The article announces the release of llm‑gemini 0.35, adding support for the new Gemini 4 Argon model available through the Gemini API and for customers on the $100/month subscription plan. It highlights Argon’s pricing—$2/million input and $10/million output at launch, with a planned price increase on December 1st—and compares its cost-effectiveness to other models, noting it remains cheaper than many competitors even after the increase. The post also showcases example SVG outputs and creative illustrations generated by Argon, such as a pelican riding a bicycle and an armadillo in fishnet tights on Mars.
Simon Willison announces the release of Kimi K3.1 Pro on the Kimi API, noting it is not yet open‑weights but full weights will be available by Nov 4 2026. The model offers a high‑speed variant 2‑3× faster at double the price, with US dollar pricing of $2.00/million input tokens and $10/million output tokens—matching GPT‑6.1 Sol and half the cost of Claude Opus 5.5. Benchmarks from Moonshot show Kimi roughly on par with Opus 5.5 and GPT‑6 Astra, scoring 55 in Artificial Analysis, ahead of several competitors.
Simon Willison tested GPT‑6 Astra by generating SVG pelicans riding bicycles at various reasoning levels and compared the results to GPT‑5.6 Sol, Terra, and Luna. The Astra pelicans consistently outperformed the other models, especially at low and xhigh reasoning levels, and even the Astra max version produced high‑quality images. Astra also used fewer tokens and was roughly twice as expensive as Sol, yet its low‑level output was cheaper and superior to any Sol model.
Simon Willison comments on GPT 6.1‑Sol, describing it as "Near‑Astra intelligence for a fifth of the price." He notes that the model’s pelican illustrations are similar to those of the GPT‑6 family and provides links to the live‑blog of the keynote and to the pelican images. The post is tagged with AI, OpenAI, generative‑AI, LLMs, and playful references to pelican‑riding‑a‑bicycle.
The release of llm-gemini 0.34 introduces the new Gemini 3.8‑Flash model, available in low, medium, and high thinking levels, and fixes an issue where async responses failed to record the resolved model version. The update also notes that Google has released Gemini 3.8‑Flash (and a restricted 3.8 Flash Cyber version) today, with example outputs (pelicans) demonstrating the model’s performance across the different thinking levels. The author highlights Gemini Flash’s speed, low cost, and competence in generating HTML, JavaScript, and Markdown‑SVG content, citing a 13‑second, 1.8‑cent example of an HTML output.
The article discusses how large language model (LLM) performance varies across many dimensions, noting that a single factor—model size—explains much of the variation. Larger models tend to be more reliable, cost more per token, and recall more obscure facts. It also explains OpenAI’s naming convention, where version numbers indicate release order, relatedness to other models, and the scale of improvement, using examples like Luna, Terra, Sol, and Astra.