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
Seedream 5.0 Pro

Seedream 5.0 Pro

ByteDance's July 2026 flagship image generation model with native 2K output, editable layer separation, multilingual text in 14 languages, and precision region editing.

Grok 4.5

Grok 4.5

Grok 4.5 is xAI's 1.5-trillion-parameter V9 MoE model, publicly launched July 8 at $2/M input - cheap, fast, and token-efficient, though neutral harness benchmarks put it well behind Fable 5 and Opus 4.8 on coding.

GPT-Live-1

GPT-Live-1

OpenAI's full-duplex voice model that listens and speaks simultaneously, replacing Advanced Voice Mode in ChatGPT with three reasoning tiers backed by GPT-5.5.

Grok 4.1 Fast

Grok 4.1 Fast

Grok 4.1 Fast is xAI's agent-optimized model with a 2M-token context window, #1 ranking on tau-bench Telecom, and one of the lowest input prices among frontier-adjacent APIs at $0.20/M tokens.

GPT-5.4 mini

GPT-5.4 mini

OpenAI's mid-range model in the GPT-5.4 family delivers near-flagship coding and agentic performance at $0.75/M input tokens with a 400K context window.

LongCat-2.0

LongCat-2.0

Meituan's 1.6T-parameter open-source MoE coding model, trained end-to-end on 50,000 domestic Chinese ASICs, with native 1M token context and a 59.5 SWE-bench Pro score.

Gemini Omni Flash

Gemini Omni Flash

Google DeepMind's multimodal video generation model that creates 10-second clips with native audio from text, images, or video inputs - and lets you refine results through conversation.

Holo3-35B-A3B

Holo3-35B-A3B

H Company's open-weight sparse MoE vision-language model purpose-built for desktop computer use, scoring 82.6% on OSWorld-Verified with only 3B active parameters.