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DeepSeek V3.2 Exp — DeepSeek’ning V3.1 Terminus asosidagi eksperimental matn modeli nashri. Uning asosiy oʻzgarishi uzun matn ketma-ketliklari uchun yanada samarali oʻqitish va inferensiyani tadqiq qilish uchun kiritilgan DeepSeek Sparse Attention’dir. Vazifa matnga asoslangan holda qoladi: yozma koʻrsatma va kontekst matnli javob, kod yoki agentga tegishli chiqish hosil qiladi. Bu siyrak diqqat tadqiqot nashri boʻlib, yakuniy V3.2 modeli va uning keyingi vositalar bilan fikrlash formatidan alohida.

DSA yengil indeksator va mayda darajali token tanlovidan foydalanadi; shu tufayli asosiy diqqat hisoblash oldingi token maʼlumotlarining tanlangan qismiga yoʻnaladi. DeepSeek arxitektura oʻzgarishini alohida oʻrganish uchun eksperimental oʻqitish konfiguratsiyasini ataylab V3.1 Terminus bilan moslashtirgan. V3.2 hisoboti yakuniy V3.2 va V3.2 Exp shu arxitekturani boʻlishishini tasdiqlaydi hamda 128K kontekstli Terminus asosidan davom etgan oʻqitishni tavsiflaydi. Eʼlon qilingan eksperimental profil shu 128K uzoq kontekst sozlamasidan foydalanadi.

Ishlab chiqaruvchining eksperimental kartasi oldingi modelning vazifa profilini saqlagan holda mulohaza va agent ishlarini qamrab oladi. Uning dalillari vositasiz mulohaza va agentning vosita ishlatishini alohida jarayonlar sifatida koʻrsatadi. Aniq savol, tegishli manba konteksti va kerakli yakuniy shaklni tayyorlang. Vazifada vosita ishlatilsa, ilova uning taʼrifini beradi va natijani qaytaradi; model taklif qilgan harakat allaqachon bajarilgan amal bilan bir xil emas.

Yakuniy V3.2 kartasi qayta koʻrilgan vosita chaqiruv formatini kiritadi va vositalar bilan fikrlashni aniq tavsiflaydi. Bu yangilanishlar yakuniy relizga tegishli; ularni eksperimental nashrning butun oʻzaro aloqa talablari deb qabul qilmaslik kerak. V3.2 Exp uchun oʻxshash nom har bir keyingi boshqaruv yoki rolni oʻzaro almashtiriladigan qiladi deb oʻylamay, nashrning oʻz xabar formatini va Terminus’dan oʻtgan vazifa kontekstini saqlang.

Uzun hujjatlarni solishtirishda har bir manbani belgilang va savolga tegishli boʻlimlarni koʻrsating. Namuna topshiriq: “Bu talablarni taklif etilgan amalga oshirish bilan solishtiring, har bir nomuvofiqlikni aniqlang va uni tasdiqlovchi parchani keltiring. Oxirida ustuvor tuzatishlar roʻyxatini bering.” Kod uchun muvaffaqiyatsiz xatti-harakat, tegishli parcha va kutilgan natijani bering, keyin aniq tushuntirish bilan yoʻnaltirilgan oʻzgartirish soʻrang. Manba tuzilishi va tugallanish qoidasi uzoq kontekstli vazifani ishlatiladigan natijaga yoʻnaltirishga yordam beradi.

Ishlab chiqaruvchining etalon amalga oshirishi siyrak diqqat indeksatorini oʻz ichiga oladi; kartada esa muhim tuzatish qayd etilgan: indeksator RoPE’i oʻzaro aralashtirilmagan joylashuvdan foydalanadi, latent diqqat moduli esa oʻzaro aralashtirilgan joylashuvni kutadi. Bu foydalanuvchining boshqa koʻrsatmasi yoki yangi imkoniyat emas, modelni sodiq bajarish uchun tegishli ishlab chiqaruvchi-etalon tafsilotidir. Bundan tashqari, model nomining oʻzi har bir amalga oshirish eksperimental arxitekturaga qanday ishlov berishini tavsiflamaydi.

Davomli agent vazifasi manba matni, suhbat, yaratilgan mulohaza va vosita kuzatuvlarini birgalikda rejalashtirishi kerak. Foydali kuzatuvlarni tartiblang va yakuniy javobdan dalillar, taxminlar hamda taklif etilgan harakatlarni ajratishni soʻrang. Manbaga bogʻliq faktik natija uchun keng oʻqitish bilimiga suyanish oʻrniga vakolatli material yoki mos vositani taqdim eting. Chiqish vazifa va uning kirishlari bilan tekshirish mumkin boʻlgan matn, kod yoki tahliliy qayd boʻlib qoladi.

", "aboutText": "DeepSeek V3.2 Exp — DeepSeek’ning V3.1 Terminus asosidagi eksperimental matn modeli nashri. Uning asosiy oʻzgarishi uzun matn ketma-ketliklari uchun yanada samarali oʻqitish va inferensiyani tadqiq qilish uchun kiritilgan DeepSeek Sparse Attention’dir. Vazifa matnga asoslangan holda qoladi: yozma koʻrsatma va kontekst matnli javob, kod yoki agentga tegishli chiqish hosil qiladi. Bu siyrak diqqat tadqiqot nashri boʻlib, yakuniy V3.2 modeli va uning keyingi vositalar bilan fikrlash formatidan alohida.\n\nDSA yengil indeksator va mayda darajali token tanlovidan foydalanadi; shu tufayli asosiy diqqat hisoblash oldingi token maʼlumotlarining tanlangan qismiga yoʻnaladi. DeepSeek arxitektura oʻzgarishini alohida oʻrganish uchun eksperimental oʻqitish konfiguratsiyasini ataylab V3.1 Terminus bilan moslashtirgan. V3.2 hisoboti yakuniy V3.2 va V3.2 Exp shu arxitekturani boʻlishishini tasdiqlaydi hamda 128K kontekstli Terminus asosidan davom etgan oʻqitishni tavsiflaydi. Eʼlon qilingan eksperimental profil shu 128K uzoq kontekst sozlamasidan foydalanadi.\n\nIshlab chiqaruvchining eksperimental kartasi oldingi modelning vazifa profilini saqlagan holda mulohaza va agent ishlarini qamrab oladi. Uning dalillari vositasiz mulohaza va agentning vosita ishlatishini alohida jarayonlar sifatida koʻrsatadi. Aniq savol, tegishli manba konteksti va kerakli yakuniy shaklni tayyorlang. Vazifada vosita ishlatilsa, ilova uning taʼrifini beradi va natijani qaytaradi; model taklif qilgan harakat allaqachon bajarilgan amal bilan bir xil emas.\n\nYakuniy V3.2 kartasi qayta koʻrilgan vosita chaqiruv formatini kiritadi va vositalar bilan fikrlashni aniq tavsiflaydi. Bu yangilanishlar yakuniy relizga tegishli; ularni eksperimental nashrning butun oʻzaro aloqa talablari deb qabul qilmaslik kerak. V3.2 Exp uchun oʻxshash nom har bir keyingi boshqaruv yoki rolni oʻzaro almashtiriladigan qiladi deb oʻylamay, nashrning oʻz xabar formatini va Terminus’dan oʻtgan vazifa kontekstini saqlang.\n\nUzun hujjatlarni solishtirishda har bir manbani belgilang va savolga tegishli boʻlimlarni koʻrsating. Namuna topshiriq: “Bu talablarni taklif etilgan amalga oshirish bilan solishtiring, har bir nomuvofiqlikni aniqlang va uni tasdiqlovchi parchani keltiring. Oxirida ustuvor tuzatishlar roʻyxatini bering.” Kod uchun muvaffaqiyatsiz xatti-harakat, tegishli parcha va kutilgan natijani bering, keyin aniq tushuntirish bilan yoʻnaltirilgan oʻzgartirish soʻrang. Manba tuzilishi va tugallanish qoidasi uzoq kontekstli vazifani ishlatiladigan natijaga yoʻnaltirishga yordam beradi.\n\nIshlab chiqaruvchining etalon amalga oshirishi siyrak diqqat indeksatorini oʻz ichiga oladi; kartada esa muhim tuzatish qayd etilgan: indeksator RoPE’i oʻzaro aralashtirilmagan joylashuvdan foydalanadi, latent diqqat moduli esa oʻzaro aralashtirilgan joylashuvni kutadi. Bu foydalanuvchining boshqa koʻrsatmasi yoki yangi imkoniyat emas, modelni sodiq bajarish uchun tegishli ishlab chiqaruvchi-etalon tafsilotidir. Bundan tashqari, model nomining oʻzi har bir amalga oshirish eksperimental arxitekturaga qanday ishlov berishini tavsiflamaydi.\n\nDavomli agent vazifasi manba matni, suhbat, yaratilgan mulohaza va vosita kuzatuvlarini birgalikda rejalashtirishi kerak. Foydali kuzatuvlarni tartiblang va yakuniy javobdan dalillar, taxminlar hamda taklif etilgan harakatlarni ajratishni soʻrang. Manbaga bogʻliq faktik natija uchun keng oʻqitish bilimiga suyanish oʻrniga vakolatli material yoki mos vositani taqdim eting. Chiqish vazifa va uning kirishlari bilan tekshirish mumkin boʻlgan matn, kod yoki tahliliy qayd boʻlib qoladi.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "d2d0640ff5a268bd30191ff16301454d6057f5dc06dac5b24725829041a21e9d", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
DS

DeepSeek: DeepSeek V3.2 Exp

DeepSeek /deepseek-v3.2-exp-thinking

Boshlash

DeepSeek V3.2 Exp — V3.1 Terminus asosida qurilgan siyrak diqqat nashri. U uzun kontekst diqqatini samaraliroq qilishni o‘rganish davomida mulohaza, kodlash va agent vazifalari uchun matnni qayta ishlaydi; ishlab chiqaruvchi konteksti 128K bo‘lib, yakuniy V3.2 relizidan alohida identifikatsiyaga ega.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
Matn
Model haqida

Bu tavsif model imkoniyatlarini yoritadi. Ailoot da tanlangan usul uchun mavjud kirish, chiqish va parametrlar quyida keltirilgan.

DeepSeek V3.2 Exp — DeepSeek’ning V3.1 Terminus asosidagi eksperimental matn modeli nashri. Uning asosiy oʻzgarishi uzun matn ketma-ketliklari uchun yanada samarali oʻqitish va inferensiyani tadqiq qilish uchun kiritilgan DeepSeek Sparse Attention’dir. Vazifa matnga asoslangan holda qoladi: yozma koʻrsatma va kontekst matnli javob, kod yoki agentga tegishli chiqish hosil qiladi. Bu siyrak diqqat tadqiqot nashri boʻlib, yakuniy V3.2 modeli va uning keyingi vositalar bilan fikrlash formatidan alohida.

DSA yengil indeksator va mayda darajali token tanlovidan foydalanadi; shu tufayli asosiy diqqat hisoblash oldingi token maʼlumotlarining tanlangan qismiga yoʻnaladi. DeepSeek arxitektura oʻzgarishini alohida oʻrganish uchun eksperimental oʻqitish konfiguratsiyasini ataylab V3.1 Terminus bilan moslashtirgan. V3.2 hisoboti yakuniy V3.2 va V3.2 Exp shu arxitekturani boʻlishishini tasdiqlaydi hamda 128K kontekstli Terminus asosidan davom etgan oʻqitishni tavsiflaydi. Eʼlon qilingan eksperimental profil shu 128K uzoq kontekst sozlamasidan foydalanadi.

Ishlab chiqaruvchining eksperimental kartasi oldingi modelning vazifa profilini saqlagan holda mulohaza va agent ishlarini qamrab oladi. Uning dalillari vositasiz mulohaza va agentning vosita ishlatishini alohida jarayonlar sifatida koʻrsatadi. Aniq savol, tegishli manba konteksti va kerakli yakuniy shaklni tayyorlang. Vazifada vosita ishlatilsa, ilova uning taʼrifini beradi va natijani qaytaradi; model taklif qilgan harakat allaqachon bajarilgan amal bilan bir xil emas.

Yakuniy V3.2 kartasi qayta koʻrilgan vosita chaqiruv formatini kiritadi va vositalar bilan fikrlashni aniq tavsiflaydi. Bu yangilanishlar yakuniy relizga tegishli; ularni eksperimental nashrning butun oʻzaro aloqa talablari deb qabul qilmaslik kerak. V3.2 Exp uchun oʻxshash nom har bir keyingi boshqaruv yoki rolni oʻzaro almashtiriladigan qiladi deb oʻylamay, nashrning oʻz xabar formatini va Terminus’dan oʻtgan vazifa kontekstini saqlang.

Uzun hujjatlarni solishtirishda har bir manbani belgilang va savolga tegishli boʻlimlarni koʻrsating. Namuna topshiriq: “Bu talablarni taklif etilgan amalga oshirish bilan solishtiring, har bir nomuvofiqlikni aniqlang va uni tasdiqlovchi parchani keltiring. Oxirida ustuvor tuzatishlar roʻyxatini bering.” Kod uchun muvaffaqiyatsiz xatti-harakat, tegishli parcha va kutilgan natijani bering, keyin aniq tushuntirish bilan yoʻnaltirilgan oʻzgartirish soʻrang. Manba tuzilishi va tugallanish qoidasi uzoq kontekstli vazifani ishlatiladigan natijaga yoʻnaltirishga yordam beradi.

Ishlab chiqaruvchining etalon amalga oshirishi siyrak diqqat indeksatorini oʻz ichiga oladi; kartada esa muhim tuzatish qayd etilgan: indeksator RoPE’i oʻzaro aralashtirilmagan joylashuvdan foydalanadi, latent diqqat moduli esa oʻzaro aralashtirilgan joylashuvni kutadi. Bu foydalanuvchining boshqa koʻrsatmasi yoki yangi imkoniyat emas, modelni sodiq bajarish uchun tegishli ishlab chiqaruvchi-etalon tafsilotidir. Bundan tashqari, model nomining oʻzi har bir amalga oshirish eksperimental arxitekturaga qanday ishlov berishini tavsiflamaydi.

Davomli agent vazifasi manba matni, suhbat, yaratilgan mulohaza va vosita kuzatuvlarini birgalikda rejalashtirishi kerak. Foydali kuzatuvlarni tartiblang va yakuniy javobdan dalillar, taxminlar hamda taklif etilgan harakatlarni ajratishni soʻrang. Manbaga bogʻliq faktik natija uchun keng oʻqitish bilimiga suyanish oʻrniga vakolatli material yoki mos vositani taqdim eting. Chiqish vazifa va uning kirishlari bilan tekshirish mumkin boʻlgan matn, kod yoki tahliliy qayd boʻlib qoladi.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · OpenAI Chat POST /v1/chat/completions
Kirish
text
Chiqish
text
Chiqish formati
json yoki SSE
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
deepseek-v3.2-exp-thinking
Standart—
MaydonmessagesTurmessage[]TalabMajburiyQiymatlar va cheklovlar
Elementlarning eng kam soni: 1
Standart—
Maydonmessages[].roleTurstringTalabMajburiyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonmessages[].contentTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: Matnli suhbat
Standart—
MaydonstreamTurbooleanTalabIxtiyoriyQiymatlar va cheklovlarStandartfalse
Kirish materiallari birikmalari

Matn · UTF8 matni

messages[].content

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrcontent_typeParametr talablariapplication/json
ParametrrequiredParametr talablarimodel, messages
Parametrmaterial_typesParametr talablaritext
ParametrmessagesParametr talablarirole/content tuzilishidagi xabarlar massivi
Parametrfile_materialParametr talablariHaqiqiy UTF-8 hujjati aynan matn sifatida kiritiladi; fayl IDsi faraz qilinmaydi
Parametroptional_controlsParametr talablaristream
Parametrcontrol_scopeParametr talablariMatnli suhbat; stream mantiqiy qiymat bo‘lib, standart holatda false.
Parametroutput_modalitiesParametr talablaritext
Parametrhosted_operationsParametr talablarifalse
Parametrmultimodal_input_bindingParametr talablarinull

Usul taxalluslari: /chat/completions

Hayot sikli: synchronous_or_streamed

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.

Tariflar

Tarif ma’lumotlari so‘rov imkoniyatlarini qo‘shmaydi. Qo‘llab-quvvatlanadigan parametrlar tanlangan usulda ko‘rsatilgan.

Tezkor rejim
Ko‘rsatilmagan Tezkor rejim tanlanganda
Flex rejimi
Ko‘rsatilmagan flex rejimi tanlanganda
Maksimal fikrlash darajasi
Ko‘rsatilmagan Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda

Ailoot tariflari

Sozlamalar bo‘yicha narxlar

SozlamaTariflar jadvaliTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.00058 / 1K token
Chiqish · Chiqish tokenlari $0.00086 / 1K token
Kirish · Keshdan o‘qish $0 / 1K token
Kirish · Keshga yozish $0 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

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. Summani band qilish va to‘lovni hisoblash tartibi →

So‘rov namunalari

Ailoot Ultra · OpenAI Chat

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-v3.2-exp-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

Natijalarni olish va saqlash

Ailoot Ultra · OpenAI Chat

json yoki SSE

Barcha tayyor natijalarni saqlash

Qaytarilgan har bir to‘liq javob, fayl va kontent qismini saqlang.

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.

Asl javobni tekshirish kerak bo‘lsa

So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan.

Javob maydonlari
MaydonTur
Maydonchoices[].indexTurinteger
Maydonchoices[].message.contentTurstring|null
Maydonchoices[].message.tool_callsTurtool_call[]
Maydonchoices[].message.reasoning_contentTurstring
Maydonchoices[].message.reasoning_detailsTurobject|array
Maydonchoices[].finish_reasonTurstring|null
MaydonusageTurobject
Xatolar va keyingi qadamlar
JavobShartKeyingi qadam
Javob400 · invalid_request_errorShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘riKeyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.

Taxalluslar

Model taxalluslari

Ailoot Ultra deepseek-v3.2-exp-thinking

Usul yo‘llari taxalluslari

/chat/completions

Chaqirish uchun tegishli ID lar

deepseek-v3.2-exp-thinking

", "method_identity": { "effective_request_model_id": "deepseek-v3.2-exp-thinking", "fmt": "chat", "http": "POST", "path": "/v1/chat/completions", "request_model_id": "deepseek-v3.2-exp-thinking", "route_label": "Ultra" }, "method_key": "Ultra|chat|/v1/chat/completions|deepseek-v3.2-exp-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-v3.2-exp-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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0.00086000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000086", "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-v3.2-exp-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-v3.2-exp-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": 0.58, "input": [ "text" ], "label": "OpenAI Chat", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [ "/chat/completions" ], "out": 0.86, "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0.00086000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000086", "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-v3.2-exp-thinking", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0.00086000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000086", "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_id": "deepseek-v3.2-exp-thinking", "nativeProvenance": { "input_facts_canonical_sha256": "1c999599576131574f4a0c720fdf0a2a1d51c637b16969b2ae8885a0623240cf", "input_html_sha256": "5c3dadbf1348b324733b0f0e03e8d892d6f962cfe48ded7da7a39a9276429beb", "private_audit_sha256": "951fb83cbe9e077415c840a41a0d6bc08f922113d076b1ffc13059ed911c6e07", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/deepseek-v3-2-exp-thinking/", "description": "DeepSeek V3.2 Exp — V3.1 Terminus asosida qurilgan siyrak diqqat nashri. U uzun kontekst diqqatini samaraliroq qilishni o‘rganish davomida mulohaza, kodlash va agent vazifalari uchun matnni qayta ishlaydi; ishlab chiqaruvchi konteksti 128K bo‘lib, yakuniy V3.2 relizidan alohida identifikatsiyaga ega.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/deepseek-v3-2-exp-thinking/index.md", "title": "DeepSeek: DeepSeek V3.2 Exp — Ailoot", "url": "https://ailoot.ai/models/uz/deepseek-v3-2-exp-thinking/" }, "rawModel": { "about": "DeepSeek V3.2 Exp — DeepSeek’ning V3.1 Terminus asosidagi eksperimental matn modeli nashri. Uning asosiy oʻzgarishi uzun matn ketma-ketliklari uchun yanada samarali oʻqitish va inferensiyani tadqiq qilish uchun kiritilgan DeepSeek Sparse Attention’dir. Vazifa matnga asoslangan holda qoladi: yozma koʻrsatma va kontekst matnli javob, kod yoki agentga tegishli chiqish hosil qiladi. Bu siyrak diqqat tadqiqot nashri boʻlib, yakuniy V3.2 modeli va uning keyingi vositalar bilan fikrlash formatidan alohida.\n\nDSA yengil indeksator va mayda darajali token tanlovidan foydalanadi; shu tufayli asosiy diqqat hisoblash oldingi token maʼlumotlarining tanlangan qismiga yoʻnaladi. DeepSeek arxitektura oʻzgarishini alohida oʻrganish uchun eksperimental oʻqitish konfiguratsiyasini ataylab V3.1 Terminus bilan moslashtirgan. V3.2 hisoboti yakuniy V3.2 va V3.2 Exp shu arxitekturani boʻlishishini tasdiqlaydi hamda 128K kontekstli Terminus asosidan davom etgan oʻqitishni tavsiflaydi. Eʼlon qilingan eksperimental profil shu 128K uzoq kontekst sozlamasidan foydalanadi.\n\nIshlab chiqaruvchining eksperimental kartasi oldingi modelning vazifa profilini saqlagan holda mulohaza va agent ishlarini qamrab oladi. Uning dalillari vositasiz mulohaza va agentning vosita ishlatishini alohida jarayonlar sifatida koʻrsatadi. Aniq savol, tegishli manba konteksti va kerakli yakuniy shaklni tayyorlang. Vazifada vosita ishlatilsa, ilova uning taʼrifini beradi va natijani qaytaradi; model taklif qilgan harakat allaqachon bajarilgan amal bilan bir xil emas.\n\nYakuniy V3.2 kartasi qayta koʻrilgan vosita chaqiruv formatini kiritadi va vositalar bilan fikrlashni aniq tavsiflaydi. Bu yangilanishlar yakuniy relizga tegishli; ularni eksperimental nashrning butun oʻzaro aloqa talablari deb qabul qilmaslik kerak. V3.2 Exp uchun oʻxshash nom har bir keyingi boshqaruv yoki rolni oʻzaro almashtiriladigan qiladi deb oʻylamay, nashrning oʻz xabar formatini va Terminus’dan oʻtgan vazifa kontekstini saqlang.\n\nUzun hujjatlarni solishtirishda har bir manbani belgilang va savolga tegishli boʻlimlarni koʻrsating. Namuna topshiriq: “Bu talablarni taklif etilgan amalga oshirish bilan solishtiring, har bir nomuvofiqlikni aniqlang va uni tasdiqlovchi parchani keltiring. Oxirida ustuvor tuzatishlar roʻyxatini bering.” Kod uchun muvaffaqiyatsiz xatti-harakat, tegishli parcha va kutilgan natijani bering, keyin aniq tushuntirish bilan yoʻnaltirilgan oʻzgartirish soʻrang. Manba tuzilishi va tugallanish qoidasi uzoq kontekstli vazifani ishlatiladigan natijaga yoʻnaltirishga yordam beradi.\n\nIshlab chiqaruvchining etalon amalga oshirishi siyrak diqqat indeksatorini oʻz ichiga oladi; kartada esa muhim tuzatish qayd etilgan: indeksator RoPE’i oʻzaro aralashtirilmagan joylashuvdan foydalanadi, latent diqqat moduli esa oʻzaro aralashtirilgan joylashuvni kutadi. Bu foydalanuvchining boshqa koʻrsatmasi yoki yangi imkoniyat emas, modelni sodiq bajarish uchun tegishli ishlab chiqaruvchi-etalon tafsilotidir. Bundan tashqari, model nomining oʻzi har bir amalga oshirish eksperimental arxitekturaga qanday ishlov berishini tavsiflamaydi.\n\nDavomli agent vazifasi manba matni, suhbat, yaratilgan mulohaza va vosita kuzatuvlarini birgalikda rejalashtirishi kerak. Foydali kuzatuvlarni tartiblang va yakuniy javobdan dalillar, taxminlar hamda taklif etilgan harakatlarni ajratishni soʻrang. Manbaga bogʻliq faktik natija uchun keng oʻqitish bilimiga suyanish oʻrniga vakolatli material yoki mos vositani taqdim eting. Chiqish vazifa va uning kirishlari bilan tekshirish mumkin boʻlgan matn, kod yoki tahliliy qayd boʻlib qoladi.", "alias_of": null, "aliases": [], "author": "DeepSeek", "capabilities": { "context_length": 163840, "manufacturer_input": [ "text" ], "manufacturer_output": [ "text" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0.00086000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000086", "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-v3.2-exp-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-v3.2-exp-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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0.00086000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000086", "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-v3.2-exp-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-v3.2-exp-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": 0.58, "input": [ "text" ], "label": "OpenAI Chat", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [ "/chat/completions" ], "out": 0.86, "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0.00086000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000086", "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-v3.2-exp-thinking", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0.00086000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000086", "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-v3.2-exp-thinking", "request_model_id": "deepseek-v3.2-exp-thinking", "route_label": "Ultra" } ], "name": "DeepSeek: DeepSeek V3.2 Exp", "price": { "in": 0.58, "in_label": null, "out": 0.86, "out_label": null, "scope_label": "Token rates", "sort_in": 0.58, "sort_out": 0.86, "varies": false }, "short": "DeepSeek V3.2 Exp — V3.1 Terminus asosida qurilgan siyrak diqqat nashri. U uzun kontekst diqqatini samaraliroq qilishni o‘rganish davomida mulohaza, kodlash va agent vazifalari uchun matnni qayta ishlaydi; ishlab chiqaruvchi konteksti 128K bo‘lib, yakuniy V3.2 relizidan alohida identifikatsiyaga ega." }, "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 V3.2 Exp — V3.1 Terminus asosida qurilgan siyrak diqqat nashri. U uzun kontekst diqqatini samaraliroq qilishni o‘rganish davomida mulohaza, kodlash va agent vazifalari uchun matnni qayta ishlaydi; ishlab chiqaruvchi konteksti 128K bo‘lib, yakuniy V3.2 relizidan alohida identifikatsiyaga ega.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "a54e053a5c28dce82d69ceddfcb6fce5f3f856d93b32d6fd1a9ffb4500cdb8c0", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "5cdb5357e666c85ac0d626466d50e0731b81d35ace573c29cd6a993ab7e1f23c" }