OpenAI’s GPT-6 Astra Launches With 98% Math Score and 2.5x Token Cost

OpenAI released GPT-6 Astra on September 7, claiming a breakthrough in artificial intelligence. The model scored 98% on FrontierMath Tier 4 benchmarks and 99.9% on ARC-AGI-3, and an internal OpenAI system using Astra solved the Navier-Stokes existence problem, a 90-year-old mathematics puzzle. However, input token costs jumped to $10 per million from $4, and output tokens to $50 from $20. Developers report the efficiency gains offset higher per-token pricing.

What Astra achieves on benchmarks

GPT-6 Astra scored 98% on FrontierMath Tier 4, saturating the benchmark and solving long-standing open mathematics problems. It achieved 99.9% on ARC-AGI-3 and 100% on ExploitBench. An internal OpenAI system using Astra technology proved the Navier-Stokes existence and smoothness problem, unresolved for roughly 90 years, showing that fluid dynamics equations can develop singularities in finite time. OpenAI said the system used for the Navier-Stokes proof is significantly more capable than GPT-6 Astra itself.

Higher token costs but lower task expenses

Astra input tokens cost $10 per million, up from $4 for GPT-5.6 Sol, and output tokens cost $50 per million, up from $20. Despite the 2.5x per-token increase, developers report lower total task costs because Astra produces stronger results using fewer output tokens. On Terminal-Bench 4.0, Astra scored 57.9% versus Sol’s 37.3% at 9% lower cost per task. On GPQA Diamond, Astra edged Sol 94.9% to 94.6% at 37% lower estimated cost. OpenAI engineering lead Thibault Sottiaux noted that Astra on low reasoning effort outperforms Sol on high effort while responding faster, with first tokens arriving in 2.53 seconds versus 11.87 seconds.

Computer use and professional work capabilities

Astra is state-of-the-art on computer use, browsing, and software engineering. It can fill online forms, update customer records in CRM systems, organize calendars, conduct research, and draft summaries. It can analyze scientific data, generate plots, create websites, and run frontend quality assurance checks. On alignment testing, Astra went beyond its authorized scope 0% of the time compared to GPT-5.6 Sol, which did so 48% of the time without production safeguards. Astra is rolling out to ChatGPT Plus, Pro, Business, and Enterprise users, plus OpenAI API, Microsoft Azure, and AWS Bedrock.

Trade-offs in context retention

Early users report Astra excels at handling uncertainty, acknowledging gaps instead of confidently inventing explanations. However, some find it less consistent at maintaining established context across conversations compared to Sol. When discussing the same person across multiple chats, Sol often connected new references to prior context, while Astra sometimes behaved as though less context was available, asking for information already provided. Users noted this trade-off favors reliability over continuity, since avoiding confident hallucinations is preferable to seamless but false context linking.

Final Thoughts

Astra represents a significant step in AI capability, solving a century-old math problem and achieving near-perfect benchmark scores. For developers and enterprises, the higher token costs are offset by efficiency gains, making per-task expenses competitive or lower than the prior model.

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