Reasoning in AI: o1 scores 74.3% on AIME vs GPT-4o’s 12%. How chain-of-thought, GRPO, and hybrid thinking mode work – and when to use them.
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LLM benchmarks explained: MMLU is saturated above 90%, GPQA Diamond still differentiates, AIME has 15-question variance. Four failure modes covered.
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.
Long March 7A exploded 85 seconds after liftoff on August 10, destroying ChinaSat-4B and forcing a YF-100 investigation that threatens Chang’e 7.
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.
GPT-5.6 Sol, Claude Opus 5, and Gemini 3.1 Pro compared on benchmark scores, context window, and price per million tokens.
LLMs predict text one token at a time, trained on 175B+ parameters. How tokenisation, transformers, attention, and RLHF actually work.