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
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…
AI VC Benchmark: 16 AI Agents on Client Identification
Client identification is part of commercial due diligence. We tested 16 models on client-list research for three research and review sites, one of them AIMultiple, scoring their submissions against answer keys we compiled and the pages they cited. We did not set reasoning effort for any model. Each ran at its agent program’s default, and…
Agentic IT: Can AI Agents Design a Benchmark
We tested 16 models on benchmark design in text-to-SQL and tool calling. None of their 32 submissions passed every criterion. The agents could build and run tests, but none demonstrated both a blank-answer test and a correct-answer test of its own scorer. Text-to-SQL turns a question into a database query; tool calling selects a function…
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…
OpenClaw Alternatives: Hermes vs ZeroClaw vs Grok Bot
Autonomous AI agents, such as OpenClaw and Hermes agent, automate multi-step tasks that would normally require constant human input. While OpenClaw has become the most widely adopted always-on autonomous agent, many users are seeking alternatives due to its challenging deployment process and complex configuration requirements. We provide 5 leading OpenClaw alternatives, highlighting their key capabilities…
Computer Use Agents: Benchmark & Architecture
Computer-use agents operate real desktops and web apps. Their designs, limits, and trade-offs are often unclear. We break down how leading systems work, how they learn, and how their architectures differ. We also reference a focused UI-grounding benchmark on 100 desktop screenshots, across 4 task types and 5 runs per sample. It isolates the quality…
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…
Time Series Classification Benchmark: Foundation Models vs Classical Methods
We benchmarked 13 time series classification methods, from pretrained time series foundation models to a 22-feature baseline from 2019, on 33 UCR/UEA datasets under one frozen protocol. That is 14,638 recorded method-dataset-resample cells, 11,874 of them scored. The chart compares 12 methods on the 15 univariate datasets every one of them completed, each dataset run…
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,…
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