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GPT-6.1 Sol vs. GPT-6 Astra: Same accuracy at 18% of the cost

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GPT-6.1 Sol vs. GPT-6 Astra: Same accuracy at 18% of the cost

OpenAI launched GPT-6.1 Sol on September 29, just a week after it launched GPT-6 Sol. OpenAI marketed 6.1 as an upgrade to 6 and as a cheaper near-match for Astra. OpenAI says the new model nearly matches Astra on agentic coding, computer use, and professional work. It also says GPT-6.1 Sol makes fewer factual errors than GPT-6 Sol at low reasoning effort. OpenAI calls it “near-Astra intelligence for a fifth of the price.” OpenAI also halved cached-input pricing, from GPT-6 Sol’s $0.20 to $0.10.

GPT-6.1 Sol costs $2 per million input tokens and $10 per million output tokens, with cached input at $0.10. GPT-6 Astra costs $10 per million input tokens, $50 per million output tokens, and $1 per million cached input tokens. That puts GPT-6.1 Sol at one-fifth of Astra’s price for input and output tokens, and one-tenth for cached input. 

These claims are a lot for my skeptical self to take in. Does the price gap hold up once real token use is measured? And how close does GPT-6.1 Sol actually come to Astra’s intelligence? To find out, I ran both models through the same three tests I ran on GPT-6 Sol and Opus 5.5. 

I called both models through the OpenAI Responses API with identical prompts, reasoning effort set to max, and a 64,000-token output limit. These are the same settings I used for GPT-6 Sol. 1 Sol.



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