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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.
David vs Goliath: Qwen3.5-35B-A3B activates 3B parameters and beats Llama 4 Scout's 17B active on MMLU-Pro, GPQA, and coding benchmarks - but Scout's 10M context window and native multimodal support tell a different story.
A data-driven comparison of Alibaba's Qwen3.5-35B-A3B and NVIDIA's Nemotron 3 Nano 30B-A3B - two ~30B MoE models activating ~3B parameters that take fundamentally different architectural approaches to the same problem.
A detailed comparison of Qwen3.5-Flash and DeepSeek V3.2 API pricing, benchmarks, and tradeoffs - flat-rate simplicity versus cache-dependent discounts in the budget AI tier.
A data-driven comparison of Qwen3.5-Flash and Gemini 2.5 Flash-Lite - two models at the exact same $0.10/$0.40 per million token price point with 1M context windows but very different performance profiles.
A detailed comparison of Qwen3.5-Flash and GPT-4o mini covering benchmarks, pricing, context windows, and ecosystem - the new open-source challenger versus OpenAI's entrenched budget API.
MiniMax M2.5 is a 230B MoE model (10B active) that scores 80.2% on SWE-Bench Verified while costing 1/10th to 1/20th of frontier competitors like Claude Opus 4.6 and GPT-5.2.
Qwen3.5-122B-A10B is a 122B-parameter MoE model activating 10B parameters per token, narrowing the gap between medium and frontier models with top scores in GPQA Diamond (86.6), MMMU (83.9), and OCRBench (92.1). Apache 2.0 licensed.
Qwen3.5-27B is a 27B dense model that matches GPT-5-mini on SWE-bench (72.4) and posts the best coding and instruction-following scores in the Qwen 3.5 medium lineup. Apache 2.0 licensed.
Qwen3.5-35B-A3B is a 35B-parameter MoE model activating just 3B parameters per token that surpasses the previous Qwen3-235B flagship across language, vision, and agent benchmarks. Apache 2.0 licensed.
Qwen3.5-Flash is Alibaba's hosted production model with 1M context, built-in tools, and multimodal support at $0.10/M input tokens - one of the cheapest frontier-tier APIs available.
Head-to-head comparison of Perplexity and ChatGPT in 2026 - pricing, accuracy, features, and which one to use for search, research, coding, and creative work.
A data-driven comparison of Cursor and Windsurf - pricing, features, benchmarks, and real-world performance for the two leading AI-native code editors in 2026.
