AI Model Showdown Reaches New Peaks
In brief
- AI models have been locked in intense competition over the past six months, with the title of "best" switching hands five times between Anthropic, OpenAI, and Google.
- This rapid turnover highlights a dynamic landscape where each company is pushing the boundaries of what these systems can do.
- Simon Willison's recent talk at PyCon US 2026 revealed this fierce competition in large language models (LLMs).
- The November 2025 inflection point marked a turning point, particularly for coding capabilities.
- Willison used an unusual test-generating an SVG of a pelican riding a bicycle-to demonstrate the evolving strengths of each model.
- This quirky challenge is effective because it's nearly impossible to train AI for such absurd tasks, making it a reliable way to gauge progress.
- As this rivalry continues, the focus remains on improving both performance and versatility.
- Whether it's writing code or tackling whimsical creative challenges, the advancements in AI are setting new standards for what we can expect from these systems.
Terms in this brief
- SVG
- Scalable Vector Graphics — a type of image format that uses vectors to create graphics, making them scalable without losing quality. It's often used for creating logos and icons.
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In November 2025, the landscape of large language models (LLMs) underwent a dramatic shift. The top model crown changed hands five times among major providers like Claude Sonnet, GPT-5.1, and Gemini 3. A unique test-drawing a pelican riding a bicycle-helped highlight differences in these models. While most agreed that Anthropic's Claude Opus 4.5 was the best for general tasks, November also marked a breakthrough in coding agents. OpenAI and Anthropic had been refining their models to write better code through reinforcement learning. This effort paid off when coding agents reached a quality threshold where they could be used reliably for real work. The month also saw the first commit to an obscure repository called "Warelay," which later gained traction. From December to January, developers explored new model capabilities and even built ambitious projects like micro-javascript-a JavaScript interpreter in Python using Pyodide and WebAssembly. These developments hint at a future where AI tools become more integrated into everyday workflows, pushing the boundaries of what's possible with LLMs.
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