsurf vs Replit in 2026
Making an app without coding skills is highly trending right now. But can these tools successfully build and deploy an app? To answer this question, we spent three days testing the following agentic IDEs/AI coding tools: Claude Code, Cline, Cursor, Windsurf and Replit Agent.
8 AI Code Models Benchmarked: LMC-Eval in 2026
More than 37% of tasks performed on AI models are about computer programming and maths.
OCR Benchmark: Text Extraction / Capture Accuracy [2026]
OCR accuracy is critical for many document processing tasks, and SOTA multi-modal LLMs are now offering an alternative to OCR.
Text-to-Video Generator Benchmark in 2026
A text-to-video generator is an AI system that turns written prompts into short videos by generating visuals, motion, and sometimes audio directly from natural language.
eCommerce AI Image Editing: GPT Images & Nano Banana
AI image editing tools analyze and automatically adjust product photos, allowing eCommerce businesses to enhance quality, remove backgrounds, or modify details with minimal effort. We tested the top 7 AI image editing tools on 20 images and 20 prompts across five dimensions, including prompt adaptability, realism, shadows, color rendering, and image quality.
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 memory.
MCP Benchmark: Top MCP Servers for Web Access in 2026
We tested 8 Model Context Protocol (MCP) servers across web search, data extraction, and browser automation. Each ran 4 tasks 5 times. We also stress-tested with 250 concurrent AI agents.
Top 10 Google Colab Alternatives in 2026
Google Colaboratory is a popular platform for data scientists and machine learning scientists, but its limitations and pricing may not meet your needs. Several alternatives offer unique features and capabilities that cater to different data science needs and scenarios.
Top AI Website Generators Benchmarked in 2026
To find the most helpful prompt-to-website creator, we benchmarked the following tools: If you need to learn about no-code AI website generator tools, you can follow the links: Benchmark results We conducted this benchmark using the latest versions of the tools available as of January 2025.
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.
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