{"id":19723,"date":"2026-09-23T11:18:18","date_gmt":"2026-09-23T15:18:18","guid":{"rendered":"https:\/\/wp.glbgpt.com\/?p=19723"},"modified":"2026-09-23T11:18:19","modified_gmt":"2026-09-23T15:18:19","slug":"gpt-6-luna-review","status":"publish","type":"post","link":"https:\/\/wp.glbgpt.com\/th\/hub\/gpt-6-luna-review","title":{"rendered":"\u0e23\u0e35\u0e27\u0e34\u0e27 GPT-6 Luna: \u0e23\u0e32\u0e04\u0e32, \u0e1a\u0e23\u0e34\u0e1a\u0e17, \u0e02\u0e49\u0e2d\u0e08\u0e33\u0e01\u0e31\u0e14\u0e02\u0e2d\u0e07 API \u0e41\u0e25\u0e30\u0e27\u0e34\u0e18\u0e35\u0e43\u0e0a\u0e49\u0e07\u0e32\u0e19\u0e17\u0e35\u0e48\u0e14\u0e35\u0e17\u0e35\u0e48\u0e2a\u0e38\u0e14"},"content":{"rendered":"<p class=\"wp-block-paragraph\"><strong>\u0e04\u0e33\u0e15\u0e2d\u0e1a\u0e14\u0e48\u0e27\u0e19:<\/strong> GPT-6 Luna is OpenAI&#8217;s focused, high-volume tasks model. The official API lists $0.10 per 1M input tokens and $0.50 per 1M output tokens at Standard rates, with a 1.05M-token context window and 128K maximum output. GlobalGPT currently lists GPT-5.6 Sol and GPT-5.6 Luna rather than exact GPT-6 routes, so the platform naming must be checked separately from OpenAI&#8217;s API model ID.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">OpenAI describes GPT-6 Luna as its most efficient model for focused, high-volume tasks. That phrase is provider positioning, not a measured quality score. This review translates the published specification into buying and architecture decisions, then records public reaction with attribution. We also ran a scoped compatibility-API test with the model ID, route, prompt set, raw output, and usage retained. The task results are shown below as one observed run, not as an official benchmark.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img alt=\"\" fetchpriority=\"high\" decoding=\"async\" width=\"2558\" height=\"1366\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/\u5fae\u4fe1\u622a\u56fe_20260923225220.jpg\" class=\"wp-image-19727\"\/><\/figure>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-3e41869c wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link has-black-color has-text-color has-background has-link-color wp-element-button\" href=\"https:\/\/www.glbgpt.com\/home\/gpt-6-luna?inviter=hub_corner_popup_gpt-6-luna&amp;login=1\" style=\"background:linear-gradient(135deg,rgb(255,245,203) 0%,rgb(182,227,212) 11%,rgb(51,167,181) 71%)\"><strong>Try GPT-6 Luna on GloalGPT<\/strong><\/a><\/div>\n<\/div>\n\n\n\n<nav aria-label=\"\u0e2a\u0e32\u0e23\u0e1a\u0e31\u0e0d\" style=\"box-sizing:border-box;margin:24px 0;padding:18px;border:1px solid #b8d9d5;border-radius:8px;background:#f2f8f7;color:#172a2d;font-family:Arial,sans-serif\"><strong style=\"display:block;color:#086b72\">\u0e2a\u0e32\u0e23\u0e1a\u0e31\u0e0d<\/strong><ol style=\"columns:2;column-gap:28px;margin:10px 0 0;padding-left:22px;line-height:1.75\"><li><a href=\"#verdict\">\u0e1a\u0e17\u0e2a\u0e23\u0e38\u0e1b\u0e2a\u0e31\u0e49\u0e19\u0e46<\/a><\/li><li><a href=\"#specs\">\u0e2a\u0e23\u0e38\u0e1b\u0e2a\u0e31\u0e49\u0e19\u0e46<\/a><\/li><li><a href=\"#pricing\">Pricing and 272K rule<\/a><\/li><li><a href=\"#api-fit\">API fit and limits<\/a><\/li><li><a href=\"#use-cases\">What the specs imply<\/a><\/li><li><a href=\"#hands-on\">\u0e01\u0e32\u0e23\u0e17\u0e14\u0e2a\u0e2d\u0e1a API \u0e41\u0e1a\u0e1a\u0e1b\u0e0f\u0e34\u0e1a\u0e31\u0e15\u0e34\u0e08\u0e23\u0e34\u0e07<\/a><\/li><li><a href=\"#reactions\">Public reactions<\/a><\/li><li><a href=\"#globalgpt\">GlobalGPT naming note<\/a><\/li><li><a href=\"#choice\">Who should choose it?<\/a><\/li><li><a href=\"#faq\">\u0e04\u0e33\u0e16\u0e32\u0e21\u0e17\u0e35\u0e48\u0e1e\u0e1a\u0e1a\u0e48\u0e2d\u0e22<\/a><\/li><\/ol><\/nav>\n\n\n\n<h2 id=\"verdict\" class=\"wp-block-heading\">\u0e1a\u0e17\u0e2a\u0e23\u0e38\u0e1b\u0e2a\u0e31\u0e49\u0e19\u0e46<\/h2>\n\n\n\n<section style=\"box-sizing:border-box;margin:24px 0;padding:18px 20px;border-left:5px solid #087f8c;background:#f2f8f7;color:#172a2d;font-family:Arial,sans-serif\"><strong style=\"display:block;color:#087f8c;font-size:12px;letter-spacing:.04em;text-transform:uppercase\">\u0e2a\u0e23\u0e38\u0e1b<\/strong><p style=\"margin:7px 0 0;line-height:1.65\">Luna is the pragmatic cost-first option on the official price sheet: it keeps the same headline context and output ceilings as Sol while charging a fraction of the token rates. That makes it compelling for focused, repeated calls, but the low price is not proof of lower latency or lower quality without a controlled test.<\/p><\/section>\n\n\n\n<p class=\"wp-block-paragraph\">The most useful distinction is not simply &#8220;smart&#8221; versus &#8220;fast.&#8221; It is workload shape. GPT-6 Luna has the same headline context and output ceilings as its sibling, but its positioning and price make a different operating point sensible. Compare the broader <a href=\"https:\/\/www.glbgpt.com\/hub\/best-ai-models\/\">AI model selection guide<\/a> \u0e41\u0e25\u0e30 <a href=\"https:\/\/www.glbgpt.com\/hub\/best-ai-model-for-coding\/\">\u0e42\u0e21\u0e40\u0e14\u0e25 AI \u0e17\u0e35\u0e48\u0e14\u0e35\u0e17\u0e35\u0e48\u0e2a\u0e38\u0e14\u0e2a\u0e33\u0e2b\u0e23\u0e31\u0e1a\u0e01\u0e32\u0e23\u0e40\u0e02\u0e35\u0e22\u0e19\u0e42\u0e04\u0e49\u0e14<\/a> guide when the task spans more than one provider.<\/p>\n\n\n\n<h2 id=\"specs\" class=\"wp-block-heading\">GPT-6 Luna at a glance<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The table below follows the current <a href=\"https:\/\/developers.openai.com\/api\/docs\/models\/gpt-6-luna\">official GPT-6 Luna model page<\/a>. Context capacity is not the same thing as a recommended prompt size, and the maximum input is separate from the headline context window.<\/p>\n\n\n\n<div style=\"overflow-x:auto;margin:24px 0;border:1px solid #b8d9d5;border-radius:8px\"><table style=\"width:100%;min-width:760px;border-collapse:collapse;background:#fff;color:#172a2d;font-family:Arial,sans-serif\"><thead><tr style=\"background:#087f8c;color:#fff\"><th style=\"padding:11px;text-align:left;vertical-align:top\">\u0e2a\u0e19\u0e32\u0e21<\/th><th style=\"padding:11px;text-align:left;vertical-align:top\">Official value<\/th><\/tr><\/thead><tbody><tr style=\"background:#fff\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Official API ID<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\"><code>gpt-6-luna<\/code><\/td><\/tr><tr style=\"background:#f2f8f7\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Provider positioning<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">focused, high-volume tasks<\/td><\/tr><tr style=\"background:#fff\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e2b\u0e19\u0e49\u0e32\u0e15\u0e48\u0e32\u0e07\u0e1a\u0e23\u0e34\u0e1a\u0e17<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">1,050,000 \u0e42\u0e17\u0e40\u0e04\u0e19<\/td><\/tr><tr style=\"background:#f2f8f7\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e04\u0e48\u0e32\u0e2a\u0e39\u0e07\u0e2a\u0e38\u0e14\u0e17\u0e35\u0e48\u0e23\u0e31\u0e1a\u0e40\u0e02\u0e49\u0e32<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">922,000 \u0e42\u0e17\u0e40\u0e04\u0e19<\/td><\/tr><tr style=\"background:#fff\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e01\u0e33\u0e25\u0e31\u0e07\u0e2d\u0e2d\u0e01\u0e2a\u0e39\u0e07\u0e2a\u0e38\u0e14<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">128,000 \u0e42\u0e17\u0e40\u0e04\u0e47\u0e19<\/td><\/tr><tr style=\"background:#f2f8f7\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e04\u0e27\u0e32\u0e21\u0e23\u0e39\u0e49\u0e17\u0e35\u0e48\u0e2a\u0e34\u0e49\u0e19\u0e2a\u0e38\u0e14<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">May 18, 2026<\/td><\/tr><tr style=\"background:#fff\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e40\u0e02\u0e49\u0e32 \/ \u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e2d\u0e2d\u0e01<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Text + image input \/ text output<\/td><\/tr><tr style=\"background:#f2f8f7\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e04\u0e27\u0e32\u0e21\u0e1e\u0e22\u0e32\u0e22\u0e32\u0e21\u0e43\u0e19\u0e01\u0e32\u0e23\u0e43\u0e0a\u0e49\u0e40\u0e2b\u0e15\u0e38\u0e1c\u0e25<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">none, low, medium, high, xhigh, max<\/td><\/tr><tr style=\"background:#fff\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Supported endpoints<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Chat Completions, Responses, Batch<\/td><\/tr><\/tbody><\/table><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Both sibling models support structured outputs, function calling, streaming, prompt caching, image input, file search, and web search in the documented feature set. The exact tool behavior still depends on endpoint, account, and request configuration; do not infer a successful tool run from a capability checkbox.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For family-level context, the <a href=\"https:\/\/www.glbgpt.com\/hub\/gpt-6-astra-review\/\">\u0e23\u0e35\u0e27\u0e34\u0e27 GPT-6 Astra<\/a> shows how a related GPT-6 article separates provider documentation, platform routes, and attributed reactions.<\/p>\n\n\n\n<h2 id=\"pricing\" class=\"wp-block-heading\">GPT-6 Luna pricing and the 272K rule<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">At Standard rates, the <a href=\"https:\/\/developers.openai.com\/api\/docs\/pricing\">OpenAI pricing page<\/a> lists $0.10 input, $0.01 cached input, $0.125 cache writes, and $0.50 output per 1M tokens. Cache writes are 1.25x uncached input. Batch and Flex are priced at 50% of Standard, Fast mode is 2x the applicable rate, and regional processing adds 10% where available.<\/p>\n\n\n\n<div style=\"overflow-x:auto;margin:24px 0;border:1px solid #b8d9d5;border-radius:8px\"><table style=\"width:100%;min-width:760px;border-collapse:collapse;background:#fff;color:#172a2d;font-family:Arial,sans-serif\"><thead><tr style=\"background:#087f8c;color:#fff\"><th style=\"padding:11px;text-align:left;vertical-align:top\">Rate category<\/th><th style=\"padding:11px;text-align:left;vertical-align:top\">Up to 272K input<\/th><th style=\"padding:11px;text-align:left;vertical-align:top\">Above 272K input<\/th><\/tr><\/thead><tbody><tr style=\"background:#fff\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e17\u0e35\u0e48\u0e22\u0e31\u0e07\u0e44\u0e21\u0e48\u0e16\u0e39\u0e01\u0e40\u0e01\u0e47\u0e1a\u0e44\u0e27\u0e49\u0e43\u0e19\u0e41\u0e04\u0e0a<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">$0.10 \/ 1M<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">$0.20 \/ 1M<\/td><\/tr><tr style=\"background:#f2f8f7\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e17\u0e35\u0e48\u0e40\u0e01\u0e47\u0e1a\u0e44\u0e27\u0e49\u0e43\u0e19\u0e41\u0e04\u0e0a<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">$0.01 \/ 1M<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">$0.02 \/ 1M<\/td><\/tr><tr style=\"background:#fff\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e01\u0e32\u0e23\u0e40\u0e02\u0e35\u0e22\u0e19\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e25\u0e07\u0e43\u0e19\u0e41\u0e04\u0e0a<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">$0.125 \/ 1M<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">$0.25 \/ 1M<\/td><\/tr><tr style=\"background:#f2f8f7\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e1c\u0e25\u0e25\u0e31\u0e1e\u0e18\u0e4c<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">$0.50 \/ 1M<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">$0.75 \/ 1M<\/td><\/tr><\/tbody><\/table><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">The long-context column is calculated from OpenAI&#8217;s rule: above 272K input, input and cache rates double and output is multiplied by 1.5 for the full request. It is not a mixed-rate bill. For a practical budget baseline, 100K input plus 10K output costs about <strong>$0.015<\/strong> before tools or regional uplift; a 300K input plus 20K output example costs about <strong>$0.075<\/strong> at the long-context rates.<\/p>\n\n\n\n<section style=\"box-sizing:border-box;margin:24px 0;padding:18px 20px;border-left:5px solid #d36449;background:#f2f8f7;color:#172a2d;font-family:Arial,sans-serif\"><strong style=\"display:block;color:#d36449;font-size:12px;letter-spacing:.04em;text-transform:uppercase\">Pricing caution<\/strong><p style=\"margin:7px 0 0;line-height:1.65\">A large context window can be useful without being cheap to fill. Retrieval, chunk selection, cache reuse, and output caps matter more than the headline 1.05M number. See the <a href=\"https:\/\/www.glbgpt.com\/hub\/gpt-6-astra-pricing\/\">\u0e23\u0e32\u0e22\u0e25\u0e30\u0e40\u0e2d\u0e35\u0e22\u0e14\u0e23\u0e32\u0e04\u0e32 GPT-6 Astra<\/a> for a related explanation of context thresholds and cache mechanics.<\/p><\/section>\n\n\n\n<h2 id=\"api-fit\" class=\"wp-block-heading\">GPT-6 Luna API fit, tools, and limits<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">OpenAI recommends the Responses API for built-in tools and function calling. Chat Completions supports function calling only when <code>\u0e01\u0e32\u0e23\u0e04\u0e34\u0e14\u0e27\u0e34\u0e40\u0e04\u0e23\u0e32\u0e30\u0e2b\u0e4c\u0e41\u0e25\u0e30\u0e04\u0e27\u0e32\u0e21\u0e1e\u0e22\u0e32\u0e22\u0e32\u0e21<\/code> is set to <code>\u0e44\u0e21\u0e48\u0e21\u0e35<\/code>. That is an implementation detail worth catching before migration: a request can be syntactically valid yet fail to match the tool behavior your current integration expects.<\/p>\n\n\n\n<div style=\"overflow-x:auto;margin:24px 0;border:1px solid #b8d9d5;border-radius:8px\"><table style=\"width:100%;min-width:680px;border-collapse:collapse;background:#fff;color:#172a2d;font-family:Arial,sans-serif\"><thead><tr style=\"background:#087f8c;color:#fff\"><th style=\"padding:11px;text-align:left;vertical-align:top\">\u0e04\u0e27\u0e32\u0e21\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16<\/th><th style=\"padding:11px;text-align:left;vertical-align:top\">Supported or documented behavior<\/th><\/tr><\/thead><tbody><tr style=\"background:#fff\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e04\u0e33\u0e15\u0e2d\u0e1a<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Supported; built-in tools and function calling<\/td><\/tr><tr style=\"background:#f2f8f7\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Chat Completions<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Supported; function calling only at reasoning_effort=none<\/td><\/tr><tr style=\"background:#fff\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e0a\u0e38\u0e14<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Supported; separate processing mode with 50% Standard token rates<\/td><\/tr><tr style=\"background:#f2f8f7\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Audio \/ realtime \/ video<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Not supported on the model page<\/td><\/tr><tr style=\"background:#fff\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e01\u0e32\u0e23\u0e1b\u0e49\u0e2d\u0e19\u0e20\u0e32\u0e1e<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Supported; output remains text<\/td><\/tr><tr style=\"background:#f2f8f7\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e04\u0e48\u0e32\u0e2a\u0e39\u0e07\u0e2a\u0e38\u0e14\u0e17\u0e35\u0e48\u0e23\u0e31\u0e1a\u0e40\u0e02\u0e49\u0e32<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">922,000 tokens; do not confuse this with the 1.05M context window<\/td><\/tr><\/tbody><\/table><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">The current rate-limit table lists the following Standard tiers. RPM means requests per minute, TPM means tokens per minute, and the queue value is the batch-token limit.<\/p>\n\n\n\n<div style=\"overflow-x:auto;margin:24px 0;border:1px solid #b8d9d5;border-radius:8px\"><table style=\"width:100%;min-width:620px;border-collapse:collapse;background:#fff;color:#172a2d;font-family:Arial,sans-serif\"><thead><tr style=\"background:#087f8c;color:#fff\"><th style=\"padding:11px;text-align:left;vertical-align:top\">\u0e23\u0e30\u0e14\u0e31\u0e1a<\/th><th style=\"padding:11px;text-align:left;vertical-align:top\">RPM<\/th><th style=\"padding:11px;text-align:left;vertical-align:top\">TPM<\/th><th style=\"padding:11px;text-align:left;vertical-align:top\">\u0e04\u0e34\u0e27\u0e41\u0e1a\u0e1a\u0e01\u0e25\u0e38\u0e48\u0e21<\/th><\/tr><\/thead><tbody><tr style=\"background:#fff\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e23\u0e30\u0e14\u0e31\u0e1a 1<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">500<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">500,000<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">5,000,000<\/td><\/tr><tr style=\"background:#f2f8f7\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e23\u0e30\u0e14\u0e31\u0e1a 2<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">5,000<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">2,000,000<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">20,000,000<\/td><\/tr><tr style=\"background:#fff\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e23\u0e30\u0e14\u0e31\u0e1a 3<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">5,000<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">4,000,000<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">40,000,000<\/td><\/tr><tr style=\"background:#f2f8f7\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e23\u0e30\u0e14\u0e31\u0e1a 4<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">10,000<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">10,000,000<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">1,000,000,000<\/td><\/tr><tr style=\"background:#fff\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">\u0e23\u0e30\u0e14\u0e31\u0e1a 5<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">30,000<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">180,000,000<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">15,000,000,000<\/td><\/tr><\/tbody><\/table><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Before production rollout, validate the exact tier, endpoint, streaming mode, and tool contract in your own account. The <a href=\"https:\/\/www.glbgpt.com\/hub\/codex-usage-limits\/\">Codex usage limits guide<\/a> is useful context for why a model&#8217;s published ceiling is not the same as an account&#8217;s live allowance.<\/p>\n\n\n\n<h2 id=\"use-cases\" class=\"wp-block-heading\">What GPT-6 Luna&#8217;s specs imply<\/h2>\n\n\n\n<div style=\"overflow-x:auto;margin:24px 0;border:1px solid #b8d9d5;border-radius:8px\"><table style=\"width:100%;min-width:760px;border-collapse:collapse;background:#fff;color:#172a2d;font-family:Arial,sans-serif\"><thead><tr style=\"background:#087f8c;color:#fff\"><th style=\"padding:11px;text-align:left;vertical-align:top\">Reader job<\/th><th style=\"padding:11px;text-align:left;vertical-align:top\">\u0e17\u0e33\u0e44\u0e21\u0e16\u0e36\u0e07\u0e40\u0e2b\u0e21\u0e32\u0e30\u0e2a\u0e21<\/th><th style=\"padding:11px;text-align:left;vertical-align:top\">\u0e2a\u0e34\u0e48\u0e07\u0e17\u0e35\u0e48\u0e15\u0e49\u0e2d\u0e07\u0e15\u0e23\u0e27\u0e08\u0e2a\u0e2d\u0e1a<\/th><\/tr><\/thead><tbody><tr style=\"background:#fff\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Focused extraction<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">The provider positions Luna for focused, high-volume tasks at a low token rate.<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Define a schema and test malformed or missing fields.<\/td><\/tr><tr style=\"background:#f2f8f7\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Repeated summaries<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Low input, cache, and output prices make repeated work easier to budget.<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Confirm cache hits and output caps in your route.<\/td><\/tr><tr style=\"background:#fff\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Large input batches<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">922K maximum input and a 1.05M context support substantial source sets.<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Watch the 272K long-context multiplier.<\/td><\/tr><tr style=\"background:#f2f8f7\"><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Open-ended agent work<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">The API exposes the same broad tool vocabulary on paper.<\/td><td style=\"padding:10px;border-bottom:1px solid #d9e7e4;vertical-align:top\">Run a controlled task before assuming Sol-level quality or latency.<\/td><\/tr><\/tbody><\/table><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">This is a fit map, not a benchmark. Use the <a href=\"https:\/\/www.glbgpt.com\/hub\/all-in-one-ai-models\/\">all-in-one AI models workflow guide<\/a> when your real decision is how to switch models inside one repeatable process rather than which single model sounds strongest.<\/p>\n\n\n\n<h2 id=\"hands-on\" class=\"wp-block-heading\">Hands-on API test: three matched tasks<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">We ran the same three small tasks through the Broly Anywhere compatibility API at <code>https:\/\/anywhere.broly.ai\/v1\/chat\/completions<\/code> using model ID <code>gpt-6-luna<\/code>: a coding review, a long-context decision memo, and strict JSON extraction. These are useful workflow observations, not an official OpenAI benchmark or a universal ranking. The first two passes hit temporary HTTP 503 overload responses; after retrying, all three tasks completed at HTTP 200.<\/p>\n\n\n\n<figure style=\"box-sizing:border-box;margin:24px 0;padding:14px;border:1px solid #b8d9d5;border-top:5px solid #087f8c;border-radius:8px;background:#f2f8f7;color:#172a2d;font-family:Arial,sans-serif\"><img decoding=\"async\" src=\"https:\/\/static.futureshareai.com\/glb_features\/mcp\/4\/gpt-6-luna-api-test-capture_16bb65514b894d98b163c3f2c98903bb.webp\" width=\"1000\" height=\"1500\" alt=\"GPT-6 Luna three-task compatibility API test results\" style=\"display:block;width:100%;height:auto;border:1px solid #c9dfdb;border-radius:5px;background:#fff\"\/><figcaption style=\"margin:10px 2px 0;color:#4f696b;font-size:14px;line-height:1.55\">Observed output from the Broly Anywhere compatibility API on September 23, 2026. One scoped run per task; not an official OpenAI benchmark.<\/figcaption><\/figure>\n\n\n\n<section style=\"box-sizing:border-box;margin:24px 0;padding:18px;border:1px solid #b8d9d5;border-radius:8px;background:#f2f8f7;color:#172a2d;font-family:Arial,sans-serif\"><div style=\"margin-bottom:14px\"><strong style=\"display:block;color:#086b72;font-size:12px;letter-spacing:.05em;text-transform:uppercase\">\u0e04\u0e33\u0e2a\u0e31\u0e48\u0e07\u0e41\u0e25\u0e30\u0e1c\u0e25\u0e25\u0e31\u0e1e\u0e18\u0e4c\u0e17\u0e35\u0e48\u0e2a\u0e31\u0e07\u0e40\u0e01\u0e15\u0e44\u0e14\u0e49<\/strong><p style=\"margin:6px 0 0;color:#496667;line-height:1.6\">The complete JSON report keeps the full prompts, outputs, HTTP status, finish reason, latency, and usage fields. The cards below keep the same information editable in the WordPress source.<\/p><\/div><article style=\"margin:0 0 14px;padding:16px;border:1px solid #c9dfdb;border-left:5px solid #087f8c;background:#fff;color:#172a2d;font-family:Arial,sans-serif\"><div style=\"display:flex;flex-wrap:wrap;align-items:center;justify-content:space-between;gap:8px\"><strong style=\"color:#087f8c;font-size:12px;letter-spacing:.06em;text-transform:uppercase\">coding review<\/strong><span style=\"padding:5px 8px;background:#e1f1ed;color:#176c62;font-size:12px;font-weight:800\">HTTP 200 \u00b7 stop<\/span><\/div><h3 style=\"margin:7px 0 4px;font:700 19px\/1.25 Georgia,serif\">Coding review and patch plan<\/h3><p style=\"margin:0 0 10px;color:#537073;font-size:13px\">19944 ms \u00b7 378 total tokens \u00b7 204 reasoning tokens<\/p><div style=\"display:grid;grid-template-columns:repeat(auto-fit,minmax(min(320px,100%),1fr));gap:10px\"><div><strong style=\"display:block;margin-bottom:5px;color:#086b72;font-size:12px;text-transform:uppercase\">\u0e07\u0e32\u0e19<\/strong><pre style=\"box-sizing:border-box;min-height:112px;margin:0;padding:10px;border:1px solid #c9dfdb;background:#f6fbfa;color:#173135;font:12px\/1.5 Consolas,monospace;white-space:pre-wrap\">Review this small JavaScript function as if it were part of a production repository. Identify the bug, explain the failure mode, and propose a minimal patch plus two focused regression tests. Function: function uniqueSorted(values) { return [...new Set(values)].sort((a, b) =&gt; a - b).filter(Boolean); }<\/pre><\/div><div><strong style=\"display:block;margin-bottom:5px;color:#086b72;font-size:12px;text-transform:uppercase\">\u0e1c\u0e25\u0e25\u0e31\u0e1e\u0e18\u0e4c\u0e17\u0e35\u0e48\u0e2a\u0e31\u0e07\u0e40\u0e01\u0e15\u0e44\u0e14\u0e49<\/strong><pre style=\"box-sizing:border-box;min-height:112px;max-height:260px;overflow:auto;margin:0;padding:10px;border:1px solid #29474a;background:#17252a;color:#eaf6f3;font:12px\/1.5 Consolas,monospace;white-space:pre-wrap\">`filter(Boolean)` removes valid `0` values (and other falsy values) from the result. Remove it:\n\n```js\nfunction uniqueSorted(values) {\n  return [...new Set(values)].sort((a, b) =&gt; a - b);\n}\n```\n\nRegression tests:\n\n```js\nassert.deepEqual(uniqueSorted([0, 2, 1]), [0, 1, 2]);\nassert.deepEqual(uniqueSorted([2, 0, 2]), [0, 2]);\n```<\/pre><\/div><\/div><\/article><article style=\"margin:0 0 14px;padding:16px;border:1px solid #c9dfdb;border-left:5px solid #087f8c;background:#fff;color:#172a2d;font-family:Arial,sans-serif\"><div style=\"display:flex;flex-wrap:wrap;align-items:center;justify-content:space-between;gap:8px\"><strong style=\"color:#087f8c;font-size:12px;letter-spacing:.06em;text-transform:uppercase\">long context synthesis<\/strong><span style=\"padding:5px 8px;background:#e1f1ed;color:#176c62;font-size:12px;font-weight:800\">HTTP 200 \u00b7 stop<\/span><\/div><h3 style=\"margin:7px 0 4px;font:700 19px\/1.25 Georgia,serif\">Multi-source synthesis<\/h3><p style=\"margin:0 0 10px;color:#537073;font-size:13px\">17740 ms \u00b7 322 total tokens \u00b7 78 reasoning tokens<\/p><div style=\"display:grid;grid-template-columns:repeat(auto-fit,minmax(min(320px,100%),1fr));gap:10px\"><div><strong style=\"display:block;margin-bottom:5px;color:#086b72;font-size:12px;text-transform:uppercase\">\u0e07\u0e32\u0e19<\/strong><pre style=\"box-sizing:border-box;min-height:112px;margin:0;padding:10px;border:1px solid #c9dfdb;background:#f6fbfa;color:#173135;font:12px\/1.5 Consolas,monospace;white-space:pre-wrap\">Synthesize the following three notes into a concise decision memo with: decision, evidence, risks, and next step. Note A: A 1.05M-token context window can hold large source sets but does not make every request cheap. Note B: Above 272K input tokens, the documented input\/cache rates increase and output is multiplied for the full request. Note C: Retrieval and cache reuse can reduce repeated prompt cost, but acceptance criteria still need testing.<\/pre><\/div><div><strong style=\"display:block;margin-bottom:5px;color:#086b72;font-size:12px;text-transform:uppercase\">\u0e1c\u0e25\u0e25\u0e31\u0e1e\u0e18\u0e4c\u0e17\u0e35\u0e48\u0e2a\u0e31\u0e07\u0e40\u0e01\u0e15\u0e44\u0e14\u0e49<\/strong><pre style=\"box-sizing:border-box;min-height:112px;max-height:260px;overflow:auto;margin:0;padding:10px;border:1px solid #29474a;background:#17252a;color:#eaf6f3;font:12px\/1.5 Consolas,monospace;white-space:pre-wrap\">## Decision\nUse the 1.05M-token context window selectively; prefer retrieval and cache reuse for repeated or oversized prompts, subject to testing.\n\n## Evidence\nThe window can hold large source sets, but capacity does not make each request inexpensive. Above 272K input tokens, documented input\/cache rates increase, and output is multiplied for the full request. Retrieval and cache reuse may reduce repeated prompt costs.\n\n## Risks\nLong-context requests may cost more than expected. Savings from retrieval and caching are not established until they meet acceptance criteria.\n\n## Next step\nTest representative workloads against defined quality and cost acceptance criteria, including requests above 272K tokens and repeated-prompt scenarios.<\/pre><\/div><\/div><\/article><article style=\"margin:0 0 14px;padding:16px;border:1px solid #c9dfdb;border-left:5px solid #087f8c;background:#fff;color:#172a2d;font-family:Arial,sans-serif\"><div style=\"display:flex;flex-wrap:wrap;align-items:center;justify-content:space-between;gap:8px\"><strong style=\"color:#087f8c;font-size:12px;letter-spacing:.06em;text-transform:uppercase\">structured extraction<\/strong><span style=\"padding:5px 8px;background:#e1f1ed;color:#176c62;font-size:12px;font-weight:800\">HTTP 200 \u00b7 stop<\/span><\/div><h3 style=\"margin:7px 0 4px;font:700 19px\/1.25 Georgia,serif\">\u0e01\u0e32\u0e23\u0e2a\u0e01\u0e31\u0e14\u0e41\u0e1a\u0e1a\u0e21\u0e35\u0e42\u0e04\u0e23\u0e07\u0e2a\u0e23\u0e49\u0e32\u0e07<\/h3><p style=\"margin:0 0 10px;color:#537073;font-size:13px\">15360 ms \u00b7 127 total tokens \u00b7 26 reasoning tokens<\/p><div style=\"display:grid;grid-template-columns:repeat(auto-fit,minmax(min(320px,100%),1fr));gap:10px\"><div><strong style=\"display:block;margin-bottom:5px;color:#086b72;font-size:12px;text-transform:uppercase\">\u0e07\u0e32\u0e19<\/strong><pre style=\"box-sizing:border-box;min-height:112px;margin:0;padding:10px;border:1px solid #c9dfdb;background:#f6fbfa;color:#173135;font:12px\/1.5 Consolas,monospace;white-space:pre-wrap\">Extract the following fields from this brief and return valid JSON only with keys customer, priority, deadline, and unknowns. Brief: Customer: Northwind Labs. Priority: high. Deadline: 2026-10-04. The owner is not named and the budget is not confirmed.<\/pre><\/div><div><strong style=\"display:block;margin-bottom:5px;color:#086b72;font-size:12px;text-transform:uppercase\">\u0e1c\u0e25\u0e25\u0e31\u0e1e\u0e18\u0e4c\u0e17\u0e35\u0e48\u0e2a\u0e31\u0e07\u0e40\u0e01\u0e15\u0e44\u0e14\u0e49<\/strong><pre style=\"box-sizing:border-box;min-height:112px;max-height:260px;overflow:auto;margin:0;padding:10px;border:1px solid #29474a;background:#17252a;color:#eaf6f3;font:12px\/1.5 Consolas,monospace;white-space:pre-wrap\">{&quot;customer&quot;:&quot;Northwind Labs&quot;,&quot;priority&quot;:&quot;high&quot;,&quot;deadline&quot;:&quot;2026-10-04&quot;,&quot;unknowns&quot;:[&quot;owner&quot;,&quot;budget&quot;]}<\/pre><\/div><\/div><\/article><\/section>\n\n\n\n<p class=\"wp-block-paragraph\">In the coding task, GPT-6 Luna identified the falsy-value bug in <code>filter(Boolean)<\/code> and supplied a patch plus regression coverage; the completed run used 378 total tokens. In the long-context task, it kept the decision, evidence, risk, and next-step structure while carrying the 272K pricing caveat forward; the response used 322 total tokens. In the structured extraction task, it returned the requested customer, priority, deadline, and unknowns fields as JSON. Those are observed output shapes for this route, not evidence that one model is universally better.<\/p>\n\n\n\n<h2 id=\"reactions\" class=\"wp-block-heading\">What public reactions can and cannot tell you<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The following sources are included as attributed public reaction, not as official documentation or controlled benchmark evidence. Their titles show what each creator chose to test or explain; they do not establish a market-wide result.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Chase AI:<\/strong> <a href=\"https:\/\/www.youtube.com\/watch?v=U_sjnF3jvCo\">GPT 6 Sol &amp; Luna Are Here (And 50% CHEAPER!)<\/a>. Treat the framing and any demonstration as that creator&#8217;s experience, not as a universal score.<\/li>\n\n\n\n<li><strong>Rob The AI Guy:<\/strong> <a href=\"https:\/\/www.youtube.com\/watch?v=E50jlwkuKSs\">ChatGPT Released GPT-6 Sol &amp; GPT-6 Luna (New ChatGPT Models &amp; Agents)<\/a>. Treat the framing and any demonstration as that creator&#8217;s experience, not as a universal score.<\/li>\n\n\n\n<li><strong>AI with Surya:<\/strong> <a href=\"https:\/\/www.youtube.com\/watch?v=9TMLtJdV4_g\">GPT-6 Soul vs Luna vs Claude Opus 5.5: Which Should You Use?<\/a>. Treat the framing and any demonstration as that creator&#8217;s experience, not as a universal score.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">A separate naming issue matters here: several public videos use &#8220;GPT-6&#8221; and &#8220;GPT-5.6&#8221; interchangeably in titles. Keep the exact model ID visible when you reproduce a claim. The <a href=\"https:\/\/www.glbgpt.com\/hub\/gpt-6-astra-vs-gpt-5-6-sol\/\">\u0e01\u0e32\u0e23\u0e40\u0e1b\u0e23\u0e35\u0e22\u0e1a\u0e40\u0e17\u0e35\u0e22\u0e1a\u0e23\u0e30\u0e2b\u0e27\u0e48\u0e32\u0e07 GPT-6 Astra \u0e41\u0e25\u0e30 GPT-5.6 Sol<\/a> is a useful example of why provider identity, platform label, and route should be recorded separately.<\/p>\n\n\n\n<h2 id=\"globalgpt\" class=\"wp-block-heading\">GlobalGPT access and the GPT-5.6 naming note<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">GlobalGPT&#8217;s verified model catalog currently exposes <strong>GPT-5.6 Sol<\/strong> \u0e41\u0e25\u0e30 <strong>GPT-5.6 Luna<\/strong>. The exact OpenAI API IDs in this article are <code>gpt-6-luna<\/code> and belong to OpenAI&#8217;s official documentation. An exact <code>\/home\/gpt-6-luna<\/code> GlobalGPT route was not verified, so this article does not claim that the platform route is GPT-6 access.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For the current platform naming and separate credit context, see the <a href=\"https:\/\/www.glbgpt.com\/hub\/gpt-5-6-pricing\/\">\u0e04\u0e39\u0e48\u0e21\u0e37\u0e2d\u0e23\u0e32\u0e04\u0e32 GPT-5.6<\/a> \u0e41\u0e25\u0e30 <a href=\"https:\/\/www.glbgpt.com\/hub\/gpt-5-6-vs-fable-5-vs-gpt-5-5\/\">\u0e01\u0e32\u0e23\u0e40\u0e1b\u0e23\u0e35\u0e22\u0e1a\u0e40\u0e17\u0e35\u0e22\u0e1a\u0e23\u0e38\u0e48\u0e19 GPT-5.6<\/a>. Those pages are platform\/editorial context; they do not override OpenAI&#8217;s API pricing table.<\/p>\n\n\n\n<section style=\"box-sizing:border-box;margin:24px 0;padding:20px;border:1px solid #b8d9d5;border-top:5px solid #087f8c;border-radius:8px;background:#f2f8f7;color:#172a2d;font-family:Arial,sans-serif\"><strong style=\"display:block;color:#086b72;font-size:12px;text-transform:uppercase\">Verified platform route<\/strong><p style=\"margin:7px 0 14px;line-height:1.6\">This link opens GlobalGPT&#8217;s current platform naming, not a verified GPT-6 OpenAI model ID.<\/p><a href=\"https:\/\/www.glbgpt.com\/home?inviter=hub_popup&amp;login=1\" style=\"display:inline-block;padding:10px 14px;border-radius:5px;background:#087f8c;color:#fff;text-decoration:none;font-weight:800\">Open GlobalGPT model catalog<\/a><\/section>\n\n\n\n<h2 id=\"choice\" class=\"wp-block-heading\">Who should choose GPT-6 Luna?<\/h2>\n\n\n\n<section style=\"box-sizing:border-box;margin:24px 0;padding:18px;border:1px solid #b8d9d5;border-radius:8px;background:#f2f8f7;color:#172a2d;font-family:Arial,sans-serif\"><div style=\"display:grid;gap:10px\"><div style=\"padding:13px 15px;border-left:5px solid #087f8c;background:#fff\"><strong>Choose Luna for<\/strong><span style=\"display:block;margin-top:4px;line-height:1.6\">classification, extraction, structured drafting, recurring summaries, and other high-volume jobs with clear acceptance criteria.<\/span><\/div><div style=\"padding:13px 15px;border-left:5px solid #d36449;background:#fff\"><strong>Choose Sol instead when<\/strong><span style=\"display:block;margin-top:4px;line-height:1.6\">the task is open-ended, code-heavy, tool-heavy, or likely to benefit from a larger reasoning budget.<\/span><\/div><div style=\"padding:13px 15px;border-left:5px solid #087f8c;background:#fff\"><strong>Validate first when<\/strong><span style=\"display:block;margin-top:4px;line-height:1.6\">your workflow depends on audio, realtime sessions, legacy Completions, or an exact GPT-6 route inside GlobalGPT.<\/span><\/div><\/div><\/section>\n\n\n\n<p class=\"wp-block-paragraph\">If your shortlist includes lower-cost alternatives, compare the <a href=\"https:\/\/www.glbgpt.com\/hub\/best-ai-models\/\">broader model catalog<\/a> rather than assuming the newest model is automatically the best value. If you are evaluating data-heavy work, the <a href=\"https:\/\/www.glbgpt.com\/hub\/best-ai-tools-for-data-analysis-tested\/\">data-analysis model guide<\/a> provides a separate workflow lens.<\/p>\n\n\n\n<h2 id=\"faq\" class=\"wp-block-heading\">\u0e04\u0e33\u0e16\u0e32\u0e21\u0e17\u0e35\u0e48\u0e21\u0e31\u0e01\u0e16\u0e39\u0e01\u0e16\u0e32\u0e21<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">What is GPT-6 Luna?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">GPT-6 Luna is OpenAI&#8217;s focused, high-volume tasks model. Its official API model ID is gpt-6-luna.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How much does GPT-6 Luna cost?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">OpenAI lists GPT-6 Luna at $0.10 per 1M input tokens, $0.01 per 1M cached input tokens, $0.125 per 1M cache writes, and $0.50 per 1M output tokens at Standard rates.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What happens above 272K input tokens?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">When a request exceeds 272K input tokens, OpenAI applies 2x input and cache rates and 1.5x output rates to the full request.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What are the context and output limits?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">GPT-6 Luna lists a 1,050,000-token context window, a 922,000-token maximum input, and a 128,000-token maximum output.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What is the knowledge cutoff?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The current OpenAI model page lists May 18, 2026 as the knowledge cutoff for GPT-6 Luna.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Which reasoning settings are available?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The API documents none, low, medium, high, xhigh, and max reasoning effort, with medium as the default.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Which API endpoints are supported?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">GPT-6 Luna supports Chat Completions, Responses, and Batch. The model page does not list Realtime, Assistants, audio, video, image generation, embeddings, fine-tuning, moderation, or legacy Completions as supported.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Can GPT-6 Luna accept images?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes. The model page lists text and image input with text output. That does not make it an image-generation model.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Is GPT-6 Luna available in GlobalGPT?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">OpenAI&#8217;s official model name is GPT-6 Luna, but GlobalGPT currently exposes GPT-5.6 Sol and GPT-5.6 Luna labels. The exact GPT-6 GlobalGPT route is not verified, so this article does not claim GPT-6 access through GlobalGPT.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Does this article include a benchmark?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes, but the scope is narrow: three matched tasks through the Broly Anywhere compatibility API. The results are retained as one observed run and are not an official OpenAI benchmark or a universal winner claim.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u0e04\u0e33\u0e15\u0e31\u0e14\u0e2a\u0e34\u0e19\u0e2a\u0e38\u0e14\u0e17\u0e49\u0e32\u0e22<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Luna is the pragmatic cost-first option on the official price sheet: it keeps the same headline context and output ceilings as Sol while charging a fraction of the token rates. That makes it compelling for focused, repeated calls, but the low price is not proof of lower latency or lower quality without a controlled test. Recheck the official model and pricing pages on publication day before publishing.<\/p>\n\n\n\n<p role=\"note\" style=\"margin:26px 0;color:#567073;font:13px\/1.6 Arial,sans-serif\">Checked September 23, 2026. Official facts: OpenAI Developers. Platform route naming: GlobalGPT. Public reaction: linked creators and publications, attributed only.<\/p>\n\n\n\n<script type=\"application\/ld+json\">{\n    \"@context\": \"https:\\\/\\\/schema.org\",\n    \"@type\": \"FAQPage\",\n    \"mainEntity\": [\n        {\n            \"@type\": \"Question\",\n            \"name\": \"What is GPT-6 Luna?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"GPT-6 Luna is OpenAI's focused, high-volume tasks model. 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GlobalGPT currently lists GPT-5.6 Sol and GPT-5.6 Luna rather than exact GPT-6 routes, so the platform naming must [&hellip;]<\/p>","protected":false},"author":16,"featured_media":19725,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_seopress_robots_primary_cat":"","_seopress_titles_title":"GPT-6 Luna Review: Pricing, Context, API Limits, and Best Uses","_seopress_titles_desc":"GPT-6 Luna review with official pricing, 1.05M context, API limits, focused high-volume workflows, and a three-task Broly Anywhere compatibility API observation, plus the GPT-5.6 GlobalGPT naming caveat.","_seopress_robots_index":"","footnotes":""},"categories":[7],"tags":[],"class_list":["post-19723","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-chat"],"acf":[],"_links":{"self":[{"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/posts\/19723","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/users\/16"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/comments?post=19723"}],"version-history":[{"count":3,"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/posts\/19723\/revisions"}],"predecessor-version":[{"id":19736,"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/posts\/19723\/revisions\/19736"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/media\/19725"}],"wp:attachment":[{"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/media?parent=19723"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/categories?post=19723"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/tags?post=19723"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}