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
GPT-5 mini

GPT-5 mini

OpenAI's original cost-efficient GPT-5 variant pairs a 400K context window with $0.25/$2.00 per million token pricing, still doing quiet duty as a cheap backbone for research agents a year after launch.

NVIDIA Nemotron 3.5 Lightning 30B-A3B

NVIDIA Nemotron 3.5 Lightning 30B-A3B

NVIDIA's 30B MoE model with 3B active parameters, distilled from Nemotron 3 Ultra, hits 86% PinchBench accuracy at up to 4x the output speed of comparable open models.

Qwen3.8-Max

Qwen3.8-Max

Alibaba's 2.4 trillion parameter flagship ships with real pricing and a published benchmark table, but the open-weight release it promised for this week still hasn't shown up.

Agent Platform Pricing Compared - August 2026

Agent Platform Pricing Compared - August 2026

Updated August 10: Anthropic's Agent SDK credit plan died before launch, Claude Managed Agents adds a new session-hour billing line, and E2B, Modal, and Daytona all rebuilt their per-second pricing.

Muse Glimmer

Muse Glimmer

Meta's 30B open-weight agentic model distilled from Muse Spark runs on a single consumer GPU, ships under Apache 2.0, and leads Gemma4-31B and Qwen3.6-27B on 5 of 8 agentic benchmarks.

Qwen3-30B-A3B

Qwen3-30B-A3B

Qwen3-30B-A3B is Alibaba's efficient MoE model that activates 3.3B of 30.5B parameters per token, matching much larger dense models on reasoning and agent benchmarks under Apache 2.0.

Muse Spark 1.1

Muse Spark 1.1

Meta's second Muse model ships a public API at $1.25/$4.25 per million tokens, a 1M-token context window, and the top score on Meta's own tool-use benchmarks.