GPT-6 Astra Outperforms GPT-5.6 in SVG Generation

GPT-6 Astra demonstrates substantial improvements over GPT-5.6 across all reasoning levels when tested on SVG generation tasks.

Performance Across Reasoning Tiers

A comparison test using pelican-on-bicycle SVG generation reveals clear quality differences between GPT-6 Astra and GPT-5.6 models. The test evaluated output at low, medium, high, xhigh, and max reasoning levels - Astra does not support a reasoning=none mode.

According to the comparison, Astra’s lowest reasoning tier produces better results than any GPT-5.6 output, including GPT-5.6 Sol at its highest settings. While the best GPT-5.6 Sol pelican at xhigh reasoning still resembles abstract shapes, every Astra tier from low through xhigh generates more recognizable imagery.

The max reasoning level for Astra produces particularly strong results, though one limitation persists below the max tier: the model does not reliably position pelican legs on both sides of the bicycle frame. This suggests that even mid-tier Astra reasoning handles complex spatial relationships better than previous-generation models at their peak, but full anatomical accuracy requires the highest reasoning setting.

Architecture and Token Efficiency

Astra demonstrates improved token efficiency compared to its predecessors. Despite processing fewer tokens at each reasoning level, the model achieves superior output quality. This efficiency gain partially offsets the higher per-token pricing.

The comparison grid methodology involved generating SVGs with GPT-6 Nova (the specific Astra variant tested), then rendering those outputs alongside GPT-5.6 results from Sol, Terra, and Luna models. This side-by-side evaluation made quality differences immediately apparent across the reasoning spectrum.

Pricing Structure

Astra costs approximately twice as much as Sol on a per-token basis: $10 per million input tokens and $50 per million output tokens, compared to Sol’s $5 input and $30 output rates. However, the reduced token consumption at each reasoning level brings actual costs closer together than the raw pricing suggests.

For tasks requiring high-quality output, developers may find Astra’s combination of better results and lower token usage makes it cost-competitive with running GPT-5.6 models at maximum reasoning levels. The pricing structure rewards Astra’s efficiency gains rather than penalizing users for the architectural improvements.

Comparison with GPT-5.6 Variants

The test compared Astra against three GPT-5.6 models: Sol, Terra, and Luna. Sol served as the primary benchmark, with its xhigh reasoning output representing the best GPT-5.6 could achieve on the pelican task. Even this top-tier previous-generation result fell short of Astra’s low reasoning output.

This performance gap suggests meaningful architectural advances rather than incremental improvements. The consistent quality advantage across all Astra reasoning tiers indicates the model handles spatial reasoning, object composition, and SVG syntax more effectively than GPT-5.6 variants.

Developers working with structured output formats like SVG, or tasks requiring precise spatial relationships, may find Astra’s improvements justify the higher base cost. The comparison grid approach - generating identical prompts across models and reasoning levels - provides a reproducible method for evaluating these tradeoffs on specific use cases.