The Pelican comparison grid for Astra is pretty interesting

OpenAI has released GPT-6 Astra, a new AI model variant that shows measurable improvements in visual generation capabilities compared to previous versions. Early testing reveals notable differences in output quality when processing the same prompts across multiple reasoning intensity levels, providing valuable insights into how the model's enhanced reasoning architecture affects creative visual tasks.

A comparative evaluation of GPT-6 Astra against earlier OpenAI models—including GPT-5.6 Sol, Terra, and Luna—was conducted using SVG generation tasks at varying reasoning levels. The testing specifically utilized pelican bicycle imagery as test subjects, rendered across five distinct reasoning intensity settings: low, medium, high, xhigh, and max. Notably, GPT-6 Astra does not support a reasoning=none setting, indicating the model fundamentally integrates reasoning into all outputs. The resulting comparison grid demonstrates clear quality differentiation between Astra and its predecessors, with performance improvements visible across multiple reasoning tiers.

• Astra's mandatory reasoning integration suggests OpenAI is prioritizing thoughtful, deliberate processing over raw speed in creative tasks
• Visual generation quality improvements across all reasoning levels indicate stronger baseline capabilities compared to Sol, Terra, and Luna variants
• The tiered reasoning approach allows users to balance output quality against computational resource requirements
• SVG generation performance improvements have potential applications in design automation, technical illustration, and programmatic graphics creation
• The removal of reasoning=none option may impact use cases requiring minimal computational overhead

The performance improvements demonstrated by GPT-6 Astra highlight the ongoing evolution of multimodal AI capabilities. As organizations increasingly integrate AI into creative and technical workflows, models that deliver consistent quality improvements across reasoning levels address practical deployment challenges. The comparison with earlier variants provides concrete evidence of progress in the Astra lineup, particularly for specialized tasks like structured image generation. These developments suggest that future AI implementations will likely prioritize reasoning-integrated approaches for quality-sensitive applications, establishing new benchmarks for what constitutes acceptable AI-generated visual content across various industries.