GPT-6 Astra vs GPT-5.6 Sol: when the 2x upgrade pays for itself

Direct answer

Astra lists at $10/$50 versus Sol's $5/$30, both with 1,050,000-token windows. OpenAI's long-context benchmarks show where the upgrade is worth it — and where it is not. This guide is written for Australian product and platform teams comparing model quality, spend, routing policy, and production rollout risk.

The facts side by side

Both models are in the NextModel catalog with 1,050,000-token context windows. GPT-5.6 Sol is listed at $5/$30 per million tokens; GPT-6 Astra at $10/$50, doubling to $20/$75 above 272K prompt tokens. Astra adds Computer Use as a catalog capability and supports the same chat and Responses APIs.

Where Astra pulls ahead

OpenAI's published MRCR v2 8-needle results put Astra at 100% retrieval in the 256K-512K range versus 91.5% for Sol, and 96.3% in the 512K-1M range where Sol falls off sharply. If your workload lives past 256K tokens — whole-service logs, large document sets, long agent histories — that retrieval gap is the product.

Where Sol stays the right answer

For prompts under 100K tokens, most teams will not notice a retrieval difference, and Sol lists at half the price. Steady-state classification, extraction, and routine chat rarely justify 2x input and output rates. Community chatter also expects a cheaper Sol-class model on the GPT-6 base later; nothing is announced.

Our take

Route by prompt size, not by hype: keep Sol as the default leg, escalate to Astra when prompts are long or the task is agentic computer use. On NextModel both sit behind one base URL, so that is a routing policy, not a rewrite.

What is unverified

MRCR figures are OpenAI's published evaluations, not first-party runs. There is no first-party CacheSafety row for either model. Price and context figures come from the NextModel catalog listing.

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