How is Gemini 3.1 at the top of SWE-bench?

<!-- SC_OFF --><div class="md"><p>Figured this out by running 4 models: Claude Opus 4.6, GPT-5.4, Gemini 3.1 Pro, and Grok 4.20, on a benchmark of 1,417 binary forecasting

<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"><blockquote> <p><strong>WARNING: wait before download from HF: I just realised my upload of the new versions with the additional fix in the chat

<table> <tr><td> <a href="https://www.reddit.com/r/ChatGPT/comments/1szv149/every_24_months_the_price_of_a_given_ai/"> <img alt="Every 2-4 months, the price of a given AI

<!-- SC_OFF --><div class="md"><p>So many things happened in just 7 days:</p> <ul> <li>OpenAI rolled out GPT-6 to more than 1.2 billion ChatGPT users. Answers now come with

<!-- SC_OFF --><div class="md"><p>After burning way too much money on GPT-4 API calls, I finally went all-in on local models with Ollama. The progress in 2026 is insane

<table> <tr><td> <a href="https://www.reddit.com/r/singularity/comments/1s3pbl6/human_vs_ai_performance_on_arcagi_3_as_a_function/"> <img alt="Human vs. AI performance on ARC-AGI

<!-- SC_OFF --><div class="md"><p>One thing we’ve been noticing lately is that a surprisingly large percentage of day-to-day AI workflows no longer seem to require frontier-scale