LLM benchmarks explained: MMLU is saturated above 90%, GPQA Diamond still differentiates, AIME has 15-question variance. Four failure modes covered.
Large Language Models
LLM releases, model benchmarks, and capability comparisons — explained in plain language for readers tracking AI's fastest-moving frontier.
LLM context windows are the model’s working memory per request. Larger windows add cost and the lost in the middle problem. Token limits and RAG explained.
Open source vs closed LLMs: the benchmark gap closed in 2026. Data privacy, cost, fine-tuning, EU AI Act, and infrastructure capacity still decide.
Best LLMs for coding in 2026 ranked by 5 developer tasks. Claude Opus 4.8 leads SWE-bench Pro at 69.2%. Open-weight now at 80.6%.
LLM hallucination, or confabulation per NIST, ranges 1% to 60%+ by task. Courts have documented 120+ fake AI citation cases.
DeepSeek V4 Pro scores 80.6% on SWE-bench, verified at $0.87 per million output tokens, versus GPT-5.6 Sol’s $30 and 91.9% Terminal-Bench score.
LLMs predict text one token at a time, trained on 175B+ parameters. How tokenisation, transformers, attention, and RLHF actually work.