GPT-6 Sol, Luna and 6.1 Sol Pricing and Availability
OpenAI released GPT-6 Sol and Luna on 22 September, then GPT-6.1 Sol on 29 September. Both Sol versions cost $2 per million input tokens and $10 per million output tokens at standard API rates, while 6.1 halves cached reads to $0.10. Luna costs $0.10 input and $0.50 output. Long-context rates and tool compatibility still matter.
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GPT-6 Sol and Luna pricing resets the lower tiers
OpenAI released GPT-6 Sol and GPT-6 Luna on September 22 as less expensive companions to GPT-6 Astra. The standard short-context API rate for Sol is $2 per million input tokens and $10 per million output tokens. Luna costs $0.10 for input and $0.50 for output. OpenAI presents both as roughly half the promotional price of their GPT-5.6 predecessors.
Sol's previous $4 input and $20 output rates were halved. Luna's output fell from $1.20 to $0.50, a slightly larger cut. The final bill still depends on reasoning length, tool calls, retries, and cache hits.
The 1.05 million-token context has a higher rate
Both models accept text and image input, provide text output, and expose functions, web search, file search, and computer use through the API. OpenAI lists a 1.05 million-token context window and a 128,000-token maximum output for each. Sol is positioned for complex coding and agent workflows, while Luna is aimed at focused, high-volume work.
Requests that use the long-context tier cost more. OpenAI's pricing table lists Sol at $4 input and $15 output per million tokens, and Luna at $0.20 input and $0.75 output. Regional processing adds 10 percent where available. EU data residency for these models is limited to Standard processing at launch. An application that regularly sends very large histories therefore needs to model the long-context rate instead of multiplying the headline price by its token count.
OpenAI says GPT-6 preserves cache reuse when developers change reasoning effort or toggle tools. This can reduce repeated-context costs, but the saving depends on an application's actual cache hits.
Access starts in Codex and ChatGPT Work, not regular Chat
Sol and Luna are available through the API and are rolling out in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise, and Edu accounts. Free and Go users can access Luna in the desktop app. OpenAI says neither model is available in regular Chat at launch, and the paid rollout may take time to reach every account.
Developers should also check API compatibility before swapping model IDs. OpenAI recommends the Responses API. In Chat Completions, Sol and Luna support function calling only when reasoning effort is set to none. A migration that keeps a higher reasoning setting while relying on Chat Completions tools can therefore fail even though the model itself is available.
GPT-6.1 Sol changes the cached-input calculation
OpenAI released GPT-6.1 Sol on 29 September, one week after Sol and Luna. Its standard API input and output prices remain $2 and $10 per million tokens, but cached input reads fall from Sol's $0.20 to $0.10. OpenAI claims the new model approaches Astra on coding, computer use, and professional work at one-fifth of Astra's standard token rates. That is a price comparison and a vendor evaluation, not a measured saving for every task.
The model ID is gpt-6.1-sol. It supports a 1.05 million-token context and a 128,000-token maximum output. Its model documentation says tool calling requires the Responses API; Chat Completions is supported without tools. It also does not support none or minimal reasoning effort, so a Sol workflow that uses either setting needs a deliberate migration. The launch covers the API, ChatGPT Work, and Codex for eligible paid plans, but not regular Chat.
OpenAI's system card classifies 6.1 Sol as Critical capability in cybersecurity, above the High classification assigned to Sol and Luna. The company says it therefore applies the Astra safeguards stack. The classification describes OpenAI's own assessment and controls, not an independent assurance about a deployment. Buyers should test task completion and total cost under their own tool permissions before replacing Sol.
Lower prices do not settle quality or safety
OpenAI reports gains in coding, factuality, computer use, and alignment, but most launch comparisons were produced or selected by OpenAI. TechCrunch noted that the announcement repeatedly compares the models with Anthropic systems. Anthropic released Claude Opus 5.5 about 90 minutes earlier, making the day as much a pricing contest as a clean independent capability test.
The original Sol and Luna system card also contains limits that the launch charts compress. OpenAI classifies those two models as High capability in cybersecurity and biological or chemical domains, but below its Critical thresholds. On HealthBench and HealthBench Hard, some GPT-6 scores regress against their predecessors as the models produce shorter answers. In internal Codex simulations, Sol generated fewer serious misalignment flags overall, while exfiltration flags increased. OpenAI cautions that those simulations are not direct measurements of external deployment safety.
For an existing Sol integration, test 6.1 when cached prompts or complex tasks dominate the bill. Keep Sol as a baseline until output, total cost, and tool calls have been checked on representative work.