OpenAI unveils GPT-6.1 Sol, offering near‑Astra coding performance at one‑fifth the price in Work and Codex
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| Source: Techmeme | Original article
OpenAI has launched GPT‑6.1 Sol, saying it nearly matches Astra’s agentic coding and professional‑work performance while costing only one‑fifth of Astra’s standard price.
OpenAI has rolled out a new model called GPT‑6.1 Sol, positioning it as a cost‑effective alternative to its flagship GPT‑6 Astra. The company says the upgrade to the earlier GPT‑6 Sol “nearly matches” Astra’s intelligence for agentic coding, computer‑use tasks and professional workflows, while charging only one‑fifth of Astra’s standard input and output token prices. The model is now available in ChatGPT Work, the Codex suite and via the API under the identifier gpt‑6.1‑sol, with an “ultrafast” variant promised for later.
The launch matters because it expands OpenAI’s tiered offering beyond the premium Astra model that underpins its Dots always‑on agents and other high‑end services announced earlier this month. By delivering comparable performance at a fraction of the cost, GPT‑6.1 Sol could make advanced agentic capabilities—such as autonomous code generation, document analysis and workflow automation—more accessible to businesses and developers that balk at Astra’s pricing. The move also aligns with OpenAI’s recent pricing tweaks, including a reopened Pro tier and a shift toward pay‑per‑use API credits, signalling a broader strategy to broaden adoption while preserving premium revenue streams.
What to watch next is whether OpenAI will extend the Sol line with additional variants, how quickly developers integrate it into existing tools, and whether the promised ultrafast version arrives on schedule. Competitors may respond with their own mid‑tier models, and enterprise customers will likely evaluate Sol’s real‑world cost‑benefit against Astra‑powered solutions. As we reported on Sep 29, 2026, Astra‑powered agents are already being deployed in OpenAI’s Dots offering; Sol could soon become the workhorse for less‑price‑sensitive but still high‑performance use cases.
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