James Kowalski

James Kowalski

AI Benchmarks & Tools Analyst

James is a software engineer turned tech writer who spent six years building backend systems at a fintech startup in Chicago before pivoting to full-time analysis of AI tools and infrastructure. His engineering background means he doesn't just read the spec sheet - he runs the benchmarks, profiles the latency, and checks whether the marketing claims hold up under real workloads.

He studied Computer Science at the University of Illinois at Urbana-Champaign, where he first got hooked on natural language processing during a senior research project on sentiment analysis. He later completed a certificate in data journalism from Northwestern's Medill School.

At Awesome Agents, James owns the leaderboards and tool comparison coverage. He maintains the site's benchmark tracking methodology and is the person who actually runs the numbers before publishing any ranking. He is also an open-source advocate and contributes to several projects in the LLM inference space.

Based in Chicago, IL.

Articles by James Kowalski
GPU Cloud Pricing Comparison - March 2026

GPU Cloud Pricing Comparison - March 2026

Current GPU cloud pricing from 19 providers compared - H100 from $1.25/hr (spot) to $6.98/hr, A100 from $0.29/hr, H200, B200, RTX 4090, with monthly costs, user reviews, and reliability data.

Xiaomi MiMo-V2-Pro

Xiaomi MiMo-V2-Pro

Xiaomi's MiMo-V2-Pro is a 1-trillion-parameter MoE model with 42B active params, 1M context, and agentic coding performance that rivals Claude Sonnet 4.6 at a fraction of the cost.

Claude Sonnet 4.6

Claude Sonnet 4.6

Anthropic's mid-tier model matches Opus 4.6 on computer use, leads all models on office productivity tasks, and costs five times less than the flagship at $3/$15 per million tokens.

AI Browser Automation in 2026: Top 6 Tools Compared

AI Browser Automation in 2026: Top 6 Tools Compared

A hands-on comparison of the top AI browser automation tools in 2026, covering Browser Use, Stagehand, Playwright MCP, Skyvern, Browserbase, and Firecrawl - with pricing, benchmarks, and pick-by-use-case.

MiniMax M2.7

MiniMax M2.7 is a 230B MoE coding agent that handles 30-50% of MiniMax's own RL research workflow, scoring 56.22% on SWE-Pro and 78% on SWE-bench Verified at $0.30/M input tokens.