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Simon Willison tests Mistral Large 4 alongside frontier models (Claude Opus 5.5, GPT-6.1, Gemini 3.8 Flash) by generating an SVG of an armadillo in fishnet tights jaywalking on Mars, highlighting how saturated benchmarks push toward absurd creative prompts.
An experiment compared two AI tools, Opus 5.5 and Sol 6.1, in creating an SVG of the Mona Lisa without reference images, evaluating their artistic output and thinking levels.
A tweet compares GPT-6 Astra and Anthropic’s new Mythos model on the Pelican SVG test, with a link to an external source.
RULER introduces instance-aware rubric rewards for SVG generation, using a vision-language judge to optimize reinforcement learning and significantly improve performance over previous methods.
A comparison benchmark between Gemini 4 Pro and Gemini 3.8 Flash AI models, tested on generating an SVG of a pelican riding a bicycle.
A Reddit post highlights Astra's impressive capabilities with SVG, suggesting it has been underrated amidst current events.
This article compares the performance of various AI models in generating SVGs based on specific prompts for the years 2025 and 2026, detailing their output quality, time taken, and cost.
Fusion, a new AI model from a Singapore lab, demonstrates strong performance in SVG generation with accurate structural connections and significant cost savings compared to Opus 5 from Claude.
GitHub has released its star history API, and developer Lee Reilly built Star Chart using Copilot CLI to generate self-updating star history charts for GitHub repositories.
SVG-Score introduces a human-aligned evaluation framework for text-to-SVG generation, addressing limitations of current metrics like CLIPScore by developing specialized evaluators and benchmarks.
Anthropic released Claude Fable 5.1, claiming improvements in coding, knowledge work, and scientific research benchmarks, while Simon Willison evaluates its performance using a custom pelican SVG generation benchmark.
The article benchmarks local LLMs running on 128GB Apple Silicon for generating SVG pelicans, comparing Qwen3.8 variants and DeepSeek V4 with quantizations by Unsloth.
Chart2SVG introduces a multimodal large language model that converts raster chart images into editable SVG code, enabling downstream tasks like data exploration and chart repurposing with improved reconstruction fidelity.
A user tested the Qwen3.8 27B model on SVG generation with a complex prompt and found the results exceeded expectations, highlighting the model's capability in creative tasks.
A new benchmark compares the performance of Qwen3.8-27b, Sol 5.6, and Qwen3.6-35B models on generating an SVG of a horse on a bicycle in the desert with a camel background, using a typo-included prompt.
This article compares the performance of local AI models Qwen3.8-27B, Muse Glimmer 30B, and Gemma 4 26B A4B on SVG generation, using Gemini 3.7 Flash as a cloud control, and highlights differences in output quality.
The title refers to using the Qwen 3.8 27b AI model to generate an animated SVG of a ship from One Piece in an ocean scene, with an estimated time of 7 minutes.
A personal AI benchmark asking models to generate an SVG of a frog with a Habsburg jaw reveals that the model adds elaborate editorializing, inventing royal status and medical commentary beyond the literal request.
An analysis investigates whether AI labs are optimizing their models for the popular 'pelican riding a bicycle' SVG benchmark, testing seven frontier models across 48 prompts with varied animals and vehicles, finding no strong evidence of overfitting.
Evaluation of Qwen 3.6 35B A3B Q8_0 model's SVG generation capability by testing with varying expert counts on the prompt 'Create an SVG of a Darth Vader.'