The November 2025 inflection point marked a watershed moment for large language models, particularly in coding capabilities. Model leadership shifted rapidly among major providers, with Claude Opus 4.5 ultimately securing dominance after a brief rotation that included GPT-5.1 and Gemini 3. The real breakthrough wasn't model rankings, but coding agents crossing into "mostly-work" territory.

Coding agents evolved from experimental tools to daily drivers thanks to Reinforcement Learning from Verifiable Rewards. OpenAI's Codex and Anthropic's Claude Code harnesses enabled developers to accomplish real work without constant oversight. This shift unlocked a wave of ambitious projects during the December-January break, though many proved impractical despite their technical novelty.

The holiday period also birthed OpenClaw, emerging from the Warelay repository to become the defining personal AI assistant platform. Mac Minis sold out across Silicon Valley as developers rushed to host their own "Claws." Recent model releases include Google's capable open-weight Gemma 4 series and GLM's massive 1.5TB open-weight model from China.

The author's unconventional benchmark—SVG pelicans riding bicycles—reveals meaningful differences between frontier models. While whimsical, this test exposes genuine capability gaps that traditional benchmarks miss, proving that AI advancement isn't just about raw performance metrics.