{ "aboutHtml": "

Cohere Rerank 4 Pro — Cohere’ning Rerank 4 oilasidagi sifatga yo‘naltirilgan model. U nomzod hujjatlarni so‘rovga nisbatan baholab, dolzarblik tartibi va ballarini qaytaradi. Model murakkab qidiruv vazifalari, jumladan ko‘p tilli hujjatlar va yarim tuzilmali axborot uchun mo‘ljallangan. U mavjud tizim nomzodlarni yig‘ib bo‘lgach, qidiruv yoki qidiruv bilan boyitilgan jarayonga mos keladi: natija qaysi parchalar ko‘rsatilishi yoki keyingi bosqichga dalil sifatida berilishini tanlashga yordam beradi.

Ishlab chiqaruvchi identifikatori rerank-v4.0-pro bo‘lib, rerank-v4.0-fast va rerank-v3.5’dan alohida. Pro qidiruv sifati ustuvor bo‘lgan murakkab holatlarga mo‘ljallangan, Fast esa pastroq kechikish va yuqoriroq o‘tkazuvchanlikni nishonga oladi. Har ikkala Rerank 4 nashri 32,768 tokenli kontekstdan foydalanadi. So‘rov va bitta nomzod hujjat shu byudjetni bo‘lishadi; bu har bir kirish maydoni uchun alohida to‘liq oyna yoki butun saqlangan qidiruv korpusi chegarasi emas.

Aniq so‘rov va nomzod matn satrlari ro‘yxatini bering. Har bir qaytgan natijada asl hujjat indeksi va dolzarblik balli bor, top_n esa nechta saralangan natija qaytarilishini tanlashi mumkin; u berilmasa, ishlab chiqaruvchi barcha natijalarni qaytaradi. Saralash aniq parchaga qayta bog‘lanishi uchun har bir kirish yonida manba identifikatorini saqlang. Qo‘llab-quvvatlash savolida zarur shartlarni so‘rang va ro‘yxatda bo‘lmagan siyosatlarni reranker topishini kutish o‘rniga, nomzod siyosat bo‘limlarini bering.

Model 100 dan ortiq tilni qo‘llab-quvvatlaydi va inglizcha hamda boshqa tillardagi materialni qayta saralaydi. Bir nechta talabli so‘rovda parchani foydali qiladigan mahsulot, vaziyat va kerakli tartib kabi shartlarni ayting. Yarim tuzilmali yozuvlar uchun Cohere V2 hujjat ro‘yxatida xom obyektlarni uzatish o‘rniga YAML satrlarini tavsiya qiladi. Sarlavha va mazmun kabi mazmunli maydonlarni puxta tartibda saqlang, chunki kesish keyingi maydonlarni olib tashlashi mumkin. Bu tayyorgarlik oddiy nasr bilan birga mahsulot yozuvlari, xabarlar va hujjat metama’lumotlari uchun ham foydali.

Modelga xos tayyorgarlik qo‘llanmasi Rerank 4 so‘rovini dastlabki 16,384 token bilan cheklaydi. U uzunroq hujjatlarni so‘rov va zaxiralangan tokenlar bilan 32,768 tokenli kontekstni bo‘lishadigan bo‘laklar orqali qayta ishlashni tavsiflaydi. Joriy V2 ma’lumotnomasi alohida ravishda max_tokens_per_doc’ni 4096 qilib belgilaydi va uzunroq hujjat satrlari shu sozlamagacha kesilishini aytadi. Model oynasi va ishlab chiqaruvchi so‘rovidagi hujjat kesilishini turli boshqaruvlar sifatida ko‘ring. Har bir uzun manba doimo to‘liq o‘qiladi deb taxmin qilish o‘rniga, ixcham tegishli parchalarni tayyorlang yoki mos hujjat byudjetini tanlang.

Tavsiya etilgan so‘rov unumdorligi uchun Cohere bir vaqtning o‘zida 1,000 dan ortiq hujjat yubormaslikni maslahat beradi. Kattaroq ro‘yxatni boshqariladigan guruhlarga ajratganda nomzod identifikatorlarini saqlang. Dolzarblik ballari 0 va 1 orasida normallashtiriladi, biroq ular so‘rov va nomzodlarga bog‘liq; faktik to‘g‘rilikning chiziqli o‘lchovi emas. Asosiy qidiruv natijasi sifatida tartibdan foydalaning. Ball chegarasi bilan filtrlashda umumiy chegara bor deb taxmin qilish o‘rniga, uni haqiqiy sohani ifodalovchi namunaviy so‘rovlar va chegaraviy dolzarb parchalar bilan belgilang.

Foydali yakuniy artefakt — qidiruv natijasi yoki dalil tanlash bosqichiga tayyor, manba identifikatorlari va ballari bor saralangan nomzodlar ro‘yxati. Yuqori dolzarblik balliga ega hujjat ham eskirgan yoki noto‘g‘ri axborot saqlashi mumkin, shuning uchun undan foydalanadigan bosqich uchun manbasi va mazmunini saqlang. Pro maqsadi erkin shakldagi javob yozish yoki embedding yaratish emas, dolzarblikni baholashdir. Qidiruv jarayonini taqqoslash yoki yaxshilashda aynan Pro nashrini natija bilan bog‘lab saqlang.

", "aboutText": "Cohere Rerank 4 Pro — Cohere’ning Rerank 4 oilasidagi sifatga yo‘naltirilgan model. U nomzod hujjatlarni so‘rovga nisbatan baholab, dolzarblik tartibi va ballarini qaytaradi. Model murakkab qidiruv vazifalari, jumladan ko‘p tilli hujjatlar va yarim tuzilmali axborot uchun mo‘ljallangan. U mavjud tizim nomzodlarni yig‘ib bo‘lgach, qidiruv yoki qidiruv bilan boyitilgan jarayonga mos keladi: natija qaysi parchalar ko‘rsatilishi yoki keyingi bosqichga dalil sifatida berilishini tanlashga yordam beradi.\n\nIshlab chiqaruvchi identifikatori rerank-v4.0-pro bo‘lib, rerank-v4.0-fast va rerank-v3.5’dan alohida. Pro qidiruv sifati ustuvor bo‘lgan murakkab holatlarga mo‘ljallangan, Fast esa pastroq kechikish va yuqoriroq o‘tkazuvchanlikni nishonga oladi. Har ikkala Rerank 4 nashri 32,768 tokenli kontekstdan foydalanadi. So‘rov va bitta nomzod hujjat shu byudjetni bo‘lishadi; bu har bir kirish maydoni uchun alohida to‘liq oyna yoki butun saqlangan qidiruv korpusi chegarasi emas.\n\nAniq so‘rov va nomzod matn satrlari ro‘yxatini bering. Har bir qaytgan natijada asl hujjat indeksi va dolzarblik balli bor, top_n esa nechta saralangan natija qaytarilishini tanlashi mumkin; u berilmasa, ishlab chiqaruvchi barcha natijalarni qaytaradi. Saralash aniq parchaga qayta bog‘lanishi uchun har bir kirish yonida manba identifikatorini saqlang. Qo‘llab-quvvatlash savolida zarur shartlarni so‘rang va ro‘yxatda bo‘lmagan siyosatlarni reranker topishini kutish o‘rniga, nomzod siyosat bo‘limlarini bering.\n\nModel 100 dan ortiq tilni qo‘llab-quvvatlaydi va inglizcha hamda boshqa tillardagi materialni qayta saralaydi. Bir nechta talabli so‘rovda parchani foydali qiladigan mahsulot, vaziyat va kerakli tartib kabi shartlarni ayting. Yarim tuzilmali yozuvlar uchun Cohere V2 hujjat ro‘yxatida xom obyektlarni uzatish o‘rniga YAML satrlarini tavsiya qiladi. Sarlavha va mazmun kabi mazmunli maydonlarni puxta tartibda saqlang, chunki kesish keyingi maydonlarni olib tashlashi mumkin. Bu tayyorgarlik oddiy nasr bilan birga mahsulot yozuvlari, xabarlar va hujjat metama’lumotlari uchun ham foydali.\n\nModelga xos tayyorgarlik qo‘llanmasi Rerank 4 so‘rovini dastlabki 16,384 token bilan cheklaydi. U uzunroq hujjatlarni so‘rov va zaxiralangan tokenlar bilan 32,768 tokenli kontekstni bo‘lishadigan bo‘laklar orqali qayta ishlashni tavsiflaydi. Joriy V2 ma’lumotnomasi alohida ravishda max_tokens_per_doc’ni 4096 qilib belgilaydi va uzunroq hujjat satrlari shu sozlamagacha kesilishini aytadi. Model oynasi va ishlab chiqaruvchi so‘rovidagi hujjat kesilishini turli boshqaruvlar sifatida ko‘ring. Har bir uzun manba doimo to‘liq o‘qiladi deb taxmin qilish o‘rniga, ixcham tegishli parchalarni tayyorlang yoki mos hujjat byudjetini tanlang.\n\nTavsiya etilgan so‘rov unumdorligi uchun Cohere bir vaqtning o‘zida 1,000 dan ortiq hujjat yubormaslikni maslahat beradi. Kattaroq ro‘yxatni boshqariladigan guruhlarga ajratganda nomzod identifikatorlarini saqlang. Dolzarblik ballari 0 va 1 orasida normallashtiriladi, biroq ular so‘rov va nomzodlarga bog‘liq; faktik to‘g‘rilikning chiziqli o‘lchovi emas. Asosiy qidiruv natijasi sifatida tartibdan foydalaning. Ball chegarasi bilan filtrlashda umumiy chegara bor deb taxmin qilish o‘rniga, uni haqiqiy sohani ifodalovchi namunaviy so‘rovlar va chegaraviy dolzarb parchalar bilan belgilang.\n\nFoydali yakuniy artefakt — qidiruv natijasi yoki dalil tanlash bosqichiga tayyor, manba identifikatorlari va ballari bor saralangan nomzodlar ro‘yxati. Yuqori dolzarblik balliga ega hujjat ham eskirgan yoki noto‘g‘ri axborot saqlashi mumkin, shuning uchun undan foydalanadigan bosqich uchun manbasi va mazmunini saqlang. Pro maqsadi erkin shakldagi javob yozish yoki embedding yaratish emas, dolzarblikni baholashdir. Qidiruv jarayonini taqqoslash yoki yaxshilashda aynan Pro nashrini natija bilan bog‘lab saqlang.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "6143dd4994e939d58ee4f232952e137173852bc50709ef8fa3d9d9e55c9b5ae9", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
•

Cohere: Rerank 4 Pro

Cohere /cohere/rerank-4-pro

Boshlash

Cohere Rerank 4 Pro qidiruv va qidiruv bilan boyitilgan jarayonlar uchun nomzod hujjatlarni so‘rovga dolzarbligi bo‘yicha tartiblaydi. U 32K kontekst, ko‘p tilli qamrov va yarim tuzilmali matnni qo‘llab-quvvatlashga ega, sifatga yo‘naltirilgan Rerank 4 nashridir.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
Saralangan natijalar
Model haqida

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

Cohere Rerank 4 Pro — Cohere’ning Rerank 4 oilasidagi sifatga yo‘naltirilgan model. U nomzod hujjatlarni so‘rovga nisbatan baholab, dolzarblik tartibi va ballarini qaytaradi. Model murakkab qidiruv vazifalari, jumladan ko‘p tilli hujjatlar va yarim tuzilmali axborot uchun mo‘ljallangan. U mavjud tizim nomzodlarni yig‘ib bo‘lgach, qidiruv yoki qidiruv bilan boyitilgan jarayonga mos keladi: natija qaysi parchalar ko‘rsatilishi yoki keyingi bosqichga dalil sifatida berilishini tanlashga yordam beradi.

Ishlab chiqaruvchi identifikatori rerank-v4.0-pro bo‘lib, rerank-v4.0-fast va rerank-v3.5’dan alohida. Pro qidiruv sifati ustuvor bo‘lgan murakkab holatlarga mo‘ljallangan, Fast esa pastroq kechikish va yuqoriroq o‘tkazuvchanlikni nishonga oladi. Har ikkala Rerank 4 nashri 32,768 tokenli kontekstdan foydalanadi. So‘rov va bitta nomzod hujjat shu byudjetni bo‘lishadi; bu har bir kirish maydoni uchun alohida to‘liq oyna yoki butun saqlangan qidiruv korpusi chegarasi emas.

Aniq so‘rov va nomzod matn satrlari ro‘yxatini bering. Har bir qaytgan natijada asl hujjat indeksi va dolzarblik balli bor, top_n esa nechta saralangan natija qaytarilishini tanlashi mumkin; u berilmasa, ishlab chiqaruvchi barcha natijalarni qaytaradi. Saralash aniq parchaga qayta bog‘lanishi uchun har bir kirish yonida manba identifikatorini saqlang. Qo‘llab-quvvatlash savolida zarur shartlarni so‘rang va ro‘yxatda bo‘lmagan siyosatlarni reranker topishini kutish o‘rniga, nomzod siyosat bo‘limlarini bering.

Model 100 dan ortiq tilni qo‘llab-quvvatlaydi va inglizcha hamda boshqa tillardagi materialni qayta saralaydi. Bir nechta talabli so‘rovda parchani foydali qiladigan mahsulot, vaziyat va kerakli tartib kabi shartlarni ayting. Yarim tuzilmali yozuvlar uchun Cohere V2 hujjat ro‘yxatida xom obyektlarni uzatish o‘rniga YAML satrlarini tavsiya qiladi. Sarlavha va mazmun kabi mazmunli maydonlarni puxta tartibda saqlang, chunki kesish keyingi maydonlarni olib tashlashi mumkin. Bu tayyorgarlik oddiy nasr bilan birga mahsulot yozuvlari, xabarlar va hujjat metama’lumotlari uchun ham foydali.

Modelga xos tayyorgarlik qo‘llanmasi Rerank 4 so‘rovini dastlabki 16,384 token bilan cheklaydi. U uzunroq hujjatlarni so‘rov va zaxiralangan tokenlar bilan 32,768 tokenli kontekstni bo‘lishadigan bo‘laklar orqali qayta ishlashni tavsiflaydi. Joriy V2 ma’lumotnomasi alohida ravishda max_tokens_per_doc’ni 4096 qilib belgilaydi va uzunroq hujjat satrlari shu sozlamagacha kesilishini aytadi. Model oynasi va ishlab chiqaruvchi so‘rovidagi hujjat kesilishini turli boshqaruvlar sifatida ko‘ring. Har bir uzun manba doimo to‘liq o‘qiladi deb taxmin qilish o‘rniga, ixcham tegishli parchalarni tayyorlang yoki mos hujjat byudjetini tanlang.

Tavsiya etilgan so‘rov unumdorligi uchun Cohere bir vaqtning o‘zida 1,000 dan ortiq hujjat yubormaslikni maslahat beradi. Kattaroq ro‘yxatni boshqariladigan guruhlarga ajratganda nomzod identifikatorlarini saqlang. Dolzarblik ballari 0 va 1 orasida normallashtiriladi, biroq ular so‘rov va nomzodlarga bog‘liq; faktik to‘g‘rilikning chiziqli o‘lchovi emas. Asosiy qidiruv natijasi sifatida tartibdan foydalaning. Ball chegarasi bilan filtrlashda umumiy chegara bor deb taxmin qilish o‘rniga, uni haqiqiy sohani ifodalovchi namunaviy so‘rovlar va chegaraviy dolzarb parchalar bilan belgilang.

Foydali yakuniy artefakt — qidiruv natijasi yoki dalil tanlash bosqichiga tayyor, manba identifikatorlari va ballari bor saralangan nomzodlar ro‘yxati. Yuqori dolzarblik balliga ega hujjat ham eskirgan yoki noto‘g‘ri axborot saqlashi mumkin, shuning uchun undan foydalanadigan bosqich uchun manbasi va mazmunini saqlang. Pro maqsadi erkin shakldagi javob yozish yoki embedding yaratish emas, dolzarblikni baholashdir. Qidiruv jarayonini taqqoslash yoki yaxshilashda aynan Pro nashrini natija bilan bog‘lab saqlang.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · Qayta saralash POST /v1/rerank
Kirish
text
Chiqish
rerank
Chiqish formati
json
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydondocumentsTurarrayTalabMajburiyQiymatlar va cheklovlarStandart—
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
cohere/rerank-4-pro
Standart—
MaydonproviderTurschemaTalabIxtiyoriyQiymatlar va cheklovlarStandart—
MaydonqueryTurstringTalabMajburiyQiymatlar va cheklovlarStandart—
Maydonsession_idTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydontop_nTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Standart—
MaydontraceTurschemaTalabIxtiyoriyQiymatlar va cheklovlarStandart—
MaydonuserTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydondocuments[].textTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydondocuments[].imageTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Kirish materiallari birikmalari

text_rerank · Majburiy: query, documents array with minItems1

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrcontent_typeParametr talablariapplication/json
ParametrrequiredParametr talablarimodel, query, documents
Parametrmaterial_typesParametr talablaritext
Parametroptional_controlsParametr talablariprovider, session_id, top_n, trace, user
Parametrmultimodal_input_bindingParametr talablarinull

Hayot sikli: synchronous

Aniq model ID sini yuboring va to‘liq asl javobni saqlang.

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

Har bir to‘lovni ko‘rsatilgan shartlarga moslang. Har bir muqobil sozlamalar to‘plami uchun bitta tarifdan foydalaning.

SozlamaTariflar jadvaliHisob birligiTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifHisob birligi—To‘lov
Kirish · Kirish tokenlari — / 1K token
Chiqish · Chiqish tokenlari — / 1K token
Kirish · Keshdan o‘qish — / 1K token
Kirish · Keshga yozish — / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token
SozlamaUltraTariflar jadvali—Hisob birligiqidiruv birligi uchunTo‘lov
total · Qidiruv birligi $0.005 qidiruv birligi uchun

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 · Qayta saralash

{\n  "documents": [\n    "Paris is the capital of France.",\n    "Berlin is the capital of Germany."\n  ],\n  "model": "cohere/rerank-4-pro",\n  "query": "What is the capital of France?",\n  "top_n": 3\n}

Natijalarni olish va saqlash

Ailoot Ultra · Qayta saralash

json

Barcha tayyor natijalarni saqlash

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

Har bir haqiqiy natija indeksi, chekli relevance_score va dastlabki hujjat obyekti saqlanadi. Indekslar dastlabki hujjatlar massiviga bog‘lanadi. To‘liq dastlabki JSON saqlanadi va faqat hisob-kitob yakunlangach qaytariladi.

Asl javobni tekshirish kerak bo‘lsa

Dastlabki operatsiyani va to‘liq/qisman javobni saqlang; POST ni qayta yubormang.

Javob maydonlari
MaydonTur
MaydonmodelTurstring
MaydonidTurstring
MaydonproviderTurstring
Maydonresults[].indexTurinteger
Maydonresults[].relevance_scoreTurnumber
Maydonresults[].document.textTurstring
Maydonresults[].document.imageTurstring
Maydonusage.search_unitsTurinteger
Maydonusage.total_tokensTurinteger
Maydonusage.costTurnumber
Xatolar va keyingi qadamlar
JavobShartKeyingi qadam
Javob400 · invalid_request_errorShartMajburiy maydon, ko‘rinish yoki qiymat yaroqsizKeyingi qadamKo‘rsatilgan so‘rov maydonini tuzating.

Taxalluslar

Model taxalluslari

Ailoot Ultra cohere/rerank-4-pro

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

cohere/rerank-4-pro

", "method_identity": { "effective_request_model_id": "cohere/rerank-4-pro", "fmt": "rerank", "http": "POST", "path": "/v1/rerank", "request_model_id": "cohere/rerank-4-pro", "route_label": "Ultra" }, "method_key": "Ultra|rerank|/v1/rerank|cohere%2Frerank-4-pro", "ordinal": 0, "rawMethod": { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Every actual result index, finite relevance_score and original document object is retained. Indices bind the original document array. Entire original JSON is stored and returned only after settlement.", "errors": [ { "code": null, "condition": "Majburiy maydon, ko‘rinish yoki qiymat yaroqsiz", "http_status": 400, "recovery": "Ko‘rsatilgan so‘rov maydonini tuzating.", "type": "invalid_request_error" } ], "hints": [], "ignored_fields": [], "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Dastlabki operatsiyani va to‘liq/qisman javobni saqlang; POST ni qayta yubormang." }, "material_combinations": [ { "accepted": [ "string documents", "objects with text; image only for separately exact-supported multimodal model" ], "mode": "text_rerank", "required": [ "query", "documents array with minItems1" ] } ], "operation_modes": [ "rerank" ], "outputs": { "all_results": "Har bir haqiqiy natija indeksi, chekli relevance_score va dastlabki hujjat obyekti saqlanadi. Indekslar dastlabki hujjatlar massiviga bog‘lanadi. To‘liq dastlabki JSON saqlanadi va faqat hisob-kitob yakunlangach qaytariladi.", "family": "rerank", "fields": [ { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "provider", "type": "string" }, { "description": null, "name": "results[].index", "type": "integer" }, { "description": null, "name": "results[].relevance_score", "type": "number" }, { "description": null, "name": "results[].document.text", "type": "string" }, { "description": null, "name": "results[].document.image", "type": "string" }, { "description": null, "name": "usage.search_units", "type": "integer" }, { "description": null, "name": "usage.total_tokens", "type": "integer" }, { "description": null, "name": "usage.cost", "type": "number" } ], "format": "json", "schema": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "description": "The list of documents to rerank. Documents may be plain strings, or structured objects with `text` and/or `image` for multimodal models.", "enum": null, "enum_known": false, "minItems": 1, "name": "documents", "required": true, "type": "array" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "cohere/rerank-4-pro" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "provider", "required": false, "type": "schema" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "The search query to rerank documents against", "enum": null, "enum_known": false, "name": "query", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "A unique identifier for grouping related requests (e.g., a conversation or agent workflow). Used for observability grouping in Broadcast and private logging; never sent to the provider. If provided in both the request body and the x-session-id header, the body value takes precedence. Maximum of 256 characters.", "enum": null, "enum_known": false, "maxLength": 256, "name": "session_id", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "Number of most relevant documents to return", "enum": null, "enum_known": false, "minimum": 1, "name": "top_n", "required": false, "type": "integer" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "trace", "required": false, "type": "schema" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "A unique identifier representing your end-user. Forwarded to Broadcast and private logging as the end-user id; never sent to the provider.", "enum": null, "enum_known": false, "maxLength": 256, "name": "user", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "documents[].text", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "Official family image carrier is remote http/https URL or data:image URI; only for exact multimodal-model bindings.", "enum": null, "enum_known": false, "name": "documents[].image", "required": false, "type": "string" } ], "unsupported": [ "stream=true" ] }, "hints": [ "Aniq model ID sini yuboring va to‘liq asl javobni saqlang." ], "input": { "content_type": "application/json", "material_types": [ "text" ], "multimodal_input_binding": null, "optional_controls": [ "provider", "session_id", "top_n", "trace", "user" ], "required": [ "model", "query", "documents" ] }, "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Preserve original operation and full/partial reply; do not replay POST." }, "manufacturer_capability": { "context_length": 32768, "manufacturer_input": [ "text" ], "manufacturer_output": [ "rerank" ] }, "method": { "http": "POST", "path": "/v1/rerank" }, "method_aliases": [], "operation_modes": [ "rerank" ], "output": { "all_results": "Every actual result index, finite relevance_score and original document object is retained. Indices bind the original document array. Entire original JSON is stored and returned only after settlement.", "family": "rerank", "fields": [ { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "provider", "type": "string" }, { "description": null, "name": "results[].index", "type": "integer" }, { "description": null, "name": "results[].relevance_score", "type": "number" }, { "description": null, "name": "results[].document.text", "type": "string" }, { "description": null, "name": "results[].document.image", "type": "string" }, { "description": null, "name": "usage.search_units", "type": "integer" }, { "description": null, "name": "usage.total_tokens", "type": "integer" }, { "description": null, "name": "usage.cost", "type": "number" } ], "format": "json", "schema": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } }, "schema": { "request": { "description": "Rerank request input", "example": { "documents": [ "Paris is the capital of France.", "Berlin is the capital of Germany." ], "model": "cohere/rerank-v3.5", "query": "What is the capital of France?", "top_n": 3 }, "properties": { "documents": { "description": "The list of documents to rerank. Documents may be plain strings, or structured objects with `text` and/or `image` for multimodal models.", "example": [ "Paris is the capital of France.", "Berlin is the capital of Germany." ], "items": { "anyOf": [ { "type": "string" }, { "description": "A structured document with optional text and/or image content. At least one of `text` or `image` must be provided.", "properties": { "image": { "description": "An image associated with the document, as a remote URL (http/https) or a base64-encoded data URI (data:image/...).", "example": "https://upload.wikimedia.org/wikipedia/commons/thumb/8/8b/Phytogenic.png", "type": "string" }, "text": { "description": "The document text", "example": "AI enables robots to perceive, plan, and act autonomously.", "type": "string" } }, "type": "object" } ], "description": "A document to rerank. Either a plain string, or a structured object with optional `text` and/or `image`." }, "minItems": 1, "type": "array" }, "model": { "enum": [ "cohere/rerank-4-pro" ], "type": "string" }, "provider": { "allOf": [ { "$ref": "#/components/schemas/ProviderPreferences" }, { "description": "Provider routing preferences for the request." } ] }, "query": { "description": "The search query to rerank documents against", "example": "What is the capital of France?", "type": "string" }, "session_id": { "description": "A unique identifier for grouping related requests (e.g., a conversation or agent workflow). Used for observability grouping in Broadcast and private logging; never sent to the provider. If provided in both the request body and the x-session-id header, the body value takes precedence. Maximum of 256 characters.", "example": "session-1234", "maxLength": 256, "type": "string" }, "top_n": { "description": "Number of most relevant documents to return", "example": 3, "minimum": 1, "type": "integer" }, "trace": { "$ref": "#/components/schemas/TraceConfig" }, "user": { "description": "A unique identifier representing your end-user. Forwarded to Broadcast and private logging as the end-user id; never sent to the provider.", "example": "user-1234", "maxLength": 256, "type": "string" } }, "required": [ "model", "query", "documents" ], "type": "object" }, "response": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "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" } ] }, { "dimensions": { "native_endpoint": "rerank", "quoted_unit": "per search unit" }, "label": "Ultra", "units": [ { "amount": "0.005", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.005", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } ], "examples": [ { "code": "{\n \"documents\": [\n \"Paris is the capital of France.\",\n \"Berlin is the capital of Germany.\"\n ],\n \"model\": \"cohere/rerank-4-pro\",\n \"query\": \"What is the capital of France?\",\n \"top_n\": 3\n}", "label": "Retained official request structure; documentary example", "language": "json" }, { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_REQUEST_FILE:?Set your actual JSON request file}\"\nexport AILOOT_REQUEST_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads(Path(os.environ['AILOOT_REQUEST_FILE']).read_text(encoding='utf-8'))\nif not isinstance(p,dict): raise SystemExit('Request must be a JSON object')\np['model']='cohere/rerank-4-pro'\nPath('request.json').write_text(json.dumps(p,ensure_ascii=False),encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/rerank \\\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\nprint(json.dumps(json.loads(Path('result.json').read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\n", "label": "Use your actual request material and retain the complete result", "language": "bash", "material_policy": "Use real owner-supplied state/questions or query/documents. No invented file ID, document, image URL, job ID or returned result." } ], "family": "rerank", "fast_multiplier": null, "flex_multiplier": null, "fmt": "rerank", "http": "POST", "in": null, "input": [ "text" ], "label": "Qayta saralash", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "native_endpoint": "rerank", "out": null, "output": [ "rerank" ], "path": "/v1/rerank", "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": "per_request", "image_input_price": null, "image_output_price": null, "per_request_price": null, "unit_prices": { "search_unit": 0.005 } }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "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" } ] }, { "dimensions": { "native_endpoint": "rerank", "quoted_unit": "per search unit" }, "label": "Ultra", "units": [ { "amount": "0.005", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.005", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } ], "request_model_id": "cohere/rerank-4-pro", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "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" }, { "amount": "0.005", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.005", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } } ], "model_id": "cohere/rerank-4-pro", "nativeProvenance": { "input_facts_canonical_sha256": "0a8300c633f556a179bddd70c1ffd17b8a84becfb771bbd08b967faa66d4efd1", "input_html_sha256": "14c58f40c1325dde44bd759af93c4914e894263faaf762850317856b635cdf22", "private_audit_sha256": "58dfc20f43510c7ad21381eb65896e6afa90577b417a7e4c08166c8c57aae3fb", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/cohere-rerank-4-pro/", "description": "Cohere Rerank 4 Pro qidiruv va qidiruv bilan boyitilgan jarayonlar uchun nomzod hujjatlarni so‘rovga dolzarbligi bo‘yicha tartiblaydi. U 32K kontekst, ko‘p tilli qamrov va yarim tuzilmali matnni qo‘llab-quvvatlashga ega, sifatga yo‘naltirilgan Rerank 4 nashridir.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/cohere-rerank-4-pro/index.md", "title": "Cohere: Rerank 4 Pro — Ailoot", "url": "https://ailoot.ai/models/uz/cohere-rerank-4-pro/" }, "rawModel": { "about": "Cohere Rerank 4 Pro — Cohere’ning Rerank 4 oilasidagi sifatga yo‘naltirilgan model. U nomzod hujjatlarni so‘rovga nisbatan baholab, dolzarblik tartibi va ballarini qaytaradi. Model murakkab qidiruv vazifalari, jumladan ko‘p tilli hujjatlar va yarim tuzilmali axborot uchun mo‘ljallangan. U mavjud tizim nomzodlarni yig‘ib bo‘lgach, qidiruv yoki qidiruv bilan boyitilgan jarayonga mos keladi: natija qaysi parchalar ko‘rsatilishi yoki keyingi bosqichga dalil sifatida berilishini tanlashga yordam beradi.\n\nIshlab chiqaruvchi identifikatori rerank-v4.0-pro bo‘lib, rerank-v4.0-fast va rerank-v3.5’dan alohida. Pro qidiruv sifati ustuvor bo‘lgan murakkab holatlarga mo‘ljallangan, Fast esa pastroq kechikish va yuqoriroq o‘tkazuvchanlikni nishonga oladi. Har ikkala Rerank 4 nashri 32,768 tokenli kontekstdan foydalanadi. So‘rov va bitta nomzod hujjat shu byudjetni bo‘lishadi; bu har bir kirish maydoni uchun alohida to‘liq oyna yoki butun saqlangan qidiruv korpusi chegarasi emas.\n\nAniq so‘rov va nomzod matn satrlari ro‘yxatini bering. Har bir qaytgan natijada asl hujjat indeksi va dolzarblik balli bor, top_n esa nechta saralangan natija qaytarilishini tanlashi mumkin; u berilmasa, ishlab chiqaruvchi barcha natijalarni qaytaradi. Saralash aniq parchaga qayta bog‘lanishi uchun har bir kirish yonida manba identifikatorini saqlang. Qo‘llab-quvvatlash savolida zarur shartlarni so‘rang va ro‘yxatda bo‘lmagan siyosatlarni reranker topishini kutish o‘rniga, nomzod siyosat bo‘limlarini bering.\n\nModel 100 dan ortiq tilni qo‘llab-quvvatlaydi va inglizcha hamda boshqa tillardagi materialni qayta saralaydi. Bir nechta talabli so‘rovda parchani foydali qiladigan mahsulot, vaziyat va kerakli tartib kabi shartlarni ayting. Yarim tuzilmali yozuvlar uchun Cohere V2 hujjat ro‘yxatida xom obyektlarni uzatish o‘rniga YAML satrlarini tavsiya qiladi. Sarlavha va mazmun kabi mazmunli maydonlarni puxta tartibda saqlang, chunki kesish keyingi maydonlarni olib tashlashi mumkin. Bu tayyorgarlik oddiy nasr bilan birga mahsulot yozuvlari, xabarlar va hujjat metama’lumotlari uchun ham foydali.\n\nModelga xos tayyorgarlik qo‘llanmasi Rerank 4 so‘rovini dastlabki 16,384 token bilan cheklaydi. U uzunroq hujjatlarni so‘rov va zaxiralangan tokenlar bilan 32,768 tokenli kontekstni bo‘lishadigan bo‘laklar orqali qayta ishlashni tavsiflaydi. Joriy V2 ma’lumotnomasi alohida ravishda max_tokens_per_doc’ni 4096 qilib belgilaydi va uzunroq hujjat satrlari shu sozlamagacha kesilishini aytadi. Model oynasi va ishlab chiqaruvchi so‘rovidagi hujjat kesilishini turli boshqaruvlar sifatida ko‘ring. Har bir uzun manba doimo to‘liq o‘qiladi deb taxmin qilish o‘rniga, ixcham tegishli parchalarni tayyorlang yoki mos hujjat byudjetini tanlang.\n\nTavsiya etilgan so‘rov unumdorligi uchun Cohere bir vaqtning o‘zida 1,000 dan ortiq hujjat yubormaslikni maslahat beradi. Kattaroq ro‘yxatni boshqariladigan guruhlarga ajratganda nomzod identifikatorlarini saqlang. Dolzarblik ballari 0 va 1 orasida normallashtiriladi, biroq ular so‘rov va nomzodlarga bog‘liq; faktik to‘g‘rilikning chiziqli o‘lchovi emas. Asosiy qidiruv natijasi sifatida tartibdan foydalaning. Ball chegarasi bilan filtrlashda umumiy chegara bor deb taxmin qilish o‘rniga, uni haqiqiy sohani ifodalovchi namunaviy so‘rovlar va chegaraviy dolzarb parchalar bilan belgilang.\n\nFoydali yakuniy artefakt — qidiruv natijasi yoki dalil tanlash bosqichiga tayyor, manba identifikatorlari va ballari bor saralangan nomzodlar ro‘yxati. Yuqori dolzarblik balliga ega hujjat ham eskirgan yoki noto‘g‘ri axborot saqlashi mumkin, shuning uchun undan foydalanadigan bosqich uchun manbasi va mazmunini saqlang. Pro maqsadi erkin shakldagi javob yozish yoki embedding yaratish emas, dolzarblikni baholashdir. Qidiruv jarayonini taqqoslash yoki yaxshilashda aynan Pro nashrini natija bilan bog‘lab saqlang.", "alias_of": null, "aliases": [], "author": "Cohere", "available": null, "canonical_model": "cohere/rerank-4-pro", "capabilities": { "context_length": 32768, "manufacturer_input": [ "text" ], "manufacturer_output": [ "rerank" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.005", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.005", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ], "summary": "Konfiguratsiya narxlari tafsilotlarda to‘liq ko‘rsatilgan." } ], "ctx": null, "entity_type": "model", "formats": [ "rerank" ], "id": "cohere/rerank-4-pro", "input": [ "text" ], "kind": "rerank", "max_out": null, "methods": [ { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Every actual result index, finite relevance_score and original document object is retained. Indices bind the original document array. Entire original JSON is stored and returned only after settlement.", "errors": [ { "code": null, "condition": "Majburiy maydon, ko‘rinish yoki qiymat yaroqsiz", "http_status": 400, "recovery": "Ko‘rsatilgan so‘rov maydonini tuzating.", "type": "invalid_request_error" } ], "hints": [], "ignored_fields": [], "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Dastlabki operatsiyani va to‘liq/qisman javobni saqlang; POST ni qayta yubormang." }, "material_combinations": [ { "accepted": [ "string documents", "objects with text; image only for separately exact-supported multimodal model" ], "mode": "text_rerank", "required": [ "query", "documents array with minItems1" ] } ], "operation_modes": [ "rerank" ], "outputs": { "all_results": "Har bir haqiqiy natija indeksi, chekli relevance_score va dastlabki hujjat obyekti saqlanadi. Indekslar dastlabki hujjatlar massiviga bog‘lanadi. To‘liq dastlabki JSON saqlanadi va faqat hisob-kitob yakunlangach qaytariladi.", "family": "rerank", "fields": [ { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "provider", "type": "string" }, { "description": null, "name": "results[].index", "type": "integer" }, { "description": null, "name": "results[].relevance_score", "type": "number" }, { "description": null, "name": "results[].document.text", "type": "string" }, { "description": null, "name": "results[].document.image", "type": "string" }, { "description": null, "name": "usage.search_units", "type": "integer" }, { "description": null, "name": "usage.total_tokens", "type": "integer" }, { "description": null, "name": "usage.cost", "type": "number" } ], "format": "json", "schema": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "description": "The list of documents to rerank. Documents may be plain strings, or structured objects with `text` and/or `image` for multimodal models.", "enum": null, "enum_known": false, "minItems": 1, "name": "documents", "required": true, "type": "array" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "cohere/rerank-4-pro" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "provider", "required": false, "type": "schema" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "The search query to rerank documents against", "enum": null, "enum_known": false, "name": "query", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "A unique identifier for grouping related requests (e.g., a conversation or agent workflow). Used for observability grouping in Broadcast and private logging; never sent to the provider. If provided in both the request body and the x-session-id header, the body value takes precedence. Maximum of 256 characters.", "enum": null, "enum_known": false, "maxLength": 256, "name": "session_id", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "Number of most relevant documents to return", "enum": null, "enum_known": false, "minimum": 1, "name": "top_n", "required": false, "type": "integer" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "trace", "required": false, "type": "schema" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "A unique identifier representing your end-user. Forwarded to Broadcast and private logging as the end-user id; never sent to the provider.", "enum": null, "enum_known": false, "maxLength": 256, "name": "user", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "documents[].text", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "Official family image carrier is remote http/https URL or data:image URI; only for exact multimodal-model bindings.", "enum": null, "enum_known": false, "name": "documents[].image", "required": false, "type": "string" } ], "unsupported": [ "stream=true" ] }, "hints": [ "Aniq model ID sini yuboring va to‘liq asl javobni saqlang." ], "input": { "content_type": "application/json", "material_types": [ "text" ], "multimodal_input_binding": null, "optional_controls": [ "provider", "session_id", "top_n", "trace", "user" ], "required": [ "model", "query", "documents" ] }, "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Preserve original operation and full/partial reply; do not replay POST." }, "manufacturer_capability": { "context_length": 32768, "manufacturer_input": [ "text" ], "manufacturer_output": [ "rerank" ] }, "method": { "http": "POST", "path": "/v1/rerank" }, "method_aliases": [], "operation_modes": [ "rerank" ], "output": { "all_results": "Every actual result index, finite relevance_score and original document object is retained. Indices bind the original document array. Entire original JSON is stored and returned only after settlement.", "family": "rerank", "fields": [ { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "provider", "type": "string" }, { "description": null, "name": "results[].index", "type": "integer" }, { "description": null, "name": "results[].relevance_score", "type": "number" }, { "description": null, "name": "results[].document.text", "type": "string" }, { "description": null, "name": "results[].document.image", "type": "string" }, { "description": null, "name": "usage.search_units", "type": "integer" }, { "description": null, "name": "usage.total_tokens", "type": "integer" }, { "description": null, "name": "usage.cost", "type": "number" } ], "format": "json", "schema": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } }, "schema": { "request": { "description": "Rerank request input", "example": { "documents": [ "Paris is the capital of France.", "Berlin is the capital of Germany." ], "model": "cohere/rerank-v3.5", "query": "What is the capital of France?", "top_n": 3 }, "properties": { "documents": { "description": "The list of documents to rerank. Documents may be plain strings, or structured objects with `text` and/or `image` for multimodal models.", "example": [ "Paris is the capital of France.", "Berlin is the capital of Germany." ], "items": { "anyOf": [ { "type": "string" }, { "description": "A structured document with optional text and/or image content. At least one of `text` or `image` must be provided.", "properties": { "image": { "description": "An image associated with the document, as a remote URL (http/https) or a base64-encoded data URI (data:image/...).", "example": "https://upload.wikimedia.org/wikipedia/commons/thumb/8/8b/Phytogenic.png", "type": "string" }, "text": { "description": "The document text", "example": "AI enables robots to perceive, plan, and act autonomously.", "type": "string" } }, "type": "object" } ], "description": "A document to rerank. Either a plain string, or a structured object with optional `text` and/or `image`." }, "minItems": 1, "type": "array" }, "model": { "enum": [ "cohere/rerank-4-pro" ], "type": "string" }, "provider": { "allOf": [ { "$ref": "#/components/schemas/ProviderPreferences" }, { "description": "Provider routing preferences for the request." } ] }, "query": { "description": "The search query to rerank documents against", "example": "What is the capital of France?", "type": "string" }, "session_id": { "description": "A unique identifier for grouping related requests (e.g., a conversation or agent workflow). Used for observability grouping in Broadcast and private logging; never sent to the provider. If provided in both the request body and the x-session-id header, the body value takes precedence. Maximum of 256 characters.", "example": "session-1234", "maxLength": 256, "type": "string" }, "top_n": { "description": "Number of most relevant documents to return", "example": 3, "minimum": 1, "type": "integer" }, "trace": { "$ref": "#/components/schemas/TraceConfig" }, "user": { "description": "A unique identifier representing your end-user. Forwarded to Broadcast and private logging as the end-user id; never sent to the provider.", "example": "user-1234", "maxLength": 256, "type": "string" } }, "required": [ "model", "query", "documents" ], "type": "object" }, "response": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "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" } ] }, { "dimensions": { "native_endpoint": "rerank", "quoted_unit": "per search unit" }, "label": "Ultra", "units": [ { "amount": "0.005", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.005", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } ], "examples": [ { "code": "{\n \"documents\": [\n \"Paris is the capital of France.\",\n \"Berlin is the capital of Germany.\"\n ],\n \"model\": \"cohere/rerank-4-pro\",\n \"query\": \"What is the capital of France?\",\n \"top_n\": 3\n}", "label": "Retained official request structure; documentary example", "language": "json" }, { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_REQUEST_FILE:?Set your actual JSON request file}\"\nexport AILOOT_REQUEST_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads(Path(os.environ['AILOOT_REQUEST_FILE']).read_text(encoding='utf-8'))\nif not isinstance(p,dict): raise SystemExit('Request must be a JSON object')\np['model']='cohere/rerank-4-pro'\nPath('request.json').write_text(json.dumps(p,ensure_ascii=False),encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/rerank \\\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\nprint(json.dumps(json.loads(Path('result.json').read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\n", "label": "Use your actual request material and retain the complete result", "language": "bash", "material_policy": "Use real owner-supplied state/questions or query/documents. No invented file ID, document, image URL, job ID or returned result." } ], "family": "rerank", "fast_multiplier": null, "flex_multiplier": null, "fmt": "rerank", "http": "POST", "in": null, "input": [ "text" ], "label": "Qayta saralash", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "native_endpoint": "rerank", "out": null, "output": [ "rerank" ], "path": "/v1/rerank", "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": "per_request", "image_input_price": null, "image_output_price": null, "per_request_price": null, "unit_prices": { "search_unit": 0.005 } }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "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" } ] }, { "dimensions": { "native_endpoint": "rerank", "quoted_unit": "per search unit" }, "label": "Ultra", "units": [ { "amount": "0.005", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.005", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } ], "request_model_id": "cohere/rerank-4-pro", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "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" }, { "amount": "0.005", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.005", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } ], "model_aliases": [ { "canonical_model_id": "cohere/rerank-4-pro", "request_model_id": "cohere/rerank-4-pro", "route_label": "Ultra" } ], "name": "Cohere: Rerank 4 Pro", "price": { "in": null, "in_label": null, "out": null, "out_label": null, "sort_in": null, "sort_out": null, "varies": true }, "public_id": "cohere/rerank-4-pro", "short": "Cohere Rerank 4 Pro qidiruv va qidiruv bilan boyitilgan jarayonlar uchun nomzod hujjatlarni so‘rovga dolzarbligi bo‘yicha tartiblaydi. U 32K kontekst, ko‘p tilli qamrov va yarim tuzilmali matnni qo‘llab-quvvatlashga ega, sifatga yo‘naltirilgan Rerank 4 nashridir." }, "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": "Cohere Rerank 4 Pro qidiruv va qidiruv bilan boyitilgan jarayonlar uchun nomzod hujjatlarni so‘rovga dolzarbligi bo‘yicha tartiblaydi. U 32K kontekst, ko‘p tilli qamrov va yarim tuzilmali matnni qo‘llab-quvvatlashga ega, sifatga yo‘naltirilgan Rerank 4 nashridir.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "fdc88d26fd932c6f852a56dc3a181500fd6bbf0c6c6c0d95b77424c857b1a76c", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "a7b9e5a028be3fe863cc53695a065d60288990cf557e8908898f53354dc3032b" }