What makes LLMs from the same provider different ?

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1t47qbw/deepseek_v4_pro_matches_gpt52_on_foodtruck_bench/"> <img alt="DeepSeek V4 Pro matches GPT-5.2 on

<!-- SC_OFF --><div class="md"><p>Hey <a href="/r/LocalLLaMA">r/LocalLLaMA</a>,</p> <p>We’re living through something wild: “intelligence density” / capability density is scaling

<!-- SC_OFF --><div class="md"><p>i give the same prompt to the same model, but the output generated by the LLM is always different. why is it so ?</p> </div><!-- SC_ON --> &#32;

<!-- SC_OFF --><div class="md"><p>Ive been trying to understand but I’m not really getting what makes a frontier model different from non frontier models and why is the AI space

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1sufmrp/the_eu_and_the_white_house_published_documents/"> <img alt="The EU and the White House

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s59rct/rys_part_3_llms_think_in_geometry_not_language/"> <img alt="RYS Part 3: LLMs think in geometry, not

<!-- SC_OFF --><div class="md"><p>I’ve been saying this for quite some time now and this paper that came out recently really puts it clearly</p> <p><a

<!-- SC_OFF --><div class="md"><p>Something I keep noticing is that LLMs can give really strong, coherent answers in a single prompt, but as soon as the conversation goes on for a