Tutorials, deep dives and product notes — built for developers.
Every AI model claims a 1M-token context window. But only GPT-5.5 and Claude Opus 4.6 actually use it. We analyzed MRCR v2, NIAH-2, and Graphwalks to show the 60-point gap between the best and worst "1M-capable" models — and which one to trust for long-context coding.
Which frontier AI model tells the truth? 🆕 Claude Fable 5 debuts at #1 on AA-Omniscience (40, 61% accuracy) but with accuracy-driven strategy — higher hallucination than Opus 4.8. GPT-5.4 Mini leads Vectara (5.5%). The reasoning paradox: thinking mode amplifies hallucination 2-3×. Full 19-model ranking.
Can an MIT-licensed open-weight model beat OpenAI's proprietary GPT-5.4? DeepSeek V4 Pro Max does on SWE-bench — at 4.3× lower cost. Full benchmark and pricing comparison.
DeepSeek V4 Pro Max ($0.87/1M, MIT, 1.6T/49B) vs GLM 5.1 ($3.08/1M, MIT, 754B/40B). GLM leads SWE-bench Pro (58.4% vs 55.4%) & HLE w/tools. V4 Pro Max dominates 12/14 benchmarks. 3.5× price gap, 5× context gap. Updated June 9, 2026.
Head-to-head: DeepSeek V4 Pro Max vs Kimi K2.6. Both MIT-licensed, both 80%+ SWE-bench. Which open-weight coding model wins on benchmarks, price, and real-world use?