{
  "aboutHtml": "<p>DeepSeek V3.1 — DeepSeek kompaniyasining gibrid matn modeli: ayni nomlangan nashr turli chat shabloni sozlamalari orqali fikrlashli va fikrlashsiz javoblarni qo‘llaydi. Ishlab chiqaruvchi avvalgi modelga nisbatan vosita qo‘llanilishi, agent vazifalari va mulohaza samaradorligi yaxshilanganini ta’kidlaydi. U yozma ko‘rsatmalar va matnli kontekstni qabul qilib, javob, kod yoki vosita chaqiruvi tarkibini yaratadi. Dastlabki V3.1 identifikatsiyasi Terminus, V3.2 va V4 oilasidan alohida.</p><p>Model kartasi uning asos va keyingi o‘qitilgan nashrlari uchun jami 671 milliard parametr, 37 milliard faol parametr va 128K kontekstni ko‘rsatadi. V3.1 dastlabki V3 asosiga tayanadi va keyingi o‘qitishdan oldin uzun hujjatlar bilan kengaytirilgan o‘qitishdan o‘tadi. Uning ekspertlar aralashmasi va yashirin diqqat tuzilishi V3 arxitekturasiga amal qiladi. Bu loyihaviy faktlar uzun kontekstli matn profilini tushuntiradi; kontekst sonini alohida eng katta chiqish spetsifikatsiyasiga aylantirmaydi.</p><p>Fikrlashsiz rejim bevosita javob tayyorlaydi, fikrlash rejimi esa javobdan oldin mulohaza bosqichini boshlaydi. Ishlab chiqaruvchi shabloni bevosita javoblar uchun yopuvchi fikrlash belgisidan, joriy mulohaza navbati uchun esa ochuvchi fikrlash belgisidan foydalanadi. Ko‘p navbatli suhbatda avvalgi mulohaza tarkibi chiqarib tashlanadi, yakuniy javoblar va yopuvchi belgi esa kontekstda qoladi. Bu dastlabki V3.1 tarix qoidasidir; u vositaga oid xabarlar davomida mulohazani saqlaydigan keyingi V3.2 qoidasidan farq qiladi.</p><p>Standart vosita chaqirish fikrlashsiz rejimda hujjatlashtirilgan. Ilova funksiya nomlari, tavsiflari va parametr sxemalarini beradi; model ishlab chiqaruvchining vosita chaqiruvi ifodasida tanlangan vosita nomi va JSON argumentlarini qaytaradi. Amalni ilova bajaradi va natijasini qaytaradi. Vositalarga muayyan maqsadlar bering va qaytgan kuzatuvni chaqiruvga bog‘lang, shunda modelning keyingi qadami amal bo‘lgan degan taxmin emas, dalilga ega bo‘ladi.</p><p>Fikrlash rejimidagi qidiruv alohida maxsus qidiruv agenti formatidan foydalanadi. DeepSeek tashqi yoki dolzarb ma’lumot talab qiladigan savollar uchun foydalanuvchi taqdim etgan qidiruv vositasini, bir nechta navbatdagi takroriy chaqiruvlar va kuzatuvlar bilan hujjatlashtiradi. Shuning uchun qidiruv matnli javob mustaqil vebni ko‘rgan degan da’vo emas, modelni o‘rab turgan ish jarayonining qismidir. Qidiruv vazifasi boshlanishidan oldin savolni, manba mezonlarini va kerakli yakuniy javobni belgilang.</p><p>Kodlashda tegishli parcha yoki fayllarni, kuzatilgan xatti-harakatni, kutilgan xatti-harakatni va saqlanadigan cheklovlarni bering. Namunaviy topshiriq: “Berilgan kirish uchun ushbu nosozlik sababini aniqlang, aniq yo‘naltirilgan tuzatishni taklif qiling va o‘zgartirish qaysi shartni hisobga olishini tushuntiring.” Hujjat vazifasida manba bo‘limlarini belgilang va aniq talablar bilan taqqoslashni so‘rang. Yakuniy javobdan manba tasdiqlagan xulosani taklif etilgan keyingi amaldan ajratishni talab qiling.</p><p>Ishlab chiqaruvchi V3.1 modelini vaznlar va faollashuvlar uchun UE8M0 FP8 masshtab formatidan foydalanib o‘qitadi. Mahalliy tavsiyalarida to‘g‘ri ishlov berilishi zarur aniqlikka sezgir parametrlar ham ko‘rsatilgan. Bular har bir ilovaning tanlangan aniqligi yoki implementatsiyasi haqidagi da’vo emas, e’lon qilingan ishlab chiqaruvchi etalonining xususiyatlaridir. Uning asosida yaratilgan model, kvantlangan variant va keyingi Terminus nazorat nuqtasi nomlangan dastlabki V3.1 nashridan alohida tanlovlar bo‘lib qoladi.</p><p>Uzunroq ishni aniq savol, manba belgilari va belgilangan yakuniy material bilan tayyorlang. Suhbat, manba matni, mulohaza va chiqishni hujjatlashtirilgan kontekstga sig‘dirib rejalashtiring hamda vazifa kengaygan sari faqat tegishli dalillarni saqlang. Agent uchun ruxsat etilgan amallarni va natijani tugallangan deb belgilaydigan mezonni ko‘rsating. Foydali yakuniy material — kirishga va ilova amalda qaytargan kuzatuvlarga bog‘langan, tekshirish mumkin bo‘lgan matnli javob, kod taklifi yoki manba tasdiqlagan tahlil.</p>",
  "aboutText": "DeepSeek V3.1 — DeepSeek kompaniyasining gibrid matn modeli: ayni nomlangan nashr turli chat shabloni sozlamalari orqali fikrlashli va fikrlashsiz javoblarni qo‘llaydi. Ishlab chiqaruvchi avvalgi modelga nisbatan vosita qo‘llanilishi, agent vazifalari va mulohaza samaradorligi yaxshilanganini ta’kidlaydi. U yozma ko‘rsatmalar va matnli kontekstni qabul qilib, javob, kod yoki vosita chaqiruvi tarkibini yaratadi. Dastlabki V3.1 identifikatsiyasi Terminus, V3.2 va V4 oilasidan alohida.\n\nModel kartasi uning asos va keyingi o‘qitilgan nashrlari uchun jami 671 milliard parametr, 37 milliard faol parametr va 128K kontekstni ko‘rsatadi. V3.1 dastlabki V3 asosiga tayanadi va keyingi o‘qitishdan oldin uzun hujjatlar bilan kengaytirilgan o‘qitishdan o‘tadi. Uning ekspertlar aralashmasi va yashirin diqqat tuzilishi V3 arxitekturasiga amal qiladi. Bu loyihaviy faktlar uzun kontekstli matn profilini tushuntiradi; kontekst sonini alohida eng katta chiqish spetsifikatsiyasiga aylantirmaydi.\n\nFikrlashsiz rejim bevosita javob tayyorlaydi, fikrlash rejimi esa javobdan oldin mulohaza bosqichini boshlaydi. Ishlab chiqaruvchi shabloni bevosita javoblar uchun yopuvchi fikrlash belgisidan, joriy mulohaza navbati uchun esa ochuvchi fikrlash belgisidan foydalanadi. Ko‘p navbatli suhbatda avvalgi mulohaza tarkibi chiqarib tashlanadi, yakuniy javoblar va yopuvchi belgi esa kontekstda qoladi. Bu dastlabki V3.1 tarix qoidasidir; u vositaga oid xabarlar davomida mulohazani saqlaydigan keyingi V3.2 qoidasidan farq qiladi.\n\nStandart vosita chaqirish fikrlashsiz rejimda hujjatlashtirilgan. Ilova funksiya nomlari, tavsiflari va parametr sxemalarini beradi; model ishlab chiqaruvchining vosita chaqiruvi ifodasida tanlangan vosita nomi va JSON argumentlarini qaytaradi. Amalni ilova bajaradi va natijasini qaytaradi. Vositalarga muayyan maqsadlar bering va qaytgan kuzatuvni chaqiruvga bog‘lang, shunda modelning keyingi qadami amal bo‘lgan degan taxmin emas, dalilga ega bo‘ladi.\n\nFikrlash rejimidagi qidiruv alohida maxsus qidiruv agenti formatidan foydalanadi. DeepSeek tashqi yoki dolzarb ma’lumot talab qiladigan savollar uchun foydalanuvchi taqdim etgan qidiruv vositasini, bir nechta navbatdagi takroriy chaqiruvlar va kuzatuvlar bilan hujjatlashtiradi. Shuning uchun qidiruv matnli javob mustaqil vebni ko‘rgan degan da’vo emas, modelni o‘rab turgan ish jarayonining qismidir. Qidiruv vazifasi boshlanishidan oldin savolni, manba mezonlarini va kerakli yakuniy javobni belgilang.\n\nKodlashda tegishli parcha yoki fayllarni, kuzatilgan xatti-harakatni, kutilgan xatti-harakatni va saqlanadigan cheklovlarni bering. Namunaviy topshiriq: “Berilgan kirish uchun ushbu nosozlik sababini aniqlang, aniq yo‘naltirilgan tuzatishni taklif qiling va o‘zgartirish qaysi shartni hisobga olishini tushuntiring.” Hujjat vazifasida manba bo‘limlarini belgilang va aniq talablar bilan taqqoslashni so‘rang. Yakuniy javobdan manba tasdiqlagan xulosani taklif etilgan keyingi amaldan ajratishni talab qiling.\n\nIshlab chiqaruvchi V3.1 modelini vaznlar va faollashuvlar uchun UE8M0 FP8 masshtab formatidan foydalanib o‘qitadi. Mahalliy tavsiyalarida to‘g‘ri ishlov berilishi zarur aniqlikka sezgir parametrlar ham ko‘rsatilgan. Bular har bir ilovaning tanlangan aniqligi yoki implementatsiyasi haqidagi da’vo emas, e’lon qilingan ishlab chiqaruvchi etalonining xususiyatlaridir. Uning asosida yaratilgan model, kvantlangan variant va keyingi Terminus nazorat nuqtasi nomlangan dastlabki V3.1 nashridan alohida tanlovlar bo‘lib qoladi.\n\nUzunroq ishni aniq savol, manba belgilari va belgilangan yakuniy material bilan tayyorlang. Suhbat, manba matni, mulohaza va chiqishni hujjatlashtirilgan kontekstga sig‘dirib rejalashtiring hamda vazifa kengaygan sari faqat tegishli dalillarni saqlang. Agent uchun ruxsat etilgan amallarni va natijani tugallangan deb belgilaydigan mezonni ko‘rsating. Foydali yakuniy material — kirishga va ilova amalda qaytargan kuzatuvlarga bog‘langan, tekshirish mumkin bo‘lgan matnli javob, kod taklifi yoki manba tasdiqlagan tahlil.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">DS</span><h2 id=\"sheet-title\">DeepSeek: DeepSeek V3.1</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Yopish\">×</button></div><div class=\"idline\"><span>DeepSeek /</span><code>deepseek-v3.1-thinking</code><button type=\"button\" id=\"copy-id\">ID ni nusxalash</button></div><nav class=\"card-jump-nav\" aria-label=\"Model kartochkasi bo‘limlari\"><button type=\"button\" data-card-jump=\"overview\">Boshlash</button><button type=\"button\" data-card-jump=\"methods\">Kirish ma’lumotlari va usullar</button><button type=\"button\" data-card-jump=\"pricing\">Tariflar</button><button type=\"button\" data-card-jump=\"examples\">Namunalar</button><button type=\"button\" data-card-jump=\"results\">Natijalar</button><button type=\"button\" data-card-jump=\"aliases\">Taxalluslar</button></nav><div class=\"card-method-tabs\" role=\"tablist\" aria-label=\"API usullari\"><button type=\"button\" role=\"tab\" tabindex=\"0\" aria-controls=\"card-method-content\" aria-selected=\"true\" id=\"card-method-tab-0\" data-card-method=\"Ultra|chat|/v1/chat/completions|deepseek-v3.1-thinking\">Chat</button></div><div id=\"card-method-content\" role=\"tabpanel\" aria-labelledby=\"card-method-tab-0\"><section class=\"card-section\" data-card-section=\"overview\"><h3 tabindex=\"-1\">Boshlash</h3><p class=\"mdesc-full\">DeepSeek V3.1 — bitta nashrda fikrlash va bevosita javob rejimlari mavjud gibrid matn modeli. Uning 128K konteksti va yaxshilangan vosita qo‘llash profili kodlash, hujjat tahlili va ilova taqdim etgan qidiruvni qo‘llaydi; natija matn yoki taklif etilgan vosita chaqiruvlaridir.</p><dl class=\"card-io\" data-card-io><div><dt>Qabul qilinadigan kirish ma’lumotlari</dt><dd>Matn</dd></div><div><dt>Natijalar</dt><dd>Matn</dd></div></dl><details class=\"card-about\"><summary>Model haqida</summary><p class=\"note-s\" data-model-method-boundary>Bu tavsif model imkoniyatlarini yoritadi. Ailoot da tanlangan usul uchun mavjud kirish, chiqish va parametrlar quyida keltirilgan.</p><div class=\"mabout\" id=\"m-about\"><p>DeepSeek V3.1 — DeepSeek kompaniyasining gibrid matn modeli: ayni nomlangan nashr turli chat shabloni sozlamalari orqali fikrlashli va fikrlashsiz javoblarni qo‘llaydi. Ishlab chiqaruvchi avvalgi modelga nisbatan vosita qo‘llanilishi, agent vazifalari va mulohaza samaradorligi yaxshilanganini ta’kidlaydi. U yozma ko‘rsatmalar va matnli kontekstni qabul qilib, javob, kod yoki vosita chaqiruvi tarkibini yaratadi. Dastlabki V3.1 identifikatsiyasi Terminus, V3.2 va V4 oilasidan alohida.</p><p>Model kartasi uning asos va keyingi o‘qitilgan nashrlari uchun jami 671 milliard parametr, 37 milliard faol parametr va 128K kontekstni ko‘rsatadi. V3.1 dastlabki V3 asosiga tayanadi va keyingi o‘qitishdan oldin uzun hujjatlar bilan kengaytirilgan o‘qitishdan o‘tadi. Uning ekspertlar aralashmasi va yashirin diqqat tuzilishi V3 arxitekturasiga amal qiladi. Bu loyihaviy faktlar uzun kontekstli matn profilini tushuntiradi; kontekst sonini alohida eng katta chiqish spetsifikatsiyasiga aylantirmaydi.</p><p>Fikrlashsiz rejim bevosita javob tayyorlaydi, fikrlash rejimi esa javobdan oldin mulohaza bosqichini boshlaydi. Ishlab chiqaruvchi shabloni bevosita javoblar uchun yopuvchi fikrlash belgisidan, joriy mulohaza navbati uchun esa ochuvchi fikrlash belgisidan foydalanadi. Ko‘p navbatli suhbatda avvalgi mulohaza tarkibi chiqarib tashlanadi, yakuniy javoblar va yopuvchi belgi esa kontekstda qoladi. Bu dastlabki V3.1 tarix qoidasidir; u vositaga oid xabarlar davomida mulohazani saqlaydigan keyingi V3.2 qoidasidan farq qiladi.</p><p>Standart vosita chaqirish fikrlashsiz rejimda hujjatlashtirilgan. Ilova funksiya nomlari, tavsiflari va parametr sxemalarini beradi; model ishlab chiqaruvchining vosita chaqiruvi ifodasida tanlangan vosita nomi va JSON argumentlarini qaytaradi. Amalni ilova bajaradi va natijasini qaytaradi. Vositalarga muayyan maqsadlar bering va qaytgan kuzatuvni chaqiruvga bog‘lang, shunda modelning keyingi qadami amal bo‘lgan degan taxmin emas, dalilga ega bo‘ladi.</p><p>Fikrlash rejimidagi qidiruv alohida maxsus qidiruv agenti formatidan foydalanadi. DeepSeek tashqi yoki dolzarb ma’lumot talab qiladigan savollar uchun foydalanuvchi taqdim etgan qidiruv vositasini, bir nechta navbatdagi takroriy chaqiruvlar va kuzatuvlar bilan hujjatlashtiradi. Shuning uchun qidiruv matnli javob mustaqil vebni ko‘rgan degan da’vo emas, modelni o‘rab turgan ish jarayonining qismidir. Qidiruv vazifasi boshlanishidan oldin savolni, manba mezonlarini va kerakli yakuniy javobni belgilang.</p><p>Kodlashda tegishli parcha yoki fayllarni, kuzatilgan xatti-harakatni, kutilgan xatti-harakatni va saqlanadigan cheklovlarni bering. Namunaviy topshiriq: “Berilgan kirish uchun ushbu nosozlik sababini aniqlang, aniq yo‘naltirilgan tuzatishni taklif qiling va o‘zgartirish qaysi shartni hisobga olishini tushuntiring.” Hujjat vazifasida manba bo‘limlarini belgilang va aniq talablar bilan taqqoslashni so‘rang. Yakuniy javobdan manba tasdiqlagan xulosani taklif etilgan keyingi amaldan ajratishni talab qiling.</p><p>Ishlab chiqaruvchi V3.1 modelini vaznlar va faollashuvlar uchun UE8M0 FP8 masshtab formatidan foydalanib o‘qitadi. Mahalliy tavsiyalarida to‘g‘ri ishlov berilishi zarur aniqlikka sezgir parametrlar ham ko‘rsatilgan. Bular har bir ilovaning tanlangan aniqligi yoki implementatsiyasi haqidagi da’vo emas, e’lon qilingan ishlab chiqaruvchi etalonining xususiyatlaridir. Uning asosida yaratilgan model, kvantlangan variant va keyingi Terminus nazorat nuqtasi nomlangan dastlabki V3.1 nashridan alohida tanlovlar bo‘lib qoladi.</p><p>Uzunroq ishni aniq savol, manba belgilari va belgilangan yakuniy material bilan tayyorlang. Suhbat, manba matni, mulohaza va chiqishni hujjatlashtirilgan kontekstga sig‘dirib rejalashtiring hamda vazifa kengaygan sari faqat tegishli dalillarni saqlang. Agent uchun ruxsat etilgan amallarni va natijani tugallangan deb belgilaydigan mezonni ko‘rsating. Foydali yakuniy material — kirishga va ilova amalda qaytargan kuzatuvlarga bog‘langan, tekshirish mumkin bo‘lgan matnli javob, kod taklifi yoki manba tasdiqlagan tahlil.</p></div></details></section><section class=\"card-section\" data-card-section=\"methods\"><h3 tabindex=\"-1\">Ailoot da mavjud · kirish ma’lumotlari va usullar</h3><details class=\"card-method\" open><summary><b>Ailoot Ultra · OpenAI Chat</b> <code>POST /v1/chat/completions</code></summary><dl class=\"method-capabilities\"><div><dt>Kirish</dt><dd><code>text</code></dd></div><div><dt>Chiqish</dt><dd><code>text</code></dd></div><div><dt>Chiqish formati</dt><dd>json yoki SSE</dd></div></dl><div class=\"tw\" data-card-parameters data-typed-parameters><table><thead><tr><th>Maydon</th><th>Tur</th><th>Talab</th><th>Qiymatlar va cheklovlar</th><th>Standart</th></tr></thead><tbody><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>string</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Majburiy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>deepseek-v3.1-thinking</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">messages</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>message[]</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Majburiy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Elementlarning eng kam soni: 1</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"model_dependent\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">messages[].role</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>string</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Majburiy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div class=\"note-s\">Modelga bogʻliq</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">messages[].content</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>string</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Quyidagi holatda qo‘llanadi: Matnli suhbat</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">stream</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>boolean</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span></td><td data-cell-label=\"Standart\" data-default=\"known\"><span class=\"card-cell-label\">Standart</span>false</td></tr></tbody></table></div><details class=\"card-about\" data-material-combinations><summary>Kirish materiallari birikmalari</summary><p class=\"note-s\"><b>Matn</b> · UTF8 matni</p><p class=\"note-s\"><code>messages[].content</code></p></details><details class=\"note-s\" data-method-contract><summary>Usul parametrlari va hayot sikli</summary><div class=\"tw\" data-card-table=\"contract\"><table><thead><tr><th>Parametr</th><th>Parametr talablari</th></tr></thead><tbody><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>content_type</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>application/json</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>required</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>model, messages</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>material_types</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>text</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>messages</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>role/content tuzilishidagi xabarlar massivi</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>file_material</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>Haqiqiy UTF-8 hujjati aynan matn sifatida kiritiladi; fayl IDsi faraz qilinmaydi</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>optional_controls</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>stream</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>control_scope</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>Matnli suhbat; stream mantiqiy qiymat bo‘lib, standart holatda false.</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>output_modalities</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>text</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>hosted_operations</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>false</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>multimodal_input_binding</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>null</td></tr></tbody></table></div><p>Usul taxalluslari: <code>/chat/completions</code></p><p>Hayot sikli: <code>synchronous_or_streamed</code></p><p class=\"note-s\">Aniq model IDsini messages bilan yuboring.</p><p class=\"note-s\">Kontekst uzunligi chiqishning yuqori chegarasi emas.</p><p class=\"note-s\">Matn fayli aynan matnni taqdim etishi mumkin; tasvir, audio va fayl obyekti kirishlari aniq bog‘lashni talab qiladi.</p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">Tariflar</h3><div data-native-semantic-method=\"v1\" data-tariff-reference-only><p class=\"note-s\" data-rate-support-note>Tarif ma’lumotlari so‘rov imkoniyatlarini qo‘shmaydi. Qo‘llab-quvvatlanadigan parametrlar tanlangan usulda ko‘rsatilgan.</p><dl data-price-conditions><dt>Tezkor rejim</dt><dd>Ko‘rsatilmagan <span class=\"note-s\">Tezkor rejim tanlanganda</span></dd><dt>Flex rejimi</dt><dd>Ko‘rsatilmagan <span class=\"note-s\">flex rejimi tanlanganda</span></dd><dt>Maksimal fikrlash darajasi</dt><dd>Ko‘rsatilmagan <span class=\"note-s\">Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda</span></dd></dl></div><div data-declared-tariff-pricing><h4>Ailoot tariflari</h4><h4>Sozlamalar bo‘yicha narxlar</h4><div class=\"tw\" data-pricing-profiles><table><thead><tr><th>Sozlama</th><th>Tariflar jadvali</th><th>To‘lov</th></tr></thead><tbody><tr><th data-cell-label=\"Sozlama\" scope=\"row\"><span class=\"card-cell-label\">Sozlama</span>Tokenlar uchun bazaviy tariflar</th><td data-cell-label=\"Tariflar jadvali\"><span class=\"card-cell-label\">Tariflar jadvali</span>Asosiy tarif</td><td data-cell-label=\"To‘lov\"><span class=\"card-cell-label\">To‘lov</span><div><span>Kirish · Kirish tokenlari</span> <b>$0.000572</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Chiqish · Chiqish tokenlari</span> <b>$0.0023</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshdan o‘qish</span> <b>$0</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshga yozish</span> <b>$0</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshga yozish, 1 soat</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. <a href=\"/docs/uz/billing/reserve/\">Summani band qilish va to‘lovni hisoblash tartibi →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">So‘rov namunalari</h3><h4>Ailoot Ultra · OpenAI Chat</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Kod variantlari\"><button class=\"cg-tab\" id=\"model-code-1215-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1215-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl</button><button class=\"cg-tab\" id=\"model-code-1215-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1215-panel-1\" aria-selected=\"false\" tabindex=\"-1\">Python SDK</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Kodni nusxalash\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-1215-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-1215-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;:&quot;deepseek-v3.1-thinking&quot;,&quot;messages&quot;:[{&quot;role&quot;:&quot;user&quot;,&quot;content&quot;:&quot;hello&quot;}],&quot;stream&quot;:false}&#39;)\n\nPath(&quot;request.json&quot;).write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n  -H &quot;Authorization: Bearer $AILOOT_API_KEY&quot; -H &#39;Content-Type: application/json&#39; \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - &lt;&lt;&#39;PY_RESULT&#39;\nimport json\nfrom pathlib import Path\nr=json.loads(Path(&quot;result.json&quot;).read_text())\nfor x in r.get(&quot;choices&quot;,[]):\n    if x.get(&quot;message&quot;,{}).get(&quot;content&quot;): print(x[&quot;message&quot;][&quot;content&quot;])\nPY_RESULT\n</code></pre></div><div class=\"cg-pane\" id=\"model-code-1215-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-1215-tab-1\" hidden><pre data-lang=\"python\"><code>from openai import OpenAI\nimport os\n\nif not os.environ.get(&quot;AILOOT_API_KEY&quot;, &quot;&quot;).strip(): raise SystemExit(&quot;Set AILOOT_API_KEY before making a request&quot;)\nclient=OpenAI(base_url=&quot;https://api.ailoot.ai/v1&quot;,api_key=os.environ[&quot;AILOOT_API_KEY&quot;],max_retries=0)\nresult=client.chat.completions.create(model=&quot;deepseek-v3.1-thinking&quot;, messages=[{&quot;role&quot;:&quot;user&quot;,&quot;content&quot;:&quot;hello&quot;}], stream=False)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem=&quot;result&quot;):\n    # Keep every field, including usage, IDs, reasoning and tool requests.\n    Path(stem + &quot;.json&quot;).write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding=&quot;utf-8&quot;)\n    print(json.dumps(data, ensure_ascii=False, indent=2))\n    def save_blob(encoded, suffix, label):\n        raw = base64.b64decode(encoded, altchars=b&quot;-_&quot;, validate=True)\n        ext = &quot;png&quot; if raw.startswith(b&quot;\\x89PNG\\r\\n\\x1a\\n&quot;) else &quot;jpg&quot; if raw.startswith(b&quot;\\xff\\xd8&quot;) else &quot;webp&quot; if raw[:4] == b&quot;RIFF&quot; and raw[8:12] == b&quot;WEBP&quot; else suffix\n        file = Path(stem + &quot;-&quot; + label + &quot;.&quot; + ext)\n        file.write_bytes(raw)\n        print(&quot;Saved&quot;, file)\n    for index, block in enumerate(data.get(&quot;content&quot;) or []):\n        print(&quot;Content&quot;, index, json.dumps(block, ensure_ascii=False))\n    for choice in data.get(&quot;choices&quot;) or []:\n        print(&quot;Choice&quot;, choice.get(&quot;index&quot;), json.dumps(choice.get(&quot;message&quot;, choice.get(&quot;delta&quot;, {})), ensure_ascii=False))\n    for index, item in enumerate(data.get(&quot;output&quot;) or []):\n        print(&quot;Output&quot;, index, json.dumps(item, ensure_ascii=False))\n        if item.get(&quot;type&quot;) == &quot;image_generation_call&quot; and item.get(&quot;result&quot;):\n            save_blob(item[&quot;result&quot;], item.get(&quot;output_format&quot;) or &quot;bin&quot;, &quot;output-&quot; + str(index))\n    for ci, candidate in enumerate(data.get(&quot;candidates&quot;) or []):\n        for pi, part in enumerate((candidate.get(&quot;content&quot;) or {}).get(&quot;parts&quot;) or []):\n            print(&quot;Candidate/part&quot;, ci, pi, json.dumps(part, ensure_ascii=False))\n            blob = part.get(&quot;inlineData&quot;) or part.get(&quot;inline_data&quot;)\n            if blob and blob.get(&quot;data&quot;):\n                mime = blob.get(&quot;mimeType&quot;) or blob.get(&quot;mime_type&quot;)\n                suffix = {&quot;image/png&quot;:&quot;png&quot;, &quot;image/jpeg&quot;:&quot;jpg&quot;, &quot;image/webp&quot;:&quot;webp&quot;, &quot;audio/wav&quot;:&quot;wav&quot;, &quot;audio/mpeg&quot;:&quot;mp3&quot;, &quot;video/mp4&quot;:&quot;mp4&quot;}.get(mime, &quot;bin&quot;)\n                save_blob(blob[&quot;data&quot;], suffix, &quot;candidate-&quot; + str(ci) + &quot;-part-&quot; + str(pi))\n\nsave_result(result.to_dict(mode=&quot;json&quot;))\n</code></pre></div></div></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Natijalarni olish va saqlash</h3><h4>Ailoot Ultra · OpenAI Chat</h4><p class=\"note-s\">json yoki SSE</p><div class=\"card-result-process\" data-all-results><h5>Barcha tayyor natijalarni saqlash</h5><p class=\"note-s\">Qaytarilgan har bir to‘liq javob, fayl va kontent qismini saqlang.</p><p class=\"note-s\">Har bir javob variantini va uning kontenti, vosita chaqiruvlari hamda fikrlash ma’lumotlarini saqlang. SSEda o‘zgarish qismlarini variant va vosita indeksi bo‘yicha yig‘ing, yakuniy tugash sababi va sarf ma’lumotlarini saqlang.</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>Asl javobni tekshirish kerak bo‘lsa</h5><p class=\"note-s\">So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan.</p></div><details class=\"card-about\" data-output-fields><summary>Javob maydonlari</summary><div class=\"tw\" data-card-table=\"output\"><table><thead><tr><th>Maydon</th><th>Tur</th></tr></thead><tbody><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">choices[].index</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>integer</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">choices[].message.content</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>string|null</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">choices[].message.tool_calls</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>tool_call[]</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">choices[].message.reasoning_content</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>string</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">choices[].message.reasoning_details</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>object|array</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">choices[].finish_reason</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>string|null</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">usage</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>object</td></tr></tbody></table></div></details><details class=\"card-about\" data-method-errors><summary>Xatolar va keyingi qadamlar</summary><div class=\"tw\" data-card-table=\"errors\"><table><thead><tr><th>Javob</th><th>Shart</th><th>Keyingi qadam</th></tr></thead><tbody><tr><th data-cell-label=\"Javob\" scope=\"row\"><span class=\"card-cell-label\">Javob</span>400 · <code>invalid_request_error</code></th><td data-cell-label=\"Shart\"><span class=\"card-cell-label\">Shart</span>Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri</td><td data-cell-label=\"Keyingi qadam\"><span class=\"card-cell-label\">Keyingi qadam</span>Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.</td></tr></tbody></table></div></details></section><section class=\"card-section\" data-card-section=\"aliases\"><h3 tabindex=\"-1\">Taxalluslar</h3><h4>Model taxalluslari</h4><p class=\"note-s\"><span data-model-alias-route=\"Ultra\"><b>Ailoot Ultra</b> <a href=\"#m=deepseek-v3.1-thinking&amp;route=Ultra&amp;caller=deepseek-v3.1-thinking&amp;fmt=chat\"><code>deepseek-v3.1-thinking</code></a></span></p><h4>Usul yo‘llari taxalluslari</h4><p class=\"note-s\"><code>/chat/completions</code></p><h4>Chaqirish uchun tegishli ID lar</h4><p class=\"note-s\"><a href=\"#m=deepseek-v3.1-thinking&amp;route=Ultra&amp;caller=deepseek-v3.1-thinking&amp;fmt=chat\"><code>deepseek-v3.1-thinking</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "deepseek-v3.1-thinking",
        "fmt": "chat",
        "http": "POST",
        "path": "/v1/chat/completions",
        "request_model_id": "deepseek-v3.1-thinking",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|chat|/v1/chat/completions|deepseek-v3.1-thinking",
      "ordinal": 0,
      "rawMethod": {
        "available": null,
        "cache_read": 0,
        "cache_write": 0,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.",
            "authentication": {
              "api_key_source": "AILOOT_API_KEY",
              "header": "Authorization: Bearer"
            },
            "errors": [
              {
                "code": null,
                "condition": "Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri",
                "http_status": 400,
                "recovery": "Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [
              "Model context is not an output cap.",
              "Send the conversation as text messages. Stream must be a boolean; omitted stream defaults to false."
            ],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/chat/completions"
              },
              "job_read": null,
              "mode": "synchronous_or_streamed",
              "unknown": "So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan."
            },
            "material_combinations": [
              {
                "mode": "text",
                "representation": "messages[].content",
                "required_material": "UTF8 matni"
              }
            ],
            "operation_modes": [
              "conversation"
            ],
            "outputs": {
              "all_results": "Har bir javob variantini va uning kontenti, vosita chaqiruvlari hamda fikrlash ma’lumotlarini saqlang. SSEda o‘zgarish qismlarini variant va vosita indeksi bo‘yicha yig‘ing, yakuniy tugash sababi va sarf ma’lumotlarini saqlang.",
              "family": "text",
              "fields": [
                {
                  "description": null,
                  "name": "choices[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "choices[].message.content",
                  "type": "string|null"
                },
                {
                  "description": null,
                  "name": "choices[].message.tool_calls",
                  "type": "tool_call[]"
                },
                {
                  "description": null,
                  "name": "choices[].message.reasoning_content",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "choices[].message.reasoning_details",
                  "type": "object|array"
                },
                {
                  "description": null,
                  "name": "choices[].finish_reason",
                  "type": "string|null"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json yoki SSE"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "deepseek-v3.1-thinking"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "messages",
                "required": true,
                "type": "message[]"
              },
              {
                "availability": "model_dependent",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "messages[].role",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "messages[].content",
                "required": false,
                "type": "string",
                "when": "Matnli suhbat"
              },
              {
                "availability": "source_interface",
                "default": false,
                "default_known": true,
                "enum": null,
                "enum_known": false,
                "name": "stream",
                "required": false,
                "type": "boolean"
              }
            ],
            "profile_binding": "Text conversation at the exact model ID and method path.",
            "unsupported": [
              "additional models/models list",
              "plugins",
              "route",
              "hosted tools; ordinary client function tools remain separate",
              "non-text output modalities",
              "provider.options overrides",
              "one-hour prompt-cache TTL"
            ]
          },
          "hints": [
            "Aniq model IDsini messages bilan yuboring.",
            "Kontekst uzunligi chiqishning yuqori chegarasi emas.",
            "Matn fayli aynan matnni taqdim etishi mumkin; tasvir, audio va fayl obyekti kirishlari aniq bog‘lashni talab qiladi."
          ],
          "input": {
            "content_type": "application/json",
            "control_scope": "Matnli suhbat; stream mantiqiy qiymat bo‘lib, standart holatda false.",
            "file_material": "Haqiqiy UTF-8 hujjati aynan matn sifatida kiritiladi; fayl IDsi faraz qilinmaydi",
            "hosted_operations": false,
            "material_types": [
              "text"
            ],
            "messages": "role/content tuzilishidagi xabarlar massivi",
            "multimodal_input_binding": null,
            "optional_controls": [
              "stream"
            ],
            "output_modalities": [
              "text"
            ],
            "required": [
              "model",
              "messages"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_or_streamed",
            "unknown": "preserve original request id; no generic chat job retrieval path"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/chat/completions"
          },
          "method_aliases": [
            "/chat/completions"
          ],
          "operation_modes": [
            "text_chat"
          ],
          "output": {
            "content": "choices[].message.content or stream delta",
            "family": "text",
            "format": "json or supported SSE",
            "tool_calls": "retain actual returned function calls"
          }
        },
        "ctx": null,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Tokenlar uchun bazaviy tariflar",
            "units": [
              {
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                "label": "Input tokens",
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                "key": "cache_read_price",
                "label": "Cache read",
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                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": "0",
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                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
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                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
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        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\":\"deepseek-v3.1-thinking\",\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}],\"stream\":false}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"choices\",[]):\n    if x.get(\"message\",{}).get(\"content\"): print(x[\"message\"][\"content\"])\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nif not os.environ.get(\"AILOOT_API_KEY\", \"\").strip(): raise SystemExit(\"Set AILOOT_API_KEY before making a request\")\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.chat.completions.create(model=\"deepseek-v3.1-thinking\", messages=[{\"role\":\"user\",\"content\":\"hello\"}], stream=False)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem=\"result\"):\n    # Keep every field, including usage, IDs, reasoning and tool requests.\n    Path(stem + \".json\").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n    print(json.dumps(data, ensure_ascii=False, indent=2))\n    def save_blob(encoded, suffix, label):\n        raw = base64.b64decode(encoded, altchars=b\"-_\", validate=True)\n        ext = \"png\" if raw.startswith(b\"\\x89PNG\\r\\n\\x1a\\n\") else \"jpg\" if raw.startswith(b\"\\xff\\xd8\") else \"webp\" if raw[:4] == b\"RIFF\" and raw[8:12] == b\"WEBP\" else suffix\n        file = Path(stem + \"-\" + label + \".\" + ext)\n        file.write_bytes(raw)\n        print(\"Saved\", file)\n    for index, block in enumerate(data.get(\"content\") or []):\n        print(\"Content\", index, json.dumps(block, ensure_ascii=False))\n    for choice in data.get(\"choices\") or []:\n        print(\"Choice\", choice.get(\"index\"), json.dumps(choice.get(\"message\", choice.get(\"delta\", {})), ensure_ascii=False))\n    for index, item in enumerate(data.get(\"output\") or []):\n        print(\"Output\", index, json.dumps(item, ensure_ascii=False))\n        if item.get(\"type\") == \"image_generation_call\" and item.get(\"result\"):\n            save_blob(item[\"result\"], item.get(\"output_format\") or \"bin\", \"output-\" + str(index))\n    for ci, candidate in enumerate(data.get(\"candidates\") or []):\n        for pi, part in enumerate((candidate.get(\"content\") or {}).get(\"parts\") or []):\n            print(\"Candidate/part\", ci, pi, json.dumps(part, ensure_ascii=False))\n            blob = part.get(\"inlineData\") or part.get(\"inline_data\")\n            if blob and blob.get(\"data\"):\n                mime = blob.get(\"mimeType\") or blob.get(\"mime_type\")\n                suffix = {\"image/png\":\"png\", \"image/jpeg\":\"jpg\", \"image/webp\":\"webp\", \"audio/wav\":\"wav\", \"audio/mpeg\":\"mp3\", \"video/mp4\":\"mp4\"}.get(mime, \"bin\")\n                save_blob(blob[\"data\"], suffix, \"candidate-\" + str(ci) + \"-part-\" + str(pi))\n\nsave_result(result.to_dict(mode=\"json\"))\n",
            "label": "Python SDK · all choices",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "chat",
        "hints": [
          "Send exact model ID with messages.",
          "Context length is not an output cap.",
          "A text file may supply literal text; image/audio/file-object inputs need exact binding."
        ],
        "http": "POST",
        "in": 0.572,
        "input": [
          "text"
        ],
        "label": "OpenAI Chat",
        "max_out": null,
        "max_reasoning_effort_multiplier": null,
        "method_aliases": [
          "/chat/completions"
        ],
        "out": 2.3,
        "output": [
          "text"
        ],
        "path": "/v1/chat/completions",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Not specified",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Not specified",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Not specified",
            "when": "When maximum reasoning effort uses the default adjustment"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
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                "amount": "0.000572000",
                "amount_unit": "usd_per_thousand_tokens",
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                "quantity_is_estimate": false,
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                "side": "in",
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              },
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                "amount": "0.0023000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
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                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
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                "amount": "0",
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                "estimate_only": false,
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                "label": "Cache write",
                "native_amount_usd_per_unit": "0",
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                "rate_role": "typed_native_retail_rate",
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                "billing_input": true,
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            ]
          }
        ],
        "request_model_id": "deepseek-v3.1-thinking",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
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    }
  ],
  "model_id": "deepseek-v3.1-thinking",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "f759593585116eafa7c5e4f05ba1f260057e89627059df0b4836d7b2e99fa766",
    "input_html_sha256": "6cd702c369c75beb077f5d7ef967566cb386d4c1fa214afdf1995eed265a127f",
    "private_audit_sha256": "2b1e967e48228ec9c64c5f9fbbfea715c539b8eae099739483d81f45424ecdba",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/uz/deepseek-v3-1-thinking/",
    "description": "DeepSeek V3.1 — bitta nashrda fikrlash va bevosita javob rejimlari mavjud gibrid matn modeli. Uning 128K konteksti va yaxshilangan vosita qo‘llash profili kodlash, hujjat tahlili va ilova taqdim etgan qidiruvni qo‘llaydi; natija matn yoki taklif etilgan vosita chaqiruvlaridir.",
    "locale": "uz",
    "md_url": "https://ailoot.ai/models/uz/deepseek-v3-1-thinking/index.md",
    "title": "DeepSeek: DeepSeek V3.1 — Ailoot",
    "url": "https://ailoot.ai/models/uz/deepseek-v3-1-thinking/"
  },
  "rawModel": {
    "about": "DeepSeek V3.1 — DeepSeek kompaniyasining gibrid matn modeli: ayni nomlangan nashr turli chat shabloni sozlamalari orqali fikrlashli va fikrlashsiz javoblarni qo‘llaydi. Ishlab chiqaruvchi avvalgi modelga nisbatan vosita qo‘llanilishi, agent vazifalari va mulohaza samaradorligi yaxshilanganini ta’kidlaydi. U yozma ko‘rsatmalar va matnli kontekstni qabul qilib, javob, kod yoki vosita chaqiruvi tarkibini yaratadi. Dastlabki V3.1 identifikatsiyasi Terminus, V3.2 va V4 oilasidan alohida.\n\nModel kartasi uning asos va keyingi o‘qitilgan nashrlari uchun jami 671 milliard parametr, 37 milliard faol parametr va 128K kontekstni ko‘rsatadi. V3.1 dastlabki V3 asosiga tayanadi va keyingi o‘qitishdan oldin uzun hujjatlar bilan kengaytirilgan o‘qitishdan o‘tadi. Uning ekspertlar aralashmasi va yashirin diqqat tuzilishi V3 arxitekturasiga amal qiladi. Bu loyihaviy faktlar uzun kontekstli matn profilini tushuntiradi; kontekst sonini alohida eng katta chiqish spetsifikatsiyasiga aylantirmaydi.\n\nFikrlashsiz rejim bevosita javob tayyorlaydi, fikrlash rejimi esa javobdan oldin mulohaza bosqichini boshlaydi. Ishlab chiqaruvchi shabloni bevosita javoblar uchun yopuvchi fikrlash belgisidan, joriy mulohaza navbati uchun esa ochuvchi fikrlash belgisidan foydalanadi. Ko‘p navbatli suhbatda avvalgi mulohaza tarkibi chiqarib tashlanadi, yakuniy javoblar va yopuvchi belgi esa kontekstda qoladi. Bu dastlabki V3.1 tarix qoidasidir; u vositaga oid xabarlar davomida mulohazani saqlaydigan keyingi V3.2 qoidasidan farq qiladi.\n\nStandart vosita chaqirish fikrlashsiz rejimda hujjatlashtirilgan. Ilova funksiya nomlari, tavsiflari va parametr sxemalarini beradi; model ishlab chiqaruvchining vosita chaqiruvi ifodasida tanlangan vosita nomi va JSON argumentlarini qaytaradi. Amalni ilova bajaradi va natijasini qaytaradi. Vositalarga muayyan maqsadlar bering va qaytgan kuzatuvni chaqiruvga bog‘lang, shunda modelning keyingi qadami amal bo‘lgan degan taxmin emas, dalilga ega bo‘ladi.\n\nFikrlash rejimidagi qidiruv alohida maxsus qidiruv agenti formatidan foydalanadi. DeepSeek tashqi yoki dolzarb ma’lumot talab qiladigan savollar uchun foydalanuvchi taqdim etgan qidiruv vositasini, bir nechta navbatdagi takroriy chaqiruvlar va kuzatuvlar bilan hujjatlashtiradi. Shuning uchun qidiruv matnli javob mustaqil vebni ko‘rgan degan da’vo emas, modelni o‘rab turgan ish jarayonining qismidir. Qidiruv vazifasi boshlanishidan oldin savolni, manba mezonlarini va kerakli yakuniy javobni belgilang.\n\nKodlashda tegishli parcha yoki fayllarni, kuzatilgan xatti-harakatni, kutilgan xatti-harakatni va saqlanadigan cheklovlarni bering. Namunaviy topshiriq: “Berilgan kirish uchun ushbu nosozlik sababini aniqlang, aniq yo‘naltirilgan tuzatishni taklif qiling va o‘zgartirish qaysi shartni hisobga olishini tushuntiring.” Hujjat vazifasida manba bo‘limlarini belgilang va aniq talablar bilan taqqoslashni so‘rang. Yakuniy javobdan manba tasdiqlagan xulosani taklif etilgan keyingi amaldan ajratishni talab qiling.\n\nIshlab chiqaruvchi V3.1 modelini vaznlar va faollashuvlar uchun UE8M0 FP8 masshtab formatidan foydalanib o‘qitadi. Mahalliy tavsiyalarida to‘g‘ri ishlov berilishi zarur aniqlikka sezgir parametrlar ham ko‘rsatilgan. Bular har bir ilovaning tanlangan aniqligi yoki implementatsiyasi haqidagi da’vo emas, e’lon qilingan ishlab chiqaruvchi etalonining xususiyatlaridir. Uning asosida yaratilgan model, kvantlangan variant va keyingi Terminus nazorat nuqtasi nomlangan dastlabki V3.1 nashridan alohida tanlovlar bo‘lib qoladi.\n\nUzunroq ishni aniq savol, manba belgilari va belgilangan yakuniy material bilan tayyorlang. Suhbat, manba matni, mulohaza va chiqishni hujjatlashtirilgan kontekstga sig‘dirib rejalashtiring hamda vazifa kengaygan sari faqat tegishli dalillarni saqlang. Agent uchun ruxsat etilgan amallarni va natijani tugallangan deb belgilaydigan mezonni ko‘rsating. Foydali yakuniy material — kirishga va ilova amalda qaytargan kuzatuvlarga bog‘langan, tekshirish mumkin bo‘lgan matnli javob, kod taklifi yoki manba tasdiqlagan tahlil.",
    "alias_of": null,
    "aliases": [],
    "author": "DeepSeek",
    "capabilities": {
      "context_length": 163840,
      "manufacturer_input": [
        "text"
      ],
      "manufacturer_output": [
        "text"
      ]
    },
    "card_routes": [
      {
        "label": "Ultra",
        "rates": [
          {
            "amount": "0.000572000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.000000572",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0.0023000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0.0000023",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          }
        ],
        "summary": "Konfiguratsiya narxlari tafsilotlarda to‘liq ko‘rsatilgan."
      }
    ],
    "ctx": null,
    "formats": [
      "chat"
    ],
    "id": "deepseek-v3.1-thinking",
    "input": [
      "text"
    ],
    "kind": "text",
    "max_out": null,
    "methods": [
      {
        "available": null,
        "cache_read": 0,
        "cache_write": 0,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.",
            "authentication": {
              "api_key_source": "AILOOT_API_KEY",
              "header": "Authorization: Bearer"
            },
            "errors": [
              {
                "code": null,
                "condition": "Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri",
                "http_status": 400,
                "recovery": "Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [
              "Model context is not an output cap.",
              "Send the conversation as text messages. Stream must be a boolean; omitted stream defaults to false."
            ],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/chat/completions"
              },
              "job_read": null,
              "mode": "synchronous_or_streamed",
              "unknown": "So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan."
            },
            "material_combinations": [
              {
                "mode": "text",
                "representation": "messages[].content",
                "required_material": "UTF8 matni"
              }
            ],
            "operation_modes": [
              "conversation"
            ],
            "outputs": {
              "all_results": "Har bir javob variantini va uning kontenti, vosita chaqiruvlari hamda fikrlash ma’lumotlarini saqlang. SSEda o‘zgarish qismlarini variant va vosita indeksi bo‘yicha yig‘ing, yakuniy tugash sababi va sarf ma’lumotlarini saqlang.",
              "family": "text",
              "fields": [
                {
                  "description": null,
                  "name": "choices[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "choices[].message.content",
                  "type": "string|null"
                },
                {
                  "description": null,
                  "name": "choices[].message.tool_calls",
                  "type": "tool_call[]"
                },
                {
                  "description": null,
                  "name": "choices[].message.reasoning_content",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "choices[].message.reasoning_details",
                  "type": "object|array"
                },
                {
                  "description": null,
                  "name": "choices[].finish_reason",
                  "type": "string|null"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json yoki SSE"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "deepseek-v3.1-thinking"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "messages",
                "required": true,
                "type": "message[]"
              },
              {
                "availability": "model_dependent",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "messages[].role",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "messages[].content",
                "required": false,
                "type": "string",
                "when": "Matnli suhbat"
              },
              {
                "availability": "source_interface",
                "default": false,
                "default_known": true,
                "enum": null,
                "enum_known": false,
                "name": "stream",
                "required": false,
                "type": "boolean"
              }
            ],
            "profile_binding": "Text conversation at the exact model ID and method path.",
            "unsupported": [
              "additional models/models list",
              "plugins",
              "route",
              "hosted tools; ordinary client function tools remain separate",
              "non-text output modalities",
              "provider.options overrides",
              "one-hour prompt-cache TTL"
            ]
          },
          "hints": [
            "Aniq model IDsini messages bilan yuboring.",
            "Kontekst uzunligi chiqishning yuqori chegarasi emas.",
            "Matn fayli aynan matnni taqdim etishi mumkin; tasvir, audio va fayl obyekti kirishlari aniq bog‘lashni talab qiladi."
          ],
          "input": {
            "content_type": "application/json",
            "control_scope": "Matnli suhbat; stream mantiqiy qiymat bo‘lib, standart holatda false.",
            "file_material": "Haqiqiy UTF-8 hujjati aynan matn sifatida kiritiladi; fayl IDsi faraz qilinmaydi",
            "hosted_operations": false,
            "material_types": [
              "text"
            ],
            "messages": "role/content tuzilishidagi xabarlar massivi",
            "multimodal_input_binding": null,
            "optional_controls": [
              "stream"
            ],
            "output_modalities": [
              "text"
            ],
            "required": [
              "model",
              "messages"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_or_streamed",
            "unknown": "preserve original request id; no generic chat job retrieval path"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/chat/completions"
          },
          "method_aliases": [
            "/chat/completions"
          ],
          "operation_modes": [
            "text_chat"
          ],
          "output": {
            "content": "choices[].message.content or stream delta",
            "family": "text",
            "format": "json or supported SSE",
            "tool_calls": "retain actual returned function calls"
          }
        },
        "ctx": null,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Tokenlar uchun bazaviy tariflar",
            "units": [
              {
                "amount": "0.000572000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.000000572",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0.0023000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0.0000023",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\":\"deepseek-v3.1-thinking\",\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}],\"stream\":false}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"choices\",[]):\n    if x.get(\"message\",{}).get(\"content\"): print(x[\"message\"][\"content\"])\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nif not os.environ.get(\"AILOOT_API_KEY\", \"\").strip(): raise SystemExit(\"Set AILOOT_API_KEY before making a request\")\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.chat.completions.create(model=\"deepseek-v3.1-thinking\", messages=[{\"role\":\"user\",\"content\":\"hello\"}], stream=False)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem=\"result\"):\n    # Keep every field, including usage, IDs, reasoning and tool requests.\n    Path(stem + \".json\").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n    print(json.dumps(data, ensure_ascii=False, indent=2))\n    def save_blob(encoded, suffix, label):\n        raw = base64.b64decode(encoded, altchars=b\"-_\", validate=True)\n        ext = \"png\" if raw.startswith(b\"\\x89PNG\\r\\n\\x1a\\n\") else \"jpg\" if raw.startswith(b\"\\xff\\xd8\") else \"webp\" if raw[:4] == b\"RIFF\" and raw[8:12] == b\"WEBP\" else suffix\n        file = Path(stem + \"-\" + label + \".\" + ext)\n        file.write_bytes(raw)\n        print(\"Saved\", file)\n    for index, block in enumerate(data.get(\"content\") or []):\n        print(\"Content\", index, json.dumps(block, ensure_ascii=False))\n    for choice in data.get(\"choices\") or []:\n        print(\"Choice\", choice.get(\"index\"), json.dumps(choice.get(\"message\", choice.get(\"delta\", {})), ensure_ascii=False))\n    for index, item in enumerate(data.get(\"output\") or []):\n        print(\"Output\", index, json.dumps(item, ensure_ascii=False))\n        if item.get(\"type\") == \"image_generation_call\" and item.get(\"result\"):\n            save_blob(item[\"result\"], item.get(\"output_format\") or \"bin\", \"output-\" + str(index))\n    for ci, candidate in enumerate(data.get(\"candidates\") or []):\n        for pi, part in enumerate((candidate.get(\"content\") or {}).get(\"parts\") or []):\n            print(\"Candidate/part\", ci, pi, json.dumps(part, ensure_ascii=False))\n            blob = part.get(\"inlineData\") or part.get(\"inline_data\")\n            if blob and blob.get(\"data\"):\n                mime = blob.get(\"mimeType\") or blob.get(\"mime_type\")\n                suffix = {\"image/png\":\"png\", \"image/jpeg\":\"jpg\", \"image/webp\":\"webp\", \"audio/wav\":\"wav\", \"audio/mpeg\":\"mp3\", \"video/mp4\":\"mp4\"}.get(mime, \"bin\")\n                save_blob(blob[\"data\"], suffix, \"candidate-\" + str(ci) + \"-part-\" + str(pi))\n\nsave_result(result.to_dict(mode=\"json\"))\n",
            "label": "Python SDK · all choices",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "chat",
        "hints": [
          "Send exact model ID with messages.",
          "Context length is not an output cap.",
          "A text file may supply literal text; image/audio/file-object inputs need exact binding."
        ],
        "http": "POST",
        "in": 0.572,
        "input": [
          "text"
        ],
        "label": "OpenAI Chat",
        "max_out": null,
        "max_reasoning_effort_multiplier": null,
        "method_aliases": [
          "/chat/completions"
        ],
        "out": 2.3,
        "output": [
          "text"
        ],
        "path": "/v1/chat/completions",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Not specified",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Not specified",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Not specified",
            "when": "When maximum reasoning effort uses the default adjustment"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0.000572000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.000000572",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0.0023000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0.0000023",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "request_model_id": "deepseek-v3.1-thinking",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.000572000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.000000572",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0.0023000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0.0000023",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "cached_input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_price",
            "label": "Cache write",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "generic_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "generic_cache_creation_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "one_hour_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "one_hour_cache_creation_token"
          }
        ]
      }
    ],
    "model_aliases": [
      {
        "canonical_model_id": "deepseek-v3.1-thinking",
        "request_model_id": "deepseek-v3.1-thinking",
        "route_label": "Ultra"
      }
    ],
    "name": "DeepSeek: DeepSeek V3.1",
    "price": {
      "in": 0.572,
      "in_label": null,
      "out": 2.3,
      "out_label": null,
      "scope_label": "Token rates",
      "sort_in": 0.572,
      "sort_out": 2.3,
      "varies": false
    },
    "short": "DeepSeek V3.1 — bitta nashrda fikrlash va bevosita javob rejimlari mavjud gibrid matn modeli. Uning 128K konteksti va yaxshilangan vosita qo‘llash profili kodlash, hujjat tahlili va ilova taqdim etgan qidiruvni qo‘llaydi; natija matn yoki taklif etilgan vosita chaqiruvlaridir."
  },
  "renderer_schema": {
    "method_order": "Original DTO methods order; every exact route/format/path/caller survives.",
    "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers."
  },
  "representation": "exact-public-dto-and-native-renderer-html",
  "representationNotes": {
    "current_reference": {
      "about_json_pointer": "/aboutHtml",
      "methods": [
        {
          "contract_json_pointer": "/methods/0/rawMethod/contract",
          "html_json_pointer": "/methods/0/html",
          "method_ordinal": 0
        }
      ],
      "role": "current-semantic-reference"
    },
    "raw_input": {
      "examples_role": "historical-input-not-current-ready-examples",
      "json_pointers": [
        "/rawModel",
        "/methods/0/rawMethod"
      ],
      "role": "historical-owner-input"
    },
    "schema": "ailoot-native-model-representations-v1"
  },
  "schema": "ailoot-static-model-facts-v1",
  "schema_version": 1,
  "shortText": "DeepSeek V3.1 — bitta nashrda fikrlash va bevosita javob rejimlari mavjud gibrid matn modeli. Uning 128K konteksti va yaxshilangan vosita qo‘llash profili kodlash, hujjat tahlili va ilova taqdim etgan qidiruvni qo‘llaydi; natija matn yoki taklif etilgan vosita chaqiruvlaridir.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "ed577cc8d75d25479fdfca7bf0dc37803687ca55d9eed5f6aabf25a3c5e2283d",
    "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6"
  },
  "sourceRevision": "c0857db8630befb197193697ebd3ccfee91b918d281c56370494ac9afeb0c010"
}