Berk Kalelioğlu
Research interests
Berk focuses on machine learning, agentic AI tools, and large and small language models (LLMs and SLMs).He is part of the AIMultiple benchmark team, conducting assessments and providing insights to help readers understand emerging technologies and their real-world applications.
Professional experience
He began his career as a Tech Project Lead at ODTU IVME-R, where he led a project to build physical quantum and pseudorandom number generators.After his tenure at IVME-R, he co-founded a game development company and released a game on Steam.
He later shifted his career toward AI and joined AIMultiple as a Researcher.
Education
Berk holds a Bachelor’s degree in Mathematics from Ankara University.Latest Articles from Berk
A-CODE-CLI Bench: Agentic CLI Benchmark
Agentic CLI tools are AI coding tools that can create and delete files, run commands, plan, and execute the coding of the entire project. We benchmarked the leading tools across 10 real-world web development scenarios, performing ~600 atomic validation checks per agent and more than ~5,000 total automated test executions, including backend logic, frontend functionality,…
Tabular Models Benchmark: Performance Across 19 Datasets
We benchmarked 13 tabular learning models on 19 real-world datasets covering roughly 272,000 samples, with dataset sizes from 435 to 48,800 rows. Every model ran with 5-fold cross-validation on identical splits. Each dataset is a round-robin of head-to-head matches between models, decided by the primary metric. Elo aggregates all 1,398 matches into a single rating…
MCP Gateway Benchmark: Latency & Security of 6 Gateways
An MCP gateway sits between an AI agent and the tools it calls, and vendors position it as the security layer for that traffic. We benchmarked six MCP gateways against one instrumented backend on a single box, measuring added latency, per-tool authorization, content protection, and audit completeness. A product appears against a control only if…
AIM-Decision: Jev vs Kev vs LLMs
Decision models, also called System One models, score a fixed set of options instead of generating text token by token. We tested 22 decision models on 1,655 classification questions, such as rating a security flaw or a product recall, and ran 19 of them on 50 browser tasks. One attempt per task; the LLMs ran…
Always-On Agents: Dots vs Grok Bot vs Muse
Always-on agents keep working after you close the app. Each one runs on a computer that keeps its files and memory, starts tasks on a schedule or when something happens, and messages you when it needs a decision. We compared five of them: OpenAI Dots, Grok Bot, Meta Muse, Hermes Agent and OpenClaw. *Nous Research…
AIM Agentic Web Benchmark
Agents rely on web interfaces to complete tasks on the web. To compare task completion across web interfaces, we built the AIM Agentic Web Benchmark and ran 100 tasks through 7 interfaces, 5 times each, for 3,500 scored results. Bright Data CLI and Bright Data MCP completed 8.2 and 8.4 more tasks per 100 than…
Agentic IT: Can AI Agents Design a Benchmark
We tested 16 models on a benchmark design in text-to-SQL and tool calling. Each model built one benchmark per topic, for a total of 32 submissions. No submission passed every rubric criterion. The agents could build and run tests, but none demonstrated both a blank-answer test and a correct-answer test of their own scorer. Text-to-SQL…
AIM Enterprise: Agentic Enterprise Benchmark
Enterprises use LLMs every day for their regular tasks. To find the most cost-efficient LLMs, we designed AIM Enterprise, an agentic enterprise benchmark, where we used 69 real enterprise tasks across strategy, marketing, HR, sales, and operations. Two judge models scored every file that passed the checks. On 32.7% of the individual scores the two…
AI Deep Research: Codex vs Claude vs Grok vs Exa
A deep research tool answers a question with a written report instead of a page of links. We ran five ways of producing one over the same 20 business research briefs and scored every report against rules written before the runs to find the best tool for deep research. Four of the five are coding…
Best Flat-Rate LLM API Providers
Flat-rate LLM providers sell unlimited model usage for a fixed monthly price instead of billing per token. This model spread because agentic coding sessions can use tens of millions of tokens, so a per-token bill is hard to predict. Very few providers offer a true flat fee; most plans marketed as flat carry a usage…
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