{ "archive_path": "archive/1790114708.518503", "base_url": "openai.com/index/introducing-gpt-6-sol-and-luna", "basename": "", "bookmarked_date": "2026-09-22 22:05", "canonical": { "archive_org_path": "https://web.archive.org/web/openai.com/index/introducing-gpt-6-sol-and-luna", "dom_path": "output.html", "favicon_path": "favicon.ico", "git_path": "git/", "google_favicon_path": "https://www.google.com/s2/favicons?domain=openai.com", "headers_path": "headers.json", "htmltotext_path": "htmltotext.txt", "index_path": "index.html", "media_path": "media/", "mercury_path": "mercury/content.html", "pdf_path": "output.pdf", "readability_path": "readability/content.html", "screenshot_path": "screenshot.png", "singlefile_path": "singlefile.html", "warc_path": "warc/", "wget_path": null }, "domain": "openai.com", "extension": "", "hash": "KW6SRSHNAXT3PGKVG3V8", "history": { "archive_org": [], "dom": [], "favicon": [ { "cmd": [ "curl", "--silent", "--location", "--compressed", "--max-time", "120", "--output", "favicon.ico", "--user-agent", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) curl/curl 8.14.1 (x86_64-pc-linux-gnu)", "https://www.google.com/s2/favicons?domain=openai.com" ], "cmd_version": "curl 8.14.1 (x86_64-pc-linux-gnu)", "end_ts": "2026-09-24T01:19:10.852433+00:00", "index_texts": null, "output": "favicon.ico", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-24T01:19:10.367486+00:00", "status": "succeeded" }, { "cmd": [ "curl", "--silent", "--location", "--compressed", "--max-time", "120", "--output", "favicon.ico", "--user-agent", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) curl/curl 8.14.1 (x86_64-pc-linux-gnu)", "https://www.google.com/s2/favicons?domain=openai.com" ], "cmd_version": "curl 8.14.1 (x86_64-pc-linux-gnu)", "end_ts": "2026-09-23T19:19:24.607710+00:00", "index_texts": null, "output": "favicon.ico", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-23T19:19:23.991490+00:00", "status": "succeeded" }, { "cmd": [ "curl", "--silent", "--location", "--compressed", "--max-time", "120", "--output", "favicon.ico", "--user-agent", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) curl/curl 8.14.1 (x86_64-pc-linux-gnu)", "https://www.google.com/s2/favicons?domain=openai.com" ], "cmd_version": "curl 8.14.1 (x86_64-pc-linux-gnu)", "end_ts": "2026-09-22T22:06:10.706515+00:00", "index_texts": null, "output": "favicon.ico", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-22T22:06:10.219885+00:00", "status": "succeeded" }, { "cmd": [ "curl", "--silent", "--location", "--compressed", "--max-time", "120", "--output", "favicon.ico", "--user-agent", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) curl/curl 8.14.1 (x86_64-pc-linux-gnu)", "https://www.google.com/s2/favicons?domain=openai.com" ], "cmd_version": "curl 8.14.1 (x86_64-pc-linux-gnu)", "end_ts": "2026-09-24T04:16:48.596340+00:00", "index_texts": null, "output": "favicon.ico", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-24T04:16:48.207951+00:00", "status": "succeeded" } ], "git": [], "headers": [ { "cmd": [ "curl", "--silent", "--location", "--compressed", "--head", "--max-time", "120", "--user-agent", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) curl/curl 8.14.1 (x86_64-pc-linux-gnu)", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "curl 8.14.1 (x86_64-pc-linux-gnu)", "end_ts": "2026-09-24T01:19:12.108224+00:00", "index_texts": null, "output": "headers.json", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-24T01:19:10.861721+00:00", "status": "succeeded" }, { "cmd": [ "curl", "--silent", "--location", "--compressed", "--head", "--max-time", "120", "--user-agent", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) curl/curl 8.14.1 (x86_64-pc-linux-gnu)", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "curl 8.14.1 (x86_64-pc-linux-gnu)", "end_ts": "2026-09-23T19:19:25.322109+00:00", "index_texts": null, "output": "headers.json", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-23T19:19:24.615405+00:00", "status": "succeeded" }, { "cmd": [ "curl", "--silent", "--location", "--compressed", "--head", "--max-time", "120", "--user-agent", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) curl/curl 8.14.1 (x86_64-pc-linux-gnu)", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "curl 8.14.1 (x86_64-pc-linux-gnu)", "end_ts": "2026-09-22T22:06:11.263356+00:00", "index_texts": null, "output": "headers.json", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-22T22:06:10.715433+00:00", "status": "succeeded" }, { "cmd": [ "curl", "--silent", "--location", "--compressed", "--head", "--max-time", "120", "--user-agent", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) curl/curl 8.14.1 (x86_64-pc-linux-gnu)", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "curl 8.14.1 (x86_64-pc-linux-gnu)", "end_ts": "2026-09-24T04:16:49.033529+00:00", "index_texts": null, "output": "headers.json", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-24T04:16:48.605832+00:00", "status": "succeeded" } ], "htmltotext": [], "media": [], "mercury": [ { "cmd": [ "/app/node_modules/@postlight/parser/cli.js", "https://openai.com/index/introducing-gpt-6-sol-and-luna/", "--format=text" ], "cmd_version": "1.0.0", "end_ts": "2026-09-24T01:19:18.993990+00:00", "index_texts": null, "output": "ArchiveError: Mercury was not able to get article text from the URL", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-24T01:19:17.356839+00:00", "status": "failed" }, { "cmd": [ "/app/node_modules/@postlight/parser/cli.js", "https://openai.com/index/introducing-gpt-6-sol-and-luna/", "--format=text" ], "cmd_version": "1.0.0", "end_ts": "2026-09-23T19:19:31.469449+00:00", "index_texts": null, "output": "ArchiveError: Mercury was not able to get article text from the URL", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-23T19:19:30.156801+00:00", "status": "failed" }, { "cmd": [ "/app/node_modules/@postlight/parser/cli.js", "https://openai.com/index/introducing-gpt-6-sol-and-luna/", "--format=text" ], "cmd_version": "1.0.0", "end_ts": "2026-09-22T22:06:17.567417+00:00", "index_texts": null, "output": "ArchiveError: Mercury was not able to get article text from the URL", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-22T22:06:16.252859+00:00", "status": "failed" }, { "cmd": [ "/app/node_modules/@postlight/parser/cli.js", "https://openai.com/index/introducing-gpt-6-sol-and-luna/", "--format=text" ], "cmd_version": "1.0.0", "end_ts": "2026-09-24T04:16:54.970982+00:00", "index_texts": null, "output": "ArchiveError: Mercury was not able to get article text from the URL", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-24T04:16:53.560344+00:00", "status": "failed" } ], "pdf": [], "readability": [ { "cmd": [ "/app/node_modules/readability-extractor/readability-extractor", "/tmp/tmpbe5_af1r", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "0.0.11", "end_ts": "2026-09-24T01:19:17.342753+00:00", "index_texts": [ "Earlier this month, we introduced GPT\u20116 Astra, the most intelligent and aligned model in the world. While the most demanding and important projects still call for Astra\u2019s full depth, work happens at different scales, rhythms, and budgets.That\u2019s why we\u2019re expanding the GPT\u20116 universe with GPT\u20116 Sol and GPT\u20116 Luna. GPT\u20116 Astra introduced a new generation of intelligence\u2014these models help distribute the benefits of that intelligence by advancing the frontier on cost efficiency. We trained GPT\u20116 Sol and Luna with similar methods as GPT\u20116 Astra, bringing the advances behind Astra\u2019s state-of-the-art performance in professional work, factuality, coding, computer use, and alignment to faster, more affordable models.The GPT\u20116 models lead across the cost\u2013intelligence curve, combining exceptional capabilities at every tier with infrastructure that delivers them efficiently at scale. Improvements in caching and inference let us serve these models at lower cost, and we\u2019re passing those savings directly on to users and customers by reducing API prices for Sol and Luna by 50% compared with their GPT\u20115.6 promotional pricing. Together, these improvements make advanced AI practical for more everyday tasks and applications at scale.GPT\u20116 API pricingPrices are per 1 million tokens.GPT\u20116 Astra continues to be our best model across the board. Choose it when you want the best results and an uncompromising experience.A step up across the model familyGPT\u20116 Sol and Luna bring intelligence upgrades and cost efficiency to the models you already know and use across capabilities most useful for getting complex work done.Professional workGPT\u20116 Sol can take on difficult work tasks while giving you more room to iterate with higher usage limits and lower cost, offering more intelligence and better results versus similarly priced competitor models.On AutomationBench, a test of business workflows across apps, GPT\u20116 Sol at xhigh effort outperforms Claude Opus 5 at max effort at just 9% of Opus 5\u2019s cost per task. At high effort, GPT\u20116 Luna improves on its predecessor by 5.4 percentage points at 58% lower cost per task.In AutomationBench 1.0.6\u2060(opens in a new window), AI agents are tested on end-to-end workflows using 47 tools across sales, marketing, operations, support, finance, and HR. The datapoint for Claude Fable 5.1 understates its actual cost, as it omits the cost of the Opus 5 fallbacks, which occurred on ~40% of tasks.GPT\u20116 Sol also exceeds Claude Fable 5.1 at far lower cost, and even bests low-effort GPT\u20116 Astra.On Agents\u2019 Last Exam, which evaluates agents on complex professional workflows, GPT\u20116 Sol at max effort scores 56.4%, above Claude Opus 5\u2019s highest score in the evaluation at 60% lower cost per task.FactualityThe usefulness of an answer depends on getting the facts right, and we\u2019re continuing to make progress on factual reliability. On our internal factuality evaluation, which is based on de-identified real-world conversations where users flagged mistakes by our models, GPT\u20116 Sol makes about half as many mistakes as its predecessor, approaching Astra-level reliability at much lower cost. GPT\u20116 Luna also improves substantially; at higher effort levels it matches GPT\u20115.6 Sol at about a hundredth its cost.Here we evaluate factuality on de-identified ChatGPT conversations where users had flagged a factual error from a prior model. These error-inducing conversations are not representative of typical usage, where factual errors are more rare. Scores are not controlled for length; however, our verbosity sweeps showed almost no dependence on answer length.CodingThis year, coding agents have begun tackling tasks with more complexity, scope, and duration than ever before. At OpenAI, our internal usage has grown exponentially. Valued at API prices, daily token usage has exceeded $600 for the median researcher and $7,000 for researchers at the 90th percentile (Research acceleration: The view inside OpenAI\u2060). As coding agents take on longer and more demanding tasks, the cost of sustained use matters more. GPT\u20116 Sol and Luna combine strong coding performance with lower API prices, giving developers more room to iterate and teams the confidence to be more ambitious about what they ask Codex to take on.On FrontierCode, which evaluates whether coding agents produce changes ready to merge into real codebases, GPT\u20116 Sol improves substantially over GPT\u20115.6 Sol, and is able to match Claude Fable 5.1 xhigh at much lower cost.On DeepSWE v1.1, which tests performance on complex software-engineering tasks in real codebases, GPT\u20116 Sol at max effort scores 68.8%, within 1.1 percentage points of Claude Fable 5\u2019s highest score in the evaluation\u201469.9% at xhigh effort\u2014at approximately 80% lower cost per task.GPT\u20116 Luna at max effort scores 66.6%, comparable to Claude Opus 5 and Fable 5 at medium effort. In these comparisons, Luna costs 93% less per task than Opus 5 and 96% less than Fable 5.Computer useWhile GPT\u20116 Astra remains the world\u2019s best model for computer use, GPT\u20116 Sol and Luna offer more cost-efficient performance than their predecessors. On OSWorld 2.0 offline, GPT\u20116 Sol at xhigh effort achieves a similar score to Claude Opus 5 at medium effort\u201460.5% versus 60.3%\u2014at approximately 80% lower cost per task. GPT\u20116 Luna (max) is able to exceed GPT\u20115.6 Sol (medium) at one tenth of its cost.In OSWorld 2.0\u2060(opens in a new window), AI agents attempt long-horizon computer-use workflows spanning everyday and professional tasks. We report the partial reward on the offline set from the v2026.08.08 release.Collaboration styleWe\u2019ve also brought GPT\u20116 Astra\u2019s improved communication style to Sol and Luna, which we think will be especially noticeable in technical and coding conversations. Expect to see more clarity, less jargon, fewer odd turns of phrase, fewer low-value details, and slightly shorter answers overall without losing substance.Website\u2019s looking clean! Could we use a Bento Box design style and add a slider between the pages in the top right? You may need to dive into the React..Although style is subjective, we prefer GPT\u20116 Sol\u2019s reply here. It doesn\u2019t jump to conclusions as quickly, spends less time reiterating details that might be obvious to the asker (e.g., that the website has four pages), uses less vague language (e.g., \u201cbento feel\u201d, \u201creshaping\u2026 around that system\u201d), is more forthcoming with what it did and didn\u2019t check, and doesn\u2019t unnecessarily share implementation details like its image tool prompt.Improving caching for agents and long conversationsAlongside lower token prices, we\u2019re helping developers building on GPT\u20116 save more on the context their applications reuse. We\u2019ve improved prompt caching for GPT\u20116 to deliver higher cache hit rates by default, helping agents reuse more context, respond faster, and benefit from discounts of 90% on cached input-token reads.Developers also have more ways to measure and optimize their caching performance:Monitor and diagnose. The Prompt Caching Dashboard\u2060(opens in a new window) shows how much input is cached and how that changes over time. The diagnostics tool\u2060(opens in a new window) helps explain missed opportunities for caching and what to fix.Adjust reasoning effort and tool availability without breaking cache. Increase reasoning effort\u2060(opens in a new window) for harder tasks or lower it for simpler follow-ups, and enable or disable tools\u2060(opens in a new window) as your agent\u2019s needs change. Both controls now preserve earlier context for cache reuse.Optimize which prefixes get cached. Explicit breakpoints let developers choose where cached prompt prefixes end. This gives developers more control over cache reuse and can improve performance.GitHub reports that, over the past several months, these improvements have reduced the share of prompt tokens requiring fresh processing by more than 50% across billions of requests to OpenAI models, helping Copilot respond faster.Continuing to improve alignmentGPT\u20116 Sol and Luna build on the alignment work introduced with Astra, our most aligned model to date. In our alignment evaluations, both Sol and Luna show improvements over their GPT\u20115.6 counterparts, including lower rates of misleading claims about their coding work.The evaluations below deliberately test challenging situations and do not measure failure rates in typical use. See the system card\u2060(opens in a new window) for the full results.AvailabilityGPT\u20116 Sol and GPT\u20116 Luna are available in ChatGPT Work and Codex starting today for all Plus, Pro, Business, Enterprise, and Edu users. Free and Go users can access GPT\u20116 Luna in the desktop app. These models are not yet available in Chat. In the OpenAI API, they are available as gpt-6-sol and gpt-6-luna.To keep service stable for everyone, we plan to roll out these models in ChatGPT gradually throughout the day. If you don\u2019t see the new models in ChatGPT Work or Codex, please try again later.Evaluations of GPT were performed in our research environment or via our API, which may provide slightly different output from production ChatGPT due to differences in the system prompts, tools available, etc. Evaluations of competitor models were taken from publicly available reports. Scores for Claude Fable 5 were reported when scores for Claude Fable 5.1 were unavailable." ], "output": "readability", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-24T01:19:13.995679+00:00", "status": "succeeded" }, { "cmd": [ "/app/node_modules/readability-extractor/readability-extractor", "/tmp/tmptkolg52r", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "0.0.11", "end_ts": "2026-09-23T19:19:30.142257+00:00", "index_texts": [ "Earlier this month, we introduced GPT\u20116 Astra, the most intelligent and aligned model in the world. While the most demanding and important projects still call for Astra\u2019s full depth, work happens at different scales, rhythms, and budgets.That\u2019s why we\u2019re expanding the GPT\u20116 universe with GPT\u20116 Sol and GPT\u20116 Luna. GPT\u20116 Astra introduced a new generation of intelligence\u2014these models help distribute the benefits of that intelligence by advancing the frontier on cost efficiency. We trained GPT\u20116 Sol and Luna with similar methods as GPT\u20116 Astra, bringing the advances behind Astra\u2019s state-of-the-art performance in professional work, factuality, coding, computer use, and alignment to faster, more affordable models.The GPT\u20116 models lead across the cost\u2013intelligence curve, combining exceptional capabilities at every tier with infrastructure that delivers them efficiently at scale. Improvements in caching and inference let us serve these models at lower cost, and we\u2019re passing those savings directly on to users and customers by reducing API prices for Sol and Luna by 50% compared with their GPT\u20115.6 promotional pricing. Together, these improvements make advanced AI practical for more everyday tasks and applications at scale.GPT\u20116 API pricingPrices are per 1 million tokens.GPT\u20116 Astra continues to be our best model across the board. Choose it when you want the best results and an uncompromising experience.A step up across the model familyGPT\u20116 Sol and Luna bring intelligence upgrades and cost efficiency to the models you already know and use across capabilities most useful for getting complex work done.Professional workGPT\u20116 Sol can take on difficult work tasks while giving you more room to iterate with higher usage limits and lower cost, offering more intelligence and better results versus similarly priced competitor models.On AutomationBench, a test of business workflows across apps, GPT\u20116 Sol at xhigh effort outperforms Claude Opus 5 at max effort at just 9% of Opus 5\u2019s cost per task. At high effort, GPT\u20116 Luna improves on its predecessor by 5.4 percentage points at 58% lower cost per task.In AutomationBench 1.0.6\u2060(opens in a new window), AI agents are tested on end-to-end workflows using 47 tools across sales, marketing, operations, support, finance, and HR. The datapoint for Claude Fable 5.1 understates its actual cost, as it omits the cost of the Opus 5 fallbacks, which occurred on ~40% of tasks.GPT\u20116 Sol also exceeds Claude Fable 5.1 at far lower cost, and even bests low-effort GPT\u20116 Astra.On Agents\u2019 Last Exam, which evaluates agents on complex professional workflows, GPT\u20116 Sol at max effort scores 56.4%, above Claude Opus 5\u2019s highest score in the evaluation at 60% lower cost per task.FactualityThe usefulness of an answer depends on getting the facts right, and we\u2019re continuing to make progress on factual reliability. On our internal factuality evaluation, which is based on de-identified real-world conversations where users flagged mistakes by our models, GPT\u20116 Sol makes about half as many mistakes as its predecessor, approaching Astra-level reliability at much lower cost. GPT\u20116 Luna also improves substantially; at higher effort levels it matches GPT\u20115.6 Sol at about a hundredth its cost.Here we evaluate factuality on de-identified ChatGPT conversations where users had flagged a factual error from a prior model. These error-inducing conversations are not representative of typical usage, where factual errors are more rare. Scores are not controlled for length; however, our verbosity sweeps showed almost no dependence on answer length.CodingThis year, coding agents have begun tackling tasks with more complexity, scope, and duration than ever before. At OpenAI, our internal usage has grown exponentially. Valued at API prices, daily token usage has exceeded $600 for the median researcher and $7,000 for researchers at the 90th percentile (Research acceleration: The view inside OpenAI\u2060). As coding agents take on longer and more demanding tasks, the cost of sustained use matters more. GPT\u20116 Sol and Luna combine strong coding performance with lower API prices, giving developers more room to iterate and teams the confidence to be more ambitious about what they ask Codex to take on.On FrontierCode, which evaluates whether coding agents produce changes ready to merge into real codebases, GPT\u20116 Sol improves substantially over GPT\u20115.6 Sol, and is able to match Claude Fable 5.1 xhigh at much lower cost.On DeepSWE v1.1, which tests performance on complex software-engineering tasks in real codebases, GPT\u20116 Sol at max effort scores 68.8%, within 1.1 percentage points of Claude Fable 5\u2019s highest score in the evaluation\u201469.9% at xhigh effort\u2014at approximately 80% lower cost per task.GPT\u20116 Luna at max effort scores 66.6%, comparable to Claude Opus 5 and Fable 5 at medium effort. In these comparisons, Luna costs 93% less per task than Opus 5 and 96% less than Fable 5.Computer useWhile GPT\u20116 Astra remains the world\u2019s best model for computer use, GPT\u20116 Sol and Luna offer more cost-efficient performance than their predecessors. On OSWorld 2.0 offline, GPT\u20116 Sol at xhigh effort achieves a similar score to Claude Opus 5 at medium effort\u201460.5% versus 60.3%\u2014at approximately 80% lower cost per task. GPT\u20116 Luna (max) is able to exceed GPT\u20115.6 Sol (medium) at one tenth of its cost.In OSWorld 2.0\u2060(opens in a new window), AI agents attempt long-horizon computer-use workflows spanning everyday and professional tasks. We report the partial reward on the offline set from the v2026.08.08 release.Collaboration styleWe\u2019ve also brought GPT\u20116 Astra\u2019s improved communication style to Sol and Luna, which we think will be especially noticeable in technical and coding conversations. Expect to see more clarity, less jargon, fewer odd turns of phrase, fewer low-value details, and slightly shorter answers overall without losing substance.Website\u2019s looking clean! Could we use a Bento Box design style and add a slider between the pages in the top right? You may need to dive into the React..Although style is subjective, we prefer GPT\u20116 Sol\u2019s reply here. It doesn\u2019t jump to conclusions as quickly, spends less time reiterating details that might be obvious to the asker (e.g., that the website has four pages), uses less vague language (e.g., \u201cbento feel\u201d, \u201creshaping\u2026 around that system\u201d), is more forthcoming with what it did and didn\u2019t check, and doesn\u2019t unnecessarily share implementation details like its image tool prompt.Improving caching for agents and long conversationsAlongside lower token prices, we\u2019re helping developers building on GPT\u20116 save more on the context their applications reuse. We\u2019ve improved prompt caching for GPT\u20116 to deliver higher cache hit rates by default, helping agents reuse more context, respond faster, and benefit from discounts of 90% on cached input-token reads.Developers also have more ways to measure and optimize their caching performance:Monitor and diagnose. The Prompt Caching Dashboard\u2060(opens in a new window) shows how much input is cached and how that changes over time. The diagnostics tool\u2060(opens in a new window) helps explain missed opportunities for caching and what to fix.Adjust reasoning effort and tool availability without breaking cache. Increase reasoning effort\u2060(opens in a new window) for harder tasks or lower it for simpler follow-ups, and enable or disable tools\u2060(opens in a new window) as your agent\u2019s needs change. Both controls now preserve earlier context for cache reuse.Optimize which prefixes get cached. Explicit breakpoints let developers choose where cached prompt prefixes end. This gives developers more control over cache reuse and can improve performance.GitHub reports that, over the past several months, these improvements have reduced the share of prompt tokens requiring fresh processing by more than 50% across billions of requests to OpenAI models, helping Copilot respond faster.Continuing to improve alignmentGPT\u20116 Sol and Luna build on the alignment work introduced with Astra, our most aligned model to date. In our alignment evaluations, both Sol and Luna show improvements over their GPT\u20115.6 counterparts, including lower rates of misleading claims about their coding work.The evaluations below deliberately test challenging situations and do not measure failure rates in typical use. See the system card\u2060(opens in a new window) for the full results.AvailabilityGPT\u20116 Sol and GPT\u20116 Luna are available in ChatGPT Work and Codex starting today for all Plus, Pro, Business, Enterprise, and Edu users. Free and Go users can access GPT\u20116 Luna in the desktop app. These models are not yet available in Chat. In the OpenAI API, they are available as gpt-6-sol and gpt-6-luna.To keep service stable for everyone, we plan to roll out these models in ChatGPT gradually throughout the day. If you don\u2019t see the new models in ChatGPT Work or Codex, please try again later.Evaluations of GPT were performed in our research environment or via our API, which may provide slightly different output from production ChatGPT due to differences in the system prompts, tools available, etc. Evaluations of competitor models were taken from publicly available reports. Scores for Claude Fable 5 were reported when scores for Claude Fable 5.1 were unavailable." ], "output": "readability", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-23T19:19:26.396773+00:00", "status": "succeeded" }, { "cmd": [ "/app/node_modules/readability-extractor/readability-extractor", "/tmp/tmpbsrh2p1t", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "0.0.11", "end_ts": "2026-09-22T22:06:16.241580+00:00", "index_texts": [ "Earlier this month, we introduced GPT\u20116 Astra, the most intelligent and aligned model in the world. While the most demanding and important projects still call for Astra\u2019s full depth, work happens at different scales, rhythms, and budgets.That\u2019s why we\u2019re expanding the GPT\u20116 universe with GPT\u20116 Sol and GPT\u20116 Luna. GPT\u20116 Astra introduced a new generation of intelligence\u2014these models help distribute the benefits of that intelligence by advancing the frontier on cost efficiency. We trained GPT\u20116 Sol and Luna with similar methods as GPT\u20116 Astra, bringing the advances behind Astra\u2019s state-of-the-art performance in professional work, factuality, coding, computer use, and alignment to faster, more affordable models.The GPT\u20116 models lead across the cost\u2013intelligence curve, combining exceptional capabilities at every tier with infrastructure that delivers them efficiently at scale. Improvements in caching and inference let us serve these models at lower cost, and we\u2019re passing those savings directly on to users and customers by reducing API prices for Sol and Luna by 50% compared with their GPT\u20115.6 promotional pricing. Together, these improvements make advanced AI practical for more everyday tasks and applications at scale.GPT\u20116 API pricingPrices are per 1 million tokens.GPT\u20116 Astra continues to be our best model across the board. Choose it when you want the best results and an uncompromising experience.A step up across the model familyGPT\u20116 Sol and Luna bring intelligence upgrades and cost efficiency to the models you already know and use across capabilities most useful for getting complex work done.Professional workGPT\u20116 Sol can take on difficult work tasks while giving you more room to iterate with higher usage limits and lower cost, offering more intelligence and better results versus similarly priced competitor models.On AutomationBench, a test of business workflows across apps, GPT\u20116 Sol at xhigh effort outperforms Claude Opus 5 at max effort at just 9% of Opus 5\u2019s cost per task. At high effort, GPT\u20116 Luna improves on its predecessor by 5.4 percentage points at 58% lower cost per task.In AutomationBench 1.0.6\u2060(opens in a new window), AI agents are tested on end-to-end workflows using 47 tools across sales, marketing, operations, support, finance, and HR. The datapoint for Claude Fable 5.1 understates its actual cost, as it omits the cost of the Opus 5 fallbacks, which occurred on ~40% of tasks.GPT\u20116 Sol also exceeds Claude Fable 5.1 at far lower cost, and even bests low-effort GPT\u20116 Astra.On Agents\u2019 Last Exam, which evaluates agents on complex professional workflows, GPT\u20116 Sol at max effort scores 56.4%, above Claude Opus 5\u2019s highest score in the evaluation at 60% lower cost per task.FactualityThe usefulness of an answer depends on getting the facts right, and we\u2019re continuing to make progress on factual reliability. On our internal factuality evaluation, which is based on de-identified real-world conversations where users flagged mistakes by our models, GPT\u20116 Sol makes about half as many mistakes as its predecessor, approaching Astra-level reliability at much lower cost. GPT\u20116 Luna also improves substantially; at higher effort levels it matches GPT\u20115.6 Sol at about a hundredth its cost.Here we evaluate factuality on de-identified ChatGPT conversations where users had flagged a factual error from a prior model. These error-inducing conversations are not representative of typical usage, where factual errors are more rare. Scores are not controlled for length; however, our verbosity sweeps showed almost no dependence on answer length.CodingThis year, coding agents have begun tackling tasks with more complexity, scope, and duration than ever before. At OpenAI, our internal usage has grown exponentially. Valued at API prices, daily token usage has exceeded $600 for the median researcher and $7,000 for researchers at the 90th percentile (Research acceleration: The view inside OpenAI\u2060). As coding agents take on longer and more demanding tasks, the cost of sustained use matters more. GPT\u20116 Sol and Luna combine strong coding performance with lower API prices, giving developers more room to iterate and teams the confidence to be more ambitious about what they ask Codex to take on.On FrontierCode, which evaluates whether coding agents produce changes ready to merge into real codebases, GPT\u20116 Sol improves substantially over GPT\u20115.6 Sol, and is able to match Claude Fable 5.1 xhigh at much lower cost.On DeepSWE v1.1, which tests performance on complex software-engineering tasks in real codebases, GPT\u20116 Sol at max effort scores 68.8%, within 1.1 percentage points of Claude Fable 5\u2019s highest score in the evaluation\u201469.9% at xhigh effort\u2014at approximately 80% lower cost per task.GPT\u20116 Luna at max effort scores 66.6%, comparable to Claude Opus 5 and Fable 5 at medium effort. In these comparisons, Luna costs 93% less per task than Opus 5 and 96% less than Fable 5.Computer useWhile GPT\u20116 Astra remains the world\u2019s best model for computer use, GPT\u20116 Sol and Luna offer more cost-efficient performance than their predecessors. On OSWorld 2.0 offline, GPT\u20116 Sol at xhigh effort achieves a similar score to Claude Opus 5 at medium effort\u201460.5% versus 60.3%\u2014at approximately 80% lower cost per task. GPT\u20116 Luna (max) is able to exceed GPT\u20115.6 Sol (medium) at one tenth of its cost.In OSWorld 2.0\u2060(opens in a new window), AI agents attempt long-horizon computer-use workflows spanning everyday and professional tasks. We report the partial reward on the offline set from the v2026.08.08 release.Collaboration styleWe\u2019ve also brought GPT\u20116 Astra\u2019s improved communication style to Sol and Luna, which we think will be especially noticeable in technical and coding conversations. Expect to see more clarity, less jargon, fewer odd turns of phrase, fewer low-value details, and slightly shorter answers overall without losing substance.Website\u2019s looking clean! Could we use a Bento Box design style and add a slider between the pages in the top right? You may need to dive into the React..Although style is subjective, we prefer GPT\u20116 Sol\u2019s reply here. It doesn\u2019t jump to conclusions as quickly, spends less time reiterating details that might be obvious to the asker (e.g., that the website has four pages), uses less vague language (e.g., \u201cbento feel\u201d, \u201creshaping\u2026 around that system\u201d), is more forthcoming with what it did and didn\u2019t check, and doesn\u2019t unnecessarily share implementation details like its image tool prompt.Improving caching for agents and long conversationsAlongside lower token prices, we\u2019re helping developers building on GPT\u20116 save more on the context their applications reuse. We\u2019ve improved prompt caching for GPT\u20116 to deliver higher cache hit rates by default, helping agents reuse more context, respond faster, and benefit from discounts of 90% on cached input-token reads.Developers also have more ways to measure and optimize their caching performance:Monitor and diagnose. The Prompt Caching Dashboard\u2060(opens in a new window) shows how much input is cached and how that changes over time. The diagnostics tool\u2060(opens in a new window) helps explain missed opportunities for caching and what to fix.Adjust reasoning effort and tool availability without breaking cache. Increase reasoning effort\u2060(opens in a new window) for harder tasks or lower it for simpler follow-ups, and enable or disable tools\u2060(opens in a new window) as your agent\u2019s needs change. Both controls now preserve earlier context for cache reuse.Optimize which prefixes get cached. Explicit breakpoints let developers choose where cached prompt prefixes end. This gives developers more control over cache reuse and can improve performance.GitHub reports that, over the past several months, these improvements have reduced the share of prompt tokens requiring fresh processing by more than 50% across billions of requests to OpenAI models, helping Copilot respond faster.Continuing to improve alignmentGPT\u20116 Sol and Luna build on the alignment work introduced with Astra, our most aligned model to date. In our alignment evaluations, both Sol and Luna show improvements over their GPT\u20115.6 counterparts, including lower rates of misleading claims about their coding work.The evaluations below deliberately test challenging situations and do not measure failure rates in typical use. See the system card\u2060(opens in a new window) for the full results.AvailabilityGPT\u20116 Sol and GPT\u20116 Luna are available in ChatGPT Work and Codex starting today for all Plus, Pro, Business, Enterprise, and Edu users. Free and Go users can access GPT\u20116 Luna in the desktop app. These models are not yet available in Chat. In the OpenAI API, they are available as gpt-6-sol and gpt-6-luna.To keep service stable for everyone, we plan to roll out these models in ChatGPT gradually throughout the day. If you don\u2019t see the new models in ChatGPT Work or Codex, please try again later.Evaluations of GPT were performed in our research environment or via our API, which may provide slightly different output from production ChatGPT due to differences in the system prompts, tools available, etc. Evaluations of competitor models were taken from publicly available reports. Scores for Claude Fable 5 were reported when scores for Claude Fable 5.1 were unavailable." ], "output": "readability", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-22T22:06:12.340705+00:00", "status": "succeeded" }, { "cmd": [ "/app/node_modules/readability-extractor/readability-extractor", "/tmp/tmpvx05ryu5", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "0.0.11", "end_ts": "2026-09-24T04:16:53.543578+00:00", "index_texts": [ "Earlier this month, we introduced GPT\u20116 Astra, the most intelligent and aligned model in the world. While the most demanding and important projects still call for Astra\u2019s full depth, work happens at different scales, rhythms, and budgets.That\u2019s why we\u2019re expanding the GPT\u20116 universe with GPT\u20116 Sol and GPT\u20116 Luna. GPT\u20116 Astra introduced a new generation of intelligence\u2014these models help distribute the benefits of that intelligence by advancing the frontier on cost efficiency. We trained GPT\u20116 Sol and Luna with similar methods as GPT\u20116 Astra, bringing the advances behind Astra\u2019s state-of-the-art performance in professional work, factuality, coding, computer use, and alignment to faster, more affordable models.The GPT\u20116 models lead across the cost\u2013intelligence curve, combining exceptional capabilities at every tier with infrastructure that delivers them efficiently at scale. Improvements in caching and inference let us serve these models at lower cost, and we\u2019re passing those savings directly on to users and customers by reducing API prices for Sol and Luna by 50% compared with their GPT\u20115.6 promotional pricing. Together, these improvements make advanced AI practical for more everyday tasks and applications at scale.GPT\u20116 API pricingPrices are per 1 million tokens.GPT\u20116 Astra continues to be our best model across the board. Choose it when you want the best results and an uncompromising experience.A step up across the model familyGPT\u20116 Sol and Luna bring intelligence upgrades and cost efficiency to the models you already know and use across capabilities most useful for getting complex work done.Professional workGPT\u20116 Sol can take on difficult work tasks while giving you more room to iterate with higher usage limits and lower cost, offering more intelligence and better results versus similarly priced competitor models.On AutomationBench, a test of business workflows across apps, GPT\u20116 Sol at xhigh effort outperforms Claude Opus 5 at max effort at just 9% of Opus 5\u2019s cost per task. At high effort, GPT\u20116 Luna improves on its predecessor by 5.4 percentage points at 58% lower cost per task.In AutomationBench 1.0.6\u2060(opens in a new window), AI agents are tested on end-to-end workflows using 47 tools across sales, marketing, operations, support, finance, and HR. The datapoint for Claude Fable 5.1 understates its actual cost, as it omits the cost of the Opus 5 fallbacks, which occurred on ~40% of tasks.GPT\u20116 Sol also exceeds Claude Fable 5.1 at far lower cost, and even bests low-effort GPT\u20116 Astra.On Agents\u2019 Last Exam, which evaluates agents on complex professional workflows, GPT\u20116 Sol at max effort scores 56.4%, above Claude Opus 5\u2019s highest score in the evaluation at 60% lower cost per task.FactualityThe usefulness of an answer depends on getting the facts right, and we\u2019re continuing to make progress on factual reliability. On our internal factuality evaluation, which is based on de-identified real-world conversations where users flagged mistakes by our models, GPT\u20116 Sol makes about half as many mistakes as its predecessor, approaching Astra-level reliability at much lower cost. GPT\u20116 Luna also improves substantially; at higher effort levels it matches GPT\u20115.6 Sol at about a hundredth its cost.Here we evaluate factuality on de-identified ChatGPT conversations where users had flagged a factual error from a prior model. These error-inducing conversations are not representative of typical usage, where factual errors are more rare. Scores are not controlled for length; however, our verbosity sweeps showed almost no dependence on answer length.CodingThis year, coding agents have begun tackling tasks with more complexity, scope, and duration than ever before. At OpenAI, our internal usage has grown exponentially. Valued at API prices, daily token usage has exceeded $600 for the median researcher and $7,000 for researchers at the 90th percentile (Research acceleration: The view inside OpenAI\u2060). As coding agents take on longer and more demanding tasks, the cost of sustained use matters more. GPT\u20116 Sol and Luna combine strong coding performance with lower API prices, giving developers more room to iterate and teams the confidence to be more ambitious about what they ask Codex to take on.On FrontierCode, which evaluates whether coding agents produce changes ready to merge into real codebases, GPT\u20116 Sol improves substantially over GPT\u20115.6 Sol, and is able to match Claude Fable 5.1 xhigh at much lower cost.On DeepSWE v1.1, which tests performance on complex software-engineering tasks in real codebases, GPT\u20116 Sol at max effort scores 68.8%, within 1.1 percentage points of Claude Fable 5\u2019s highest score in the evaluation\u201469.9% at xhigh effort\u2014at approximately 80% lower cost per task.GPT\u20116 Luna at max effort scores 66.6%, comparable to Claude Opus 5 and Fable 5 at medium effort. In these comparisons, Luna costs 93% less per task than Opus 5 and 96% less than Fable 5.Computer useWhile GPT\u20116 Astra remains the world\u2019s best model for computer use, GPT\u20116 Sol and Luna offer more cost-efficient performance than their predecessors. On OSWorld 2.0 offline, GPT\u20116 Sol at xhigh effort achieves a similar score to Claude Opus 5 at medium effort\u201460.5% versus 60.3%\u2014at approximately 80% lower cost per task. GPT\u20116 Luna (max) is able to exceed GPT\u20115.6 Sol (medium) at one tenth of its cost.In OSWorld 2.0\u2060(opens in a new window), AI agents attempt long-horizon computer-use workflows spanning everyday and professional tasks. We report the partial reward on the offline set from the v2026.08.08 release.Collaboration styleWe\u2019ve also brought GPT\u20116 Astra\u2019s improved communication style to Sol and Luna, which we think will be especially noticeable in technical and coding conversations. Expect to see more clarity, less jargon, fewer odd turns of phrase, fewer low-value details, and slightly shorter answers overall without losing substance.Website\u2019s looking clean! Could we use a Bento Box design style and add a slider between the pages in the top right? You may need to dive into the React..Although style is subjective, we prefer GPT\u20116 Sol\u2019s reply here. It doesn\u2019t jump to conclusions as quickly, spends less time reiterating details that might be obvious to the asker (e.g., that the website has four pages), uses less vague language (e.g., \u201cbento feel\u201d, \u201creshaping\u2026 around that system\u201d), is more forthcoming with what it did and didn\u2019t check, and doesn\u2019t unnecessarily share implementation details like its image tool prompt.Improving caching for agents and long conversationsAlongside lower token prices, we\u2019re helping developers building on GPT\u20116 save more on the context their applications reuse. We\u2019ve improved prompt caching for GPT\u20116 to deliver higher cache hit rates by default, helping agents reuse more context, respond faster, and benefit from discounts of 90% on cached input-token reads.Developers also have more ways to measure and optimize their caching performance:Monitor and diagnose. The Prompt Caching Dashboard\u2060(opens in a new window) shows how much input is cached and how that changes over time. The diagnostics tool\u2060(opens in a new window) helps explain missed opportunities for caching and what to fix.Adjust reasoning effort and tool availability without breaking cache. Increase reasoning effort\u2060(opens in a new window) for harder tasks or lower it for simpler follow-ups, and enable or disable tools\u2060(opens in a new window) as your agent\u2019s needs change. Both controls now preserve earlier context for cache reuse.Optimize which prefixes get cached. Explicit breakpoints let developers choose where cached prompt prefixes end. This gives developers more control over cache reuse and can improve performance.GitHub reports that, over the past several months, these improvements have reduced the share of prompt tokens requiring fresh processing by more than 50% across billions of requests to OpenAI models, helping Copilot respond faster.Continuing to improve alignmentGPT\u20116 Sol and Luna build on the alignment work introduced with Astra, our most aligned model to date. In our alignment evaluations, both Sol and Luna show improvements over their GPT\u20115.6 counterparts, including lower rates of misleading claims about their coding work.The evaluations below deliberately test challenging situations and do not measure failure rates in typical use. See the system card\u2060(opens in a new window) for the full results.AvailabilityGPT\u20116 Sol and GPT\u20116 Luna are available in ChatGPT Work and Codex starting today for all Plus, Pro, Business, Enterprise, and Edu users. Free and Go users can access GPT\u20116 Luna in the desktop app. These models are not yet available in Chat. In the OpenAI API, they are available as gpt-6-sol and gpt-6-luna.To keep service stable for everyone, we plan to roll out these models in ChatGPT gradually throughout the day. If you don\u2019t see the new models in ChatGPT Work or Codex, please try again later.Evaluations of GPT were performed in our research environment or via our API, which may provide slightly different output from production ChatGPT due to differences in the system prompts, tools available, etc. Evaluations of competitor models were taken from publicly available reports. Scores for Claude Fable 5 were reported when scores for Claude Fable 5.1 were unavailable." ], "output": "readability", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-24T04:16:49.948838+00:00", "status": "succeeded" } ], "screenshot": [], "singlefile": [], "title": [ { "cmd": [ "curl", "--silent", "--location", "--compressed", "--max-time", "120", "--user-agent", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) curl/curl 8.14.1 (x86_64-pc-linux-gnu)", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "curl 8.14.1 (x86_64-pc-linux-gnu)", "end_ts": "2026-09-24T01:19:13.987174+00:00", "index_texts": null, "output": "Introducing GPT-6 Sol and Luna | OpenAI", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-24T01:19:12.498435+00:00", "status": "succeeded" }, { "cmd": [ "curl", "--silent", "--location", "--compressed", "--max-time", "120", "--user-agent", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) curl/curl 8.14.1 (x86_64-pc-linux-gnu)", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "curl 8.14.1 (x86_64-pc-linux-gnu)", "end_ts": "2026-09-23T19:19:26.388927+00:00", "index_texts": null, "output": "Introducing GPT-6 Sol and Luna | OpenAI", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-23T19:19:25.705840+00:00", "status": "succeeded" }, { "cmd": [ "curl", "--silent", "--location", "--compressed", "--max-time", "120", "--user-agent", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) curl/curl 8.14.1 (x86_64-pc-linux-gnu)", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "curl 8.14.1 (x86_64-pc-linux-gnu)", "end_ts": "2026-09-22T22:06:12.332780+00:00", "index_texts": null, "output": "Introducing GPT-6 Sol and Luna | OpenAI", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-22T22:06:11.631569+00:00", "status": "succeeded" }, { "cmd": [ "curl", "--silent", "--location", "--compressed", "--max-time", "120", "--user-agent", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) curl/curl 8.14.1 (x86_64-pc-linux-gnu)", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "curl 8.14.1 (x86_64-pc-linux-gnu)", "end_ts": "2026-09-24T04:16:49.940770+00:00", "index_texts": null, "output": "Introducing GPT-6 Sol and Luna | OpenAI", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-24T04:16:49.392756+00:00", "status": "succeeded" } ], "wget": [ { "cmd": [ "wget", "--no-verbose", "--adjust-extension", "--convert-links", "--force-directories", "--span-hosts", "--no-parent", "-e", "robots=off", "--reject=js,mjs,woff,woff2,ttf,otf,eot,mp4,m4v,mov,webm,ogv,mkv,avi,flv", "--timeout=120", "--restrict-file-names=windows", "--page-requisites", "--user-agent=Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) wget/GNU Wget 1.21.3", "--compression=auto", "--timestamping", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "GNU Wget 1.21.3", "end_ts": "2026-09-24T01:19:12.488305+00:00", "index_texts": null, "output": "ArchiveError: 403 Forbidden (try changing WGET_USER_AGENT)", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-24T01:19:12.117992+00:00", "status": "failed" }, { "cmd": [ "wget", "--no-verbose", "--adjust-extension", "--convert-links", "--force-directories", "--span-hosts", "--no-parent", "-e", "robots=off", "--reject=js,mjs,woff,woff2,ttf,otf,eot,mp4,m4v,mov,webm,ogv,mkv,avi,flv", "--timeout=120", "--restrict-file-names=windows", "--page-requisites", "--user-agent=Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) wget/GNU Wget 1.21.3", "--compression=auto", "--timestamping", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "GNU Wget 1.21.3", "end_ts": "2026-09-23T19:19:25.698272+00:00", "index_texts": null, "output": "ArchiveError: 403 Forbidden (try changing WGET_USER_AGENT)", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-23T19:19:25.331605+00:00", "status": "failed" }, { "cmd": [ "wget", "--no-verbose", "--adjust-extension", "--convert-links", "--force-directories", "--span-hosts", "--no-parent", "-e", "robots=off", "--reject=js,mjs,woff,woff2,ttf,otf,eot,mp4,m4v,mov,webm,ogv,mkv,avi,flv", "--timeout=120", "--restrict-file-names=windows", "--page-requisites", "--user-agent=Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) wget/GNU Wget 1.21.3", "--compression=auto", "--timestamping", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "GNU Wget 1.21.3", "end_ts": "2026-09-22T22:06:11.621137+00:00", "index_texts": null, "output": "ArchiveError: 403 Forbidden (try changing WGET_USER_AGENT)", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-22T22:06:11.273701+00:00", "status": "failed" }, { "cmd": [ "wget", "--no-verbose", "--adjust-extension", "--convert-links", "--force-directories", "--span-hosts", "--no-parent", "-e", "robots=off", "--reject=js,mjs,woff,woff2,ttf,otf,eot,mp4,m4v,mov,webm,ogv,mkv,avi,flv", "--timeout=120", "--restrict-file-names=windows", "--page-requisites", "--user-agent=Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36 ArchiveBox/0.7.4 (+https://github.com/ArchiveBox/ArchiveBox/) wget/GNU Wget 1.21.3", "--compression=auto", "--timestamping", "https://openai.com/index/introducing-gpt-6-sol-and-luna/" ], "cmd_version": "GNU Wget 1.21.3", "end_ts": "2026-09-24T04:16:49.384008+00:00", "index_texts": null, "output": "ArchiveError: 403 Forbidden (try changing WGET_USER_AGENT)", "pwd": "/data/archive/1790114708.518503", "schema": "ArchiveResult", "start_ts": "2026-09-24T04:16:49.042051+00:00", "status": "failed" } ] }, "icons": null, "is_archived": false, "is_static": false, "latest": { "archive_org": null, "dom": null, "favicon": "favicon.ico", "git": null, "media": null, "pdf": null, "screenshot": null, "singlefile": null, "title": "Introducing GPT-6 Sol and Luna | OpenAI", "warc": null, "wget": "ArchiveError: 403 Forbidden (try changing WGET_USER_AGENT)" }, "link_dir": "/data/archive/1790114708.518503", "newest_archive_date": "2026-09-24T04:16:53.560344+00:00", "num_failures": 8, "num_outputs": 16, "oldest_archive_date": "2026-09-22T22:06:10.219885+00:00", "path": "/index/introducing-gpt-6-sol-and-luna/", "schema": "Link", "scheme": "https", "snapshot_id": "1ce91b48-f944-4a66-9fa2-79368a4ebe89", "sources": [ "/data/sources/1790114708-import.txt" ], "tags": ",war-wiki", "tags_str": "war-wiki", "timestamp": "1790114708.518503", "title": "Introducing GPT-6 Sol and Luna | OpenAI", "updated": "2026-09-24T04:16:48.207749+00:00", "updated_date": "2026-09-24 04:16", "url": "https://openai.com/index/introducing-gpt-6-sol-and-luna/" }