GPT-6 Astra Shows Strong Tool Use and Math Results but Trails in Text Quality Tests
OpenAI introduced GPT-6 Astra on 3 September 2026 as a model designed for extended tasks that require maintaining goals, using tools and correcting intermediate errors across code, documents and computer interfaces.
In early demonstrations the model controlled a pixel display on a Divoom Bluetooth speaker, configured a CRM system through a browser and built an editable three-dimensional house scene in Blender before transferring it to Unreal Engine 5.
Independent evaluations released by 7 September present a more nuanced view. Artificial Analysis Intelligence Index 4.2 placed Astra second behind Claude Fable 5.1, while Epoch AI Epoch Capabilities Index ranked it first at 169 points with a 165–174 confidence interval.
On coding benchmarks Astra led Kilo Terminal-Bench 2.0 with 79.3 percent tasks completed yet used roughly three times fewer tokens than GPT-5.6 Sol in Codex evaluations. In Real Python checks it correctly identified a non-existent function and performed an 11-line edit for $0.31 across five tasks.
Text quality remains a weaker area. On ToneBench Astra scored 1995 Elo in max mode, placing 30th and behind both Sol and Fable configurations. The model performed better at avoiding boilerplate language than at crafting strong openings.
Document and fact-handling tests showed progress. Astra achieved 33.2 percent fully completed tasks on GDP.pdf compared with 28.2 percent for Sol, and reduced hallucination rates on AA-Omniscience from 92 percent to 51 percent.
In mathematics FrontierMath Erdős saw Astra solve two previously open problems on a single attempt each, requiring 15–16 hours and roughly $230 per solution. Additional experiments solved five tasks under modified conditions at a total compute cost exceeding $220 000.
On ARC-AGI-3 the model reached 99.9 percent when using an OpenAI adapter that preserves reasoning state between calls, versus 62.7 percent in the standard configuration.
OpenAI classified Astra as Critical under its Preparedness Framework after the model scored 100 percent on ExploitBench and identified two previously unknown vulnerabilities during testing. The company also noted reduced ability to detect undesirable behaviour from reasoning traces and that asynchronous monitoring may allow actions to complete before intervention.
API pricing lists $10 per million input tokens and $50 per million output tokens for short context, with cache reads at $1 per million. Costs for completed work remain highly variable depending on environment, retries and human oversight required.
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