GPT-6 솔·루나, 가격 절반 낮추고 성능은 소폭 향상

OpenAI's GPT-6 Sol and Luna cut prices in half but barely move the needle on performance Matthias Bastian View the LinkedIn Profile of Matthias Bastian Sep 22, 2026 Nano Banana Pro prompted by THE DECODER Ask about this article… Search With GPT-6 Sol and Luna, OpenAI adds two cheaper models to its lineup that match their predecessors' performance at half the token price and aim to rival some of Anthropic's more expensive models. The biggest change is a 50 percent price cut compared with GPT-5.6 Sol and Luna. GPT-6 Sol now costs $2 per million input tokens and $10 per million output tokens, while Luna comes in at $0.10 for input and $0.50 for output. OpenAI attributes the lower prices to improvements in caching and inference, saying it's passing those savings directly to users. That puts its pricing in the same range as cheaper open-weight models. Terra, previously the cheapest model in the lineup, is no longer available. Ad Model Input Output GPT-6 Sol vs. GPT-5.6 Sol $4 → $2 $20 → $10 GPT-6 Luna vs. GPT-5.6 Luna $0.20 → $0.10 $1.20 → $0.50 According to OpenAI, Sol is designed for recurring complex tasks such as building new features, reviewing code, debugging, and analyzing data. Luna is meant to handle large volumes of well-defined tasks at low cost, like summarizing documents, extracting information, and answering short questions. Ad Along with cutting token prices, OpenAI says it has improved prompt caching for GPT-6, offering a 90 percent discount on cached input tokens. A new prompt caching dashboard and diagnostics tool are meant to help developers optimize cache usage, and developers can now change reasoning effort and tool availability without invalidating the cache. At launch, both models are available in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise, and Edu subscribers. Free and Go users get access to Luna through the desktop app, but neither model is initially available in regular chat. The API offers them as gpt-6-sol and gpt-6-luna , while access in ChatGPT is rolling out gradually. Ad GPT-6 Sol and Luna's benchmark advantage comes down to cost OpenAI is positioning the new models primarily on price-to-performance compared with Anthropic's Claude lineup. On OSWorld 2.0, which tests computer use, OpenAI says GPT-6 delivers results similar to Claude Opus 5 at roughly 80 percent lower cost, though Astra still leads in this category. On AutomationBench, which tests business workflows across 47 tools, GPT-6 Sol at its highest effort level reportedly beats Claude Opus 5 at maximum effort. OpenAI puts Sol's cost per task at just 9 percent of Opus's. Luna, meanwhile, improves on its predecessor by 5.4 percentage points while costing 58 percent less. Ad For coding, OpenAI provides results from two benchmarks. FrontierCode 1.1 tests whether AI agents produce code that can actually be integrated into an existing codebase, checking test quality, code style, and compliance with requirements. GPT-6 Sol scores 49.3 percent at maximum effort for $2.14 per task, putting it roughly on par with Claude Fable 5.1, which scores 50.3 percent at maximum effort but costs six times as much at $12.83. Claude Opus 5 reaches 53.4 percent for $4.31 at medium effort, the setting that produced its best result on this test. Ad OpenAI's reasoning levels are getting out of hand On DeepSWE v1.1, a benchmark for demanding software engineering tasks over long stretches in real codebases, OpenAI reports 68.8 percent for GPT-6 Sol at maximum effort. That's within 1.1 percentage points of Claude Fable 5's best score of 69.9 percent at "xhigh," while Fable at "max" hits 69.7 percent for $21.63 per task. Of course, Sol isn't chasing the frontier here. Claude Opus 5 reaches 73.7 percent at maximum effort for $11.84 per task, and OpenAI's own GPT-5.6 Sol scores 72.7 percent for $6.46. The point of GPT-6 Sol is to land close to those numbers for a fraction of the price. At "xhigh," it delivers 66.6 percent for $1.00 per task. Luna is cheaper still, matching that score at maximum effort for just $0.22. OpenAI says Luna's result is comparable to Claude Opus 5 and Claude Fable 5 at medium effort, while costing 93 percent less than Opus and 96 percent less than Fable. The DeepSWE results also turn the choice between OpenAI's own models into a headache. Luna at maximum effort matches Sol at "xhigh" while costing 78 percent less. Cranking Sol up to maximum effort only gets you to 68.8 percent, a mere 2.2 percentage points above Luna. Who's supposed to sort through all this in real-world use? Overall, OpenAI's benchmark selection looks cherry-picked. Metrics like GDPval for knowledge work or Terminal-Bench 4.0 for agentic coding are missing, even though they're part of the usual lineup. OpenAI also appears to have missed the launch of Opus 5.5 , which is potentially up to 40 percent cheaper than Opus 5 with significantly better performance. Independent analysis sees little actual progress According to Artificial Analysis , GPT-6 Sol and Luna cut per-task costs in half compared to their predecessors, but intelligence scores stay at GPT-5.6 levels, with gains in some evaluations and regressions in others. On the coding agent index, Sol improves by 2 points while Luna drops by 2, according to the analysis. Artificial Analysis also found regressions on two key knowledge-work benchmarks. On GDPval-AA v2.1, which tests computer-based knowledge work across 44 professional fields, Sol loses about 100 Elo points and Luna drops about 75. Manual inspection traced the regressions mainly to lower presentation quality and incomplete results. Artificial Analysis has faced criticism before , though, when it rated OpenAI's Astra model too low because of outdated benchmarks. Two benchmark updates followed, after which Astra was back on top. We'll see what happens this time. Either way, it's very much clear that OpenAI is betting heavily on price with GPT-6 Sol and Luna. Now the models need to prove themselves in day-to-day work, since benchmarks only tell part of the story. That may also explain why OpenAI left some of them out. Combined with the different reasoning levels and results that sometimes look tuned for benchmarks, whether intentionally or not, the whole benchmarking game seems more ridiculous with each new model launch. AI News Without the Hype – Curated by Humans Subscribe to THE DECODER for ad-free reading, a weekly AI newsletter, our exclusive "AI Radar" frontier report six times a year, full archive access, and access to our comment section. Subscribe now Source: OpenAI