{ "aboutHtml": "

Umumiy tavsif va model profili

GLM-4-AirX — Z.ai’ning GLM-4 Air qatoridagi yuqori tezlikli a’zo. Ishlab chiqaruvchi uni kam kechikish va dasturdagi parallel vazifalarga yo‘naltirib, ko‘rsatmalarga rioya qilish, injiniring kodi va funksiya chaqiruvlariga alohida e’tibor beradi. Model matn qabul qiladi va matn qaytaradi. Uning o‘z profilida 8K tokenlik kontekst oynasi va eng katta 4K tokenlik chiqish ko‘rsatilgan. Bular GLM-4-Air-250414 uchun e’lon qilingan katta kontekstdan farq qiladi; AirX o‘zining e’lon qilingan profilini saqlaydi.

Interaktiv yordamchi uchun bu aniq yo‘naltirilgan so‘rovlarni anglatadi: funksiyani izohlash, xabarni tasniflash, berilgan abzasdan maydonlarni ajratish yoki texnik ko‘rsatmani qisqa javobga aylantirish. Tegishli parcha, joriy savol va kerakli natijani birga bering. Katta fon to‘plami sig‘adi deb taxmin qilish o‘rniga to‘liq suhbat va vosita kuzatuvlarini faol kontekst ichida saqlang.

Qo‘llanadigan imkoniyatlar

GLM-4 tavsifida oqimli matn, funksiya chaqiruvi, kontekst keshi, JSON kabi tuzilgan formatlar va MCP vositalari hamda ma’lumot manbalari bilan integratsiya ko‘rsatilgan. Oqim javobni yaratilishi bilan ko‘rsatadi; dastur butun javob kelishidan oldin foydali matnni namoyish qilishi mumkin. U yetkazishni o‘zgartiradi, modelning kirish yoki chiqish turini emas.

Funksiya chaqiruvi modelga dastur ta’riflagan funksiyalardan amal taklif qilish imkonini beradi. Dastur bu amalni bajaradi va kuzatuvni qaytaradi. Mos sikl — kichik ma’lumot qidiruvi, so‘ng qaytgan ma’lumotning qisqa izohi. MCP integratsiyasi ham dastur taqdim etgan vosita va ma’lumotlarga bog‘liq. Kontekst keshi takroriy kirishdan qayta foydalanishga tegishli; u har bir takroriy suhbat keshga tushganini anglatmaydi.

JSON natijasi uchun materialni berishdan oldin maydonlar va ularning ma’nosini ko‘rsating. Masalan, texnik so‘rovni bering va uning toifasi, talab qilinadigan amal va javobsiz savollarini so‘rang. Olingan tuzilma dasturdagi keyingi qadamga foydalidir, lekin qiymatlar berilgan matn bilan tekshirilishi kerak.

Tavsiya etilgan vaziyatlar

Ishlab chiqaruvchi GLM-4 qo‘llanishlariga tarjima, semantik tasnif, hujjat maydonlarini ajratish, marketing matni, tahliliy hisobot va safar rejalashni kiritadi. AirX’ning qisqaroq profili bunday jarayonning bevosita qadamiga mos keladi. Tarjima so‘rovi qisqa parcha, auditoriya va terminologiyani berishi mumkin; chiqish talab qilingan nomlarni saqlagan qayta yozilgan parcha bo‘ladi. Tasnif so‘rovi noma’lum baho so‘rash o‘rniga ruxsat etilgan belgilar va ta’riflarni berishi mumkin.

Ajratib olish uchun so‘ralgan identifikatorlar va boshqa maydonlarni belgilang va faqat tegishli manba parchasini kiriting. Yozish uchun mahsulot faktlari va mahsulot abzasi yoki kampaniya satri kabi bitta materialni bering. Hisobot yoki safar rejasi uchun joriy qadamning tegishli faktlari va cheklovlarini yetkazing. Yangi ma’lumot zarur bo‘lsa, uni dasturning qidiruv yoki ma’lumot vositasi beradi; keyin model qaytgan material bilan ishlaydi.

Batafsil tayyorlash va tezlik yo‘nalishi

AirX 15 trillion token, jumladan katta hajmdagi sintetik mulohaza materialida dastlab o‘qitilgan deb ta’riflanadi. Keyingi o‘qitishi ko‘rsatmalar, injiniring kodi va funksiyadan foydalanishni kuchaytirish uchun dialog afzalliklariga moslashtirish, rad etishli tanlash va mustahkamlovchi o‘rganishdan foydalanadi. Ishlab chiqaruvchi uni GLM-4-Air-250414’ning tezroq varianti deb taqdim etadi; asosiy qismlardagi o‘zgarishlar ko‘rsatmani dastlab qayta ishlash va avtoregressiv dekodlashni qamraydi. Bu muayyan joylashtirish uchun o‘lchangan kechikish yoki parallellik kafolati emas, ishlab chiqaruvchi loyihasining tavsifidir.

Foydali so‘rovni tayyorlash

AirX’ga bitta chegaralangan vazifa va ixcham kutilgan materialni bering. Kod uchun funksiya, tegishli kirish qiymatlari va talab etilgan xatti-harakatni kiriting, so‘ng tuzatilgan parcha va uning izohini so‘rang. Tuzilgan ajratishda maydon ta’riflarini parchadan oldin joylang va yo‘q ma’lumotni ochiq belgilashni so‘rang. Natijani e’lon qilingan 4K chiqish profiliga mos rejalang; bitta javobdan cheklanmagan hujjat kutish o‘rniga katta hisobotni aniq vazifalarga bo‘ling.

", "aboutText": "### Umumiy tavsif va model profili\n\nGLM-4-AirX — Z.ai’ning GLM-4 Air qatoridagi yuqori tezlikli a’zo. Ishlab chiqaruvchi uni kam kechikish va dasturdagi parallel vazifalarga yo‘naltirib, ko‘rsatmalarga rioya qilish, injiniring kodi va funksiya chaqiruvlariga alohida e’tibor beradi. Model matn qabul qiladi va matn qaytaradi. Uning o‘z profilida 8K tokenlik kontekst oynasi va eng katta 4K tokenlik chiqish ko‘rsatilgan. Bular GLM-4-Air-250414 uchun e’lon qilingan katta kontekstdan farq qiladi; AirX o‘zining e’lon qilingan profilini saqlaydi.\n\nInteraktiv yordamchi uchun bu aniq yo‘naltirilgan so‘rovlarni anglatadi: funksiyani izohlash, xabarni tasniflash, berilgan abzasdan maydonlarni ajratish yoki texnik ko‘rsatmani qisqa javobga aylantirish. Tegishli parcha, joriy savol va kerakli natijani birga bering. Katta fon to‘plami sig‘adi deb taxmin qilish o‘rniga to‘liq suhbat va vosita kuzatuvlarini faol kontekst ichida saqlang.\n\n### Qo‘llanadigan imkoniyatlar\n\nGLM-4 tavsifida oqimli matn, funksiya chaqiruvi, kontekst keshi, JSON kabi tuzilgan formatlar va MCP vositalari hamda ma’lumot manbalari bilan integratsiya ko‘rsatilgan. Oqim javobni yaratilishi bilan ko‘rsatadi; dastur butun javob kelishidan oldin foydali matnni namoyish qilishi mumkin. U yetkazishni o‘zgartiradi, modelning kirish yoki chiqish turini emas.\n\nFunksiya chaqiruvi modelga dastur ta’riflagan funksiyalardan amal taklif qilish imkonini beradi. Dastur bu amalni bajaradi va kuzatuvni qaytaradi. Mos sikl — kichik ma’lumot qidiruvi, so‘ng qaytgan ma’lumotning qisqa izohi. MCP integratsiyasi ham dastur taqdim etgan vosita va ma’lumotlarga bog‘liq. Kontekst keshi takroriy kirishdan qayta foydalanishga tegishli; u har bir takroriy suhbat keshga tushganini anglatmaydi.\n\nJSON natijasi uchun materialni berishdan oldin maydonlar va ularning ma’nosini ko‘rsating. Masalan, texnik so‘rovni bering va uning toifasi, talab qilinadigan amal va javobsiz savollarini so‘rang. Olingan tuzilma dasturdagi keyingi qadamga foydalidir, lekin qiymatlar berilgan matn bilan tekshirilishi kerak.\n\n### Tavsiya etilgan vaziyatlar\n\nIshlab chiqaruvchi GLM-4 qo‘llanishlariga tarjima, semantik tasnif, hujjat maydonlarini ajratish, marketing matni, tahliliy hisobot va safar rejalashni kiritadi. AirX’ning qisqaroq profili bunday jarayonning bevosita qadamiga mos keladi. Tarjima so‘rovi qisqa parcha, auditoriya va terminologiyani berishi mumkin; chiqish talab qilingan nomlarni saqlagan qayta yozilgan parcha bo‘ladi. Tasnif so‘rovi noma’lum baho so‘rash o‘rniga ruxsat etilgan belgilar va ta’riflarni berishi mumkin.\n\nAjratib olish uchun so‘ralgan identifikatorlar va boshqa maydonlarni belgilang va faqat tegishli manba parchasini kiriting. Yozish uchun mahsulot faktlari va mahsulot abzasi yoki kampaniya satri kabi bitta materialni bering. Hisobot yoki safar rejasi uchun joriy qadamning tegishli faktlari va cheklovlarini yetkazing. Yangi ma’lumot zarur bo‘lsa, uni dasturning qidiruv yoki ma’lumot vositasi beradi; keyin model qaytgan material bilan ishlaydi.\n\n### Batafsil tayyorlash va tezlik yo‘nalishi\n\nAirX 15 trillion token, jumladan katta hajmdagi sintetik mulohaza materialida dastlab o‘qitilgan deb ta’riflanadi. Keyingi o‘qitishi ko‘rsatmalar, injiniring kodi va funksiyadan foydalanishni kuchaytirish uchun dialog afzalliklariga moslashtirish, rad etishli tanlash va mustahkamlovchi o‘rganishdan foydalanadi. Ishlab chiqaruvchi uni GLM-4-Air-250414’ning tezroq varianti deb taqdim etadi; asosiy qismlardagi o‘zgarishlar ko‘rsatmani dastlab qayta ishlash va avtoregressiv dekodlashni qamraydi. Bu muayyan joylashtirish uchun o‘lchangan kechikish yoki parallellik kafolati emas, ishlab chiqaruvchi loyihasining tavsifidir.\n\n### Foydali so‘rovni tayyorlash\n\nAirX’ga bitta chegaralangan vazifa va ixcham kutilgan materialni bering. Kod uchun funksiya, tegishli kirish qiymatlari va talab etilgan xatti-harakatni kiriting, so‘ng tuzatilgan parcha va uning izohini so‘rang. Tuzilgan ajratishda maydon ta’riflarini parchadan oldin joylang va yo‘q ma’lumotni ochiq belgilashni so‘rang. Natijani e’lon qilingan 4K chiqish profiliga mos rejalang; bitta javobdan cheklanmagan hujjat kutish o‘rniga katta hisobotni aniq vazifalarga bo‘ling.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "b238799bc78fa66af03cada1eee223cab259a548036e4c6662d246739a466d78", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
Z

GLM-4-AirX

Z.ai /glm-4-airx

Boshlash

GLM-4-AirX — kam kechikishli dialog, kodlash va vosita yordamidagi jarayonlar uchun Z.ai’ning tezlikka yo‘naltirilgan matn modeli. E’lon qilingan profilida 8K kontekst va 4K gacha chiqish belgilangan; bu uni qisqa, tuzilgan natijali aniq vazifalarga mos qiladi.

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.

Umumiy tavsif va model profili

GLM-4-AirX — Z.ai’ning GLM-4 Air qatoridagi yuqori tezlikli a’zo. Ishlab chiqaruvchi uni kam kechikish va dasturdagi parallel vazifalarga yo‘naltirib, ko‘rsatmalarga rioya qilish, injiniring kodi va funksiya chaqiruvlariga alohida e’tibor beradi. Model matn qabul qiladi va matn qaytaradi. Uning o‘z profilida 8K tokenlik kontekst oynasi va eng katta 4K tokenlik chiqish ko‘rsatilgan. Bular GLM-4-Air-250414 uchun e’lon qilingan katta kontekstdan farq qiladi; AirX o‘zining e’lon qilingan profilini saqlaydi.

Interaktiv yordamchi uchun bu aniq yo‘naltirilgan so‘rovlarni anglatadi: funksiyani izohlash, xabarni tasniflash, berilgan abzasdan maydonlarni ajratish yoki texnik ko‘rsatmani qisqa javobga aylantirish. Tegishli parcha, joriy savol va kerakli natijani birga bering. Katta fon to‘plami sig‘adi deb taxmin qilish o‘rniga to‘liq suhbat va vosita kuzatuvlarini faol kontekst ichida saqlang.

Qo‘llanadigan imkoniyatlar

GLM-4 tavsifida oqimli matn, funksiya chaqiruvi, kontekst keshi, JSON kabi tuzilgan formatlar va MCP vositalari hamda ma’lumot manbalari bilan integratsiya ko‘rsatilgan. Oqim javobni yaratilishi bilan ko‘rsatadi; dastur butun javob kelishidan oldin foydali matnni namoyish qilishi mumkin. U yetkazishni o‘zgartiradi, modelning kirish yoki chiqish turini emas.

Funksiya chaqiruvi modelga dastur ta’riflagan funksiyalardan amal taklif qilish imkonini beradi. Dastur bu amalni bajaradi va kuzatuvni qaytaradi. Mos sikl — kichik ma’lumot qidiruvi, so‘ng qaytgan ma’lumotning qisqa izohi. MCP integratsiyasi ham dastur taqdim etgan vosita va ma’lumotlarga bog‘liq. Kontekst keshi takroriy kirishdan qayta foydalanishga tegishli; u har bir takroriy suhbat keshga tushganini anglatmaydi.

JSON natijasi uchun materialni berishdan oldin maydonlar va ularning ma’nosini ko‘rsating. Masalan, texnik so‘rovni bering va uning toifasi, talab qilinadigan amal va javobsiz savollarini so‘rang. Olingan tuzilma dasturdagi keyingi qadamga foydalidir, lekin qiymatlar berilgan matn bilan tekshirilishi kerak.

Tavsiya etilgan vaziyatlar

Ishlab chiqaruvchi GLM-4 qo‘llanishlariga tarjima, semantik tasnif, hujjat maydonlarini ajratish, marketing matni, tahliliy hisobot va safar rejalashni kiritadi. AirX’ning qisqaroq profili bunday jarayonning bevosita qadamiga mos keladi. Tarjima so‘rovi qisqa parcha, auditoriya va terminologiyani berishi mumkin; chiqish talab qilingan nomlarni saqlagan qayta yozilgan parcha bo‘ladi. Tasnif so‘rovi noma’lum baho so‘rash o‘rniga ruxsat etilgan belgilar va ta’riflarni berishi mumkin.

Ajratib olish uchun so‘ralgan identifikatorlar va boshqa maydonlarni belgilang va faqat tegishli manba parchasini kiriting. Yozish uchun mahsulot faktlari va mahsulot abzasi yoki kampaniya satri kabi bitta materialni bering. Hisobot yoki safar rejasi uchun joriy qadamning tegishli faktlari va cheklovlarini yetkazing. Yangi ma’lumot zarur bo‘lsa, uni dasturning qidiruv yoki ma’lumot vositasi beradi; keyin model qaytgan material bilan ishlaydi.

Batafsil tayyorlash va tezlik yo‘nalishi

AirX 15 trillion token, jumladan katta hajmdagi sintetik mulohaza materialida dastlab o‘qitilgan deb ta’riflanadi. Keyingi o‘qitishi ko‘rsatmalar, injiniring kodi va funksiyadan foydalanishni kuchaytirish uchun dialog afzalliklariga moslashtirish, rad etishli tanlash va mustahkamlovchi o‘rganishdan foydalanadi. Ishlab chiqaruvchi uni GLM-4-Air-250414’ning tezroq varianti deb taqdim etadi; asosiy qismlardagi o‘zgarishlar ko‘rsatmani dastlab qayta ishlash va avtoregressiv dekodlashni qamraydi. Bu muayyan joylashtirish uchun o‘lchangan kechikish yoki parallellik kafolati emas, ishlab chiqaruvchi loyihasining tavsifidir.

Foydali so‘rovni tayyorlash

AirX’ga bitta chegaralangan vazifa va ixcham kutilgan materialni bering. Kod uchun funksiya, tegishli kirish qiymatlari va talab etilgan xatti-harakatni kiriting, so‘ng tuzatilgan parcha va uning izohini so‘rang. Tuzilgan ajratishda maydon ta’riflarini parchadan oldin joylang va yo‘q ma’lumotni ochiq belgilashni so‘rang. Natijani e’lon qilingan 4K chiqish profiliga mos rejalang; bitta javobdan cheklanmagan hujjat kutish o‘rniga katta hisobotni aniq vazifalarga bo‘ling.

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
glm-4-airx
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
Standart tarif Tezkor rejim tanlanganda
Flex rejimi
Standart tarif flex rejimi tanlanganda
Maksimal fikrlash darajasi
Standart tarif 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 jadvaliKontekst tokenlari soni bundan ko‘pKontekst tokenlari soni bundan oshmaydi, chegara kiradiTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifKontekst tokenlari soni bundan ko‘p—Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘qTo‘lov
Kirish · Kirish tokenlari $0.0028 / 1K token
Chiqish · Chiqish tokenlari $0.0028 / 1K token
Kirish · Keshdan o‘qish — / 1K token
Kirish · Keshga yozish — / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token
SozlamaSo‘rov uzunligi bo‘yicha 1-darajaTariflar jadvali—Kontekst tokenlari soni bundan ko‘p0Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘qTo‘lov
Kirish · Kirish tokenlari $0.0028 / 1K token
Chiqish · Chiqish tokenlari $0.0028 / 1K token
Kirish · Keshdan o‘qish — / 1K token
Kirish · Keshga yozish — / 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":"glm-4-airx","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 glm-4-airx

Usul yo‘llari taxalluslari

/chat/completions

Chaqirish uchun tegishli ID lar

glm-4-airx

", "method_identity": { "effective_request_model_id": "glm-4-airx", "fmt": "chat", "http": "POST", "path": "/v1/chat/completions", "request_model_id": "glm-4-airx", "route_label": "Ultra" }, "method_key": "Ultra|chat|/v1/chat/completions|glm-4-airx", "ordinal": 0, "rawMethod": { "available": null, "cache_read": null, "cache_write": null, "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": [ "glm-4-airx" ], "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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": { "max_context_tokens_inclusive": null, "min_context_tokens_exclusive": 0 }, "label": "So‘rov uzunligi bo‘yicha 1-daraja", "units": [ { "amount": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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" } ] } ], "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\":\"glm-4-airx\",\"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=\"glm-4-airx\", 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": 1, "flex_multiplier": 1, "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": 2.8, "input": [ "text" ], "label": "OpenAI Chat", "max_out": null, "max_reasoning_effort_multiplier": 1, "method_aliases": [ "/chat/completions" ], "out": 2.8, "output": [ "text" ], "path": "/v1/chat/completions", "price_conditions": [ { "label": "Tezkor rejim", "value": "Standart tarif", "when": "Tezkor rejim tanlanganda" }, { "label": "Flex rejimi", "value": "Standart tarif", "when": "flex rejimi tanlanganda" }, { "label": "Maksimal fikrlash darajasi", "value": "Standart tarif", "when": "Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda" } ], "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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": { "max_context_tokens_inclusive": null, "min_context_tokens_exclusive": 0 }, "label": "Request-length tier 1", "units": [ { "amount": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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" } ] } ], "request_model_id": "glm-4-airx", "route_label": "Ultra", "status_reads": [], "tiers": [ { "cache_read": null, "cache_read_multiplier": null, "cache_write": null, "cache_write_1h": null, "cache_write_multiplier": null, "created_at": "0001-01-01T00:00:00Z", "in": 2.8, "input_multiplier": null, "interval_boundary": "min_exclusive_max_inclusive", "max": null, "max_tokens": null, "min": 0, "min_tokens": 0, "null_rate_policy": "unknown or inherited; never zero", "out": 2.8, "output_multiplier": null, "per_request_price": null, "sort_order": 0, "tier_label": "", "updated_at": "0001-01-01T00:00:00Z" } ], "units": [ { "amount": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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" } ] } }, { "html": "
Z

GLM-4-AirX

Z.ai /glm-4-airx

Boshlash

GLM-4-AirX — kam kechikishli dialog, kodlash va vosita yordamidagi jarayonlar uchun Z.ai’ning tezlikka yo‘naltirilgan matn modeli. E’lon qilingan profilida 8K kontekst va 4K gacha chiqish belgilangan; bu uni qisqa, tuzilgan natijali aniq vazifalarga mos qiladi.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
json · sse
Model haqida

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

Umumiy tavsif va model profili

GLM-4-AirX — Z.ai’ning GLM-4 Air qatoridagi yuqori tezlikli a’zo. Ishlab chiqaruvchi uni kam kechikish va dasturdagi parallel vazifalarga yo‘naltirib, ko‘rsatmalarga rioya qilish, injiniring kodi va funksiya chaqiruvlariga alohida e’tibor beradi. Model matn qabul qiladi va matn qaytaradi. Uning o‘z profilida 8K tokenlik kontekst oynasi va eng katta 4K tokenlik chiqish ko‘rsatilgan. Bular GLM-4-Air-250414 uchun e’lon qilingan katta kontekstdan farq qiladi; AirX o‘zining e’lon qilingan profilini saqlaydi.

Interaktiv yordamchi uchun bu aniq yo‘naltirilgan so‘rovlarni anglatadi: funksiyani izohlash, xabarni tasniflash, berilgan abzasdan maydonlarni ajratish yoki texnik ko‘rsatmani qisqa javobga aylantirish. Tegishli parcha, joriy savol va kerakli natijani birga bering. Katta fon to‘plami sig‘adi deb taxmin qilish o‘rniga to‘liq suhbat va vosita kuzatuvlarini faol kontekst ichida saqlang.

Qo‘llanadigan imkoniyatlar

GLM-4 tavsifida oqimli matn, funksiya chaqiruvi, kontekst keshi, JSON kabi tuzilgan formatlar va MCP vositalari hamda ma’lumot manbalari bilan integratsiya ko‘rsatilgan. Oqim javobni yaratilishi bilan ko‘rsatadi; dastur butun javob kelishidan oldin foydali matnni namoyish qilishi mumkin. U yetkazishni o‘zgartiradi, modelning kirish yoki chiqish turini emas.

Funksiya chaqiruvi modelga dastur ta’riflagan funksiyalardan amal taklif qilish imkonini beradi. Dastur bu amalni bajaradi va kuzatuvni qaytaradi. Mos sikl — kichik ma’lumot qidiruvi, so‘ng qaytgan ma’lumotning qisqa izohi. MCP integratsiyasi ham dastur taqdim etgan vosita va ma’lumotlarga bog‘liq. Kontekst keshi takroriy kirishdan qayta foydalanishga tegishli; u har bir takroriy suhbat keshga tushganini anglatmaydi.

JSON natijasi uchun materialni berishdan oldin maydonlar va ularning ma’nosini ko‘rsating. Masalan, texnik so‘rovni bering va uning toifasi, talab qilinadigan amal va javobsiz savollarini so‘rang. Olingan tuzilma dasturdagi keyingi qadamga foydalidir, lekin qiymatlar berilgan matn bilan tekshirilishi kerak.

Tavsiya etilgan vaziyatlar

Ishlab chiqaruvchi GLM-4 qo‘llanishlariga tarjima, semantik tasnif, hujjat maydonlarini ajratish, marketing matni, tahliliy hisobot va safar rejalashni kiritadi. AirX’ning qisqaroq profili bunday jarayonning bevosita qadamiga mos keladi. Tarjima so‘rovi qisqa parcha, auditoriya va terminologiyani berishi mumkin; chiqish talab qilingan nomlarni saqlagan qayta yozilgan parcha bo‘ladi. Tasnif so‘rovi noma’lum baho so‘rash o‘rniga ruxsat etilgan belgilar va ta’riflarni berishi mumkin.

Ajratib olish uchun so‘ralgan identifikatorlar va boshqa maydonlarni belgilang va faqat tegishli manba parchasini kiriting. Yozish uchun mahsulot faktlari va mahsulot abzasi yoki kampaniya satri kabi bitta materialni bering. Hisobot yoki safar rejasi uchun joriy qadamning tegishli faktlari va cheklovlarini yetkazing. Yangi ma’lumot zarur bo‘lsa, uni dasturning qidiruv yoki ma’lumot vositasi beradi; keyin model qaytgan material bilan ishlaydi.

Batafsil tayyorlash va tezlik yo‘nalishi

AirX 15 trillion token, jumladan katta hajmdagi sintetik mulohaza materialida dastlab o‘qitilgan deb ta’riflanadi. Keyingi o‘qitishi ko‘rsatmalar, injiniring kodi va funksiyadan foydalanishni kuchaytirish uchun dialog afzalliklariga moslashtirish, rad etishli tanlash va mustahkamlovchi o‘rganishdan foydalanadi. Ishlab chiqaruvchi uni GLM-4-Air-250414’ning tezroq varianti deb taqdim etadi; asosiy qismlardagi o‘zgarishlar ko‘rsatmani dastlab qayta ishlash va avtoregressiv dekodlashni qamraydi. Bu muayyan joylashtirish uchun o‘lchangan kechikish yoki parallellik kafolati emas, ishlab chiqaruvchi loyihasining tavsifidir.

Foydali so‘rovni tayyorlash

AirX’ga bitta chegaralangan vazifa va ixcham kutilgan materialni bering. Kod uchun funksiya, tegishli kirish qiymatlari va talab etilgan xatti-harakatni kiriting, so‘ng tuzatilgan parcha va uning izohini so‘rang. Tuzilgan ajratishda maydon ta’riflarini parchadan oldin joylang va yo‘q ma’lumotni ochiq belgilashni so‘rang. Natijani e’lon qilingan 4K chiqish profiliga mos rejalang; bitta javobdan cheklanmagan hujjat kutish o‘rniga katta hisobotni aniq vazifalarga bo‘ling.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · Anthropic Messages POST /v1/messages
Kirish
text
Chiqish
json, sse
Chiqish formati
JSON yoki SSE
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
glm-4-airx
Standart—
MaydonmessagesTurmessage[]TalabMajburiyQiymatlar va cheklovlarStandart—
Maydonmessages[].roleTurstringTalabMajburiyQiymatlar va cheklovlar
user, assistant
Standart—
Maydonmessages[].contentTurstringTalabMajburiyQiymatlar va cheklovlarStandart—
Maydonmax_tokensTurintegerTalabMajburiyQiymatlar va cheklovlarStandart—
MaydonstreamTurbooleanTalabIxtiyoriyQiymatlar va cheklovlarStandartfalse
Kirish materiallari birikmalari

Matn · UTF-8 matni

messages[].content satri

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrcontent_typeParametr talablariapplication/json
ParametrrequiredParametr talablarimodel, messages, max_tokens
Parametroptional_controlsParametr talablaristream
Parametrmaterial_typesParametr talablaritext
Parametrtext_file_materialParametr talablariHaqiqiy UTF-8 hujjatini o‘qing va uning matnini ko‘rsatilgan kirish yoki xabar kontenti satriga joylashtiring.

Hayot sikli: synchronous_or_streamed

Tariflar

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

Tezkor rejim
Standart tarif Tezkor rejim tanlanganda
Flex rejimi
Standart tarif flex rejimi tanlanganda
Maksimal fikrlash darajasi
Standart tarif 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 jadvaliKontekst tokenlari soni bundan ko‘pKontekst tokenlari soni bundan oshmaydi, chegara kiradiTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifKontekst tokenlari soni bundan ko‘p—Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘qTo‘lov
Kirish · Kirish tokenlari $0.0028 / 1K token
Chiqish · Chiqish tokenlari $0.0028 / 1K token
Kirish · Keshdan o‘qish — / 1K token
Kirish · Keshga yozish — / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token
SozlamaSo‘rov uzunligi bo‘yicha 1-darajaTariflar jadvali—Kontekst tokenlari soni bundan ko‘p0Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘qTo‘lov
Kirish · Kirish tokenlari $0.0028 / 1K token
Chiqish · Chiqish tokenlari $0.0028 / 1K token
Kirish · Keshdan o‘qish — / 1K token
Kirish · Keshga yozish — / 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 · Anthropic Messages

{\n  "model": "glm-4-airx",\n  "max_tokens": 128,\n  "messages": [\n    {\n      "role": "user",\n      "content": "Summarize the incident report."\n    }\n  ],\n  "stream": false\n}

Natijalarni olish va saqlash

Ailoot Ultra · Anthropic Messages

JSON yoki SSE

Barcha tayyor natijalarni saqlash

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

Barcha kontent bloklari, fikrlash imzolari va haqiqiy vosita ishlatish IDlarini saqlang. SSE bloklarining barcha o‘zgarish qismlarini yig‘ing; birinchi matn bloki to‘liq chiqish emas.

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
MaydonidTurstring
MaydonmodelTurstring
Maydoncontent[]Turtext|thinking|tool_use|other content block
Maydonstop_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 glm-4-airx

Usul yo‘llari taxalluslari

/chat/completions

Chaqirish uchun tegishli ID lar

glm-4-airx

", "method_identity": { "effective_request_model_id": "glm-4-airx", "fmt": "messages", "http": "POST", "path": "/v1/messages", "request_model_id": "glm-4-airx", "route_label": "Ultra" }, "method_key": "Ultra|messages|/v1/messages|glm-4-airx", "ordinal": 1, "rawMethod": { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "authentication": { "api_key_source": "AILOOT_API_KEY", "headers": [ "Authorization: Bearer", "x-api-key", "anthropic-version" ], "version_example": "2023-06-01" }, "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" } ], "example_body": { "max_tokens": 128, "messages": [ { "content": "Summarize the incident report.", "role": "user" } ], "model": "glm-4-airx", "stream": false }, "lifecycle": { "create": { "http": "POST", "path": "/v1/messages" }, "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 satri", "required_material": "UTF-8 matni" } ], "operation_modes": [ "synchronous_json", "streamed_SSE" ], "outputs": { "all_results": "Barcha kontent bloklari, fikrlash imzolari va haqiqiy vosita ishlatish IDlarini saqlang. SSE bloklarining barcha o‘zgarish qismlarini yig‘ing; birinchi matn bloki to‘liq chiqish emas.", "family": "protocol", "fields": [ { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "content[]", "type": "text|thinking|tool_use|other content block" }, { "description": null, "name": "stop_reason", "type": "string|null" }, { "description": null, "name": "usage", "type": "object" } ], "format": "JSON yoki SSE" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "glm-4-airx" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages", "required": true, "type": "message[]" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "user", "assistant" ], "enum_known": true, "name": "messages[].role", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].content", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "max_tokens", "required": true, "type": "integer" }, { "availability": "source_interface", "default": false, "default_known": true, "enum": null, "enum_known": false, "name": "stream", "required": false, "type": "boolean" } ], "profile_binding": "Documented current stock Messages bridge; optional model capabilities are separate from this minimal text request.", "request_schema": { "additionalProperties": false, "properties": { "max_tokens": { "minimum": 1, "type": "integer" }, "messages": { "items": { "additionalProperties": false, "properties": { "content": { "oneOf": [ { "type": "string" }, { "items": { "oneOf": [ { "properties": { "text": { "type": "string" }, "type": { "const": "text" } }, "required": [ "type", "text" ], "type": "object" }, { "properties": { "source": { "oneOf": [ { "properties": { "type": { "const": "url" }, "url": { "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "properties": { "data": { "type": "string" }, "media_type": { "type": "string" }, "type": { "const": "base64" } }, "required": [ "type", "media_type", "data" ], "type": "object" } ] }, "type": { "const": "image" } }, "required": [ "type", "source" ], "type": "object" } ] }, "type": "array" } ] }, "role": { "enum": [ "user", "assistant" ], "type": "string" } }, "required": [ "role", "content" ], "type": "object" }, "minItems": 1, "type": "array" }, "model": { "enum": [ "glm-4-airx" ], "type": "string" }, "stream": { "default": false, "type": "boolean" } }, "required": [ "model", "messages", "max_tokens" ], "type": "object" }, "schema_template": { "max_tokens": 128, "messages": [ { "content": "Summarize the incident report.", "role": "user" } ], "model": "glm-4-airx", "stream": false }, "unsupported": [ "No query API key.", "Hosted/server-side tools and model-specific optional controls are not part of this minimal family example." ] }, "input": { "content_type": "application/json", "material_types": [ "text" ], "optional_controls": [ "stream" ], "required": [ "model", "messages", "max_tokens" ], "text_file_material": "Haqiqiy UTF-8 hujjatini o‘qing va uning matnini ko‘rsatilgan kirish yoki xabar kontenti satriga joylashtiring." }, "lifecycle": { "create": { "http": "POST", "path": "/v1/messages" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "Keep request/response IDs and headers. No generic text job retrieval URL is supplied." }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/messages" }, "output": { "all_results": "Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.", "family": "protocol", "format": "JSON or SSE" } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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": { "max_context_tokens_inclusive": null, "min_context_tokens_exclusive": 0 }, "label": "So‘rov uzunligi bo‘yicha 1-daraja", "units": [ { "amount": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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" } ] } ], "examples": [ { "code": "{\n \"model\": \"glm-4-airx\",\n \"max_tokens\": 128,\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"Summarize the incident report.\"\n }\n ],\n \"stream\": false\n}", "label": "Complete minimal JSON body", "language": "json" }, { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'glm-4-airx', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H \"x-api-key: $AILOOT_API_KEY\" -H 'anthropic-version: 2023-06-01' -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\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\n", "label": "curl · use your UTF-8 document", "language": "bash" }, { "code": "import json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'glm-4-airx', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\n", "label": "Python · save the complete result", "language": "python" } ], "family": "anthropic", "fast_multiplier": 1, "flex_multiplier": 1, "fmt": "messages", "http": "POST", "in": 2.8, "input": [ "text" ], "label": "Anthropic Messages", "max_out": null, "max_reasoning_effort_multiplier": 1, "method_aliases": [], "out": 2.8, "output": [ "json", "sse" ], "path": "/v1/messages", "price_conditions": [ { "label": "Tezkor rejim", "value": "Standart tarif", "when": "Tezkor rejim tanlanganda" }, { "label": "Flex rejimi", "value": "Standart tarif", "when": "flex rejimi tanlanganda" }, { "label": "Maksimal fikrlash darajasi", "value": "Standart tarif", "when": "Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda" } ], "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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": { "max_context_tokens_inclusive": null, "min_context_tokens_exclusive": 0 }, "label": "Request-length tier 1", "units": [ { "amount": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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" } ] } ], "request_model_id": "glm-4-airx", "route_label": "Ultra", "status_reads": [], "tiers": [ { "cache_read": null, "cache_read_multiplier": null, "cache_write": null, "cache_write_1h": null, "cache_write_multiplier": null, "created_at": "0001-01-01T00:00:00Z", "in": 2.8, "input_multiplier": null, "max": null, "max_tokens": null, "min": 0, "min_tokens": 0, "out": 2.8, "output_multiplier": null, "per_request_price": null, "sort_order": 0, "tier_label": "", "updated_at": "0001-01-01T00:00:00Z" } ], "units": [ { "amount": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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" } ] } } ], "model_id": "glm-4-airx", "nativeProvenance": { "input_facts_canonical_sha256": "515b33a6d843c389900e58001d49cee381013c1e0d4483eb810bd7c1a982cfab", "input_html_sha256": "c6698ca06ec92be13496337429e575d8aae88829b590b4c8d6fccfe882967e8d", "private_audit_sha256": "92cf43bede01313c5a255ae78d54f63f37ae7a5d56f131c3da72856647cf0dec", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/glm-4-airx/", "description": "GLM-4-AirX — kam kechikishli dialog, kodlash va vosita yordamidagi jarayonlar uchun Z.ai’ning tezlikka yo‘naltirilgan matn modeli. E’lon qilingan profilida 8K kontekst va 4K gacha chiqish belgilangan; bu uni qisqa, tuzilgan natijali aniq vazifalarga mos qiladi.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/glm-4-airx/index.md", "title": "GLM-4-AirX — Ailoot", "url": "https://ailoot.ai/models/uz/glm-4-airx/" }, "rawModel": { "about": "### Umumiy tavsif va model profili\n\nGLM-4-AirX — Z.ai’ning GLM-4 Air qatoridagi yuqori tezlikli a’zo. Ishlab chiqaruvchi uni kam kechikish va dasturdagi parallel vazifalarga yo‘naltirib, ko‘rsatmalarga rioya qilish, injiniring kodi va funksiya chaqiruvlariga alohida e’tibor beradi. Model matn qabul qiladi va matn qaytaradi. Uning o‘z profilida 8K tokenlik kontekst oynasi va eng katta 4K tokenlik chiqish ko‘rsatilgan. Bular GLM-4-Air-250414 uchun e’lon qilingan katta kontekstdan farq qiladi; AirX o‘zining e’lon qilingan profilini saqlaydi.\n\nInteraktiv yordamchi uchun bu aniq yo‘naltirilgan so‘rovlarni anglatadi: funksiyani izohlash, xabarni tasniflash, berilgan abzasdan maydonlarni ajratish yoki texnik ko‘rsatmani qisqa javobga aylantirish. Tegishli parcha, joriy savol va kerakli natijani birga bering. Katta fon to‘plami sig‘adi deb taxmin qilish o‘rniga to‘liq suhbat va vosita kuzatuvlarini faol kontekst ichida saqlang.\n\n### Qo‘llanadigan imkoniyatlar\n\nGLM-4 tavsifida oqimli matn, funksiya chaqiruvi, kontekst keshi, JSON kabi tuzilgan formatlar va MCP vositalari hamda ma’lumot manbalari bilan integratsiya ko‘rsatilgan. Oqim javobni yaratilishi bilan ko‘rsatadi; dastur butun javob kelishidan oldin foydali matnni namoyish qilishi mumkin. U yetkazishni o‘zgartiradi, modelning kirish yoki chiqish turini emas.\n\nFunksiya chaqiruvi modelga dastur ta’riflagan funksiyalardan amal taklif qilish imkonini beradi. Dastur bu amalni bajaradi va kuzatuvni qaytaradi. Mos sikl — kichik ma’lumot qidiruvi, so‘ng qaytgan ma’lumotning qisqa izohi. MCP integratsiyasi ham dastur taqdim etgan vosita va ma’lumotlarga bog‘liq. Kontekst keshi takroriy kirishdan qayta foydalanishga tegishli; u har bir takroriy suhbat keshga tushganini anglatmaydi.\n\nJSON natijasi uchun materialni berishdan oldin maydonlar va ularning ma’nosini ko‘rsating. Masalan, texnik so‘rovni bering va uning toifasi, talab qilinadigan amal va javobsiz savollarini so‘rang. Olingan tuzilma dasturdagi keyingi qadamga foydalidir, lekin qiymatlar berilgan matn bilan tekshirilishi kerak.\n\n### Tavsiya etilgan vaziyatlar\n\nIshlab chiqaruvchi GLM-4 qo‘llanishlariga tarjima, semantik tasnif, hujjat maydonlarini ajratish, marketing matni, tahliliy hisobot va safar rejalashni kiritadi. AirX’ning qisqaroq profili bunday jarayonning bevosita qadamiga mos keladi. Tarjima so‘rovi qisqa parcha, auditoriya va terminologiyani berishi mumkin; chiqish talab qilingan nomlarni saqlagan qayta yozilgan parcha bo‘ladi. Tasnif so‘rovi noma’lum baho so‘rash o‘rniga ruxsat etilgan belgilar va ta’riflarni berishi mumkin.\n\nAjratib olish uchun so‘ralgan identifikatorlar va boshqa maydonlarni belgilang va faqat tegishli manba parchasini kiriting. Yozish uchun mahsulot faktlari va mahsulot abzasi yoki kampaniya satri kabi bitta materialni bering. Hisobot yoki safar rejasi uchun joriy qadamning tegishli faktlari va cheklovlarini yetkazing. Yangi ma’lumot zarur bo‘lsa, uni dasturning qidiruv yoki ma’lumot vositasi beradi; keyin model qaytgan material bilan ishlaydi.\n\n### Batafsil tayyorlash va tezlik yo‘nalishi\n\nAirX 15 trillion token, jumladan katta hajmdagi sintetik mulohaza materialida dastlab o‘qitilgan deb ta’riflanadi. Keyingi o‘qitishi ko‘rsatmalar, injiniring kodi va funksiyadan foydalanishni kuchaytirish uchun dialog afzalliklariga moslashtirish, rad etishli tanlash va mustahkamlovchi o‘rganishdan foydalanadi. Ishlab chiqaruvchi uni GLM-4-Air-250414’ning tezroq varianti deb taqdim etadi; asosiy qismlardagi o‘zgarishlar ko‘rsatmani dastlab qayta ishlash va avtoregressiv dekodlashni qamraydi. Bu muayyan joylashtirish uchun o‘lchangan kechikish yoki parallellik kafolati emas, ishlab chiqaruvchi loyihasining tavsifidir.\n\n### Foydali so‘rovni tayyorlash\n\nAirX’ga bitta chegaralangan vazifa va ixcham kutilgan materialni bering. Kod uchun funksiya, tegishli kirish qiymatlari va talab etilgan xatti-harakatni kiriting, so‘ng tuzatilgan parcha va uning izohini so‘rang. Tuzilgan ajratishda maydon ta’riflarini parchadan oldin joylang va yo‘q ma’lumotni ochiq belgilashni so‘rang. Natijani e’lon qilingan 4K chiqish profiliga mos rejalang; bitta javobdan cheklanmagan hujjat kutish o‘rniga katta hisobotni aniq vazifalarga bo‘ling.", "alias_of": null, "aliases": [], "author": "Z.ai", "capabilities": { "context_length": 8000, "manufacturer_input": [], "manufacturer_output": [] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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", "messages" ], "id": "glm-4-airx", "input": [ "text" ], "kind": "text", "max_out": null, "methods": [ { "available": null, "cache_read": null, "cache_write": null, "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": [ "glm-4-airx" ], "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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": { "max_context_tokens_inclusive": null, "min_context_tokens_exclusive": 0 }, "label": "So‘rov uzunligi bo‘yicha 1-daraja", "units": [ { "amount": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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" } ] } ], "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\":\"glm-4-airx\",\"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=\"glm-4-airx\", 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": 1, "flex_multiplier": 1, "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": 2.8, "input": [ "text" ], "label": "OpenAI Chat", "max_out": null, "max_reasoning_effort_multiplier": 1, "method_aliases": [ "/chat/completions" ], "out": 2.8, "output": [ "text" ], "path": "/v1/chat/completions", "price_conditions": [ { "label": "Tezkor rejim", "value": "Standart tarif", "when": "Tezkor rejim tanlanganda" }, { "label": "Flex rejimi", "value": "Standart tarif", "when": "flex rejimi tanlanganda" }, { "label": "Maksimal fikrlash darajasi", "value": "Standart tarif", "when": "Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda" } ], "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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": { "max_context_tokens_inclusive": null, "min_context_tokens_exclusive": 0 }, "label": "Request-length tier 1", "units": [ { "amount": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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" } ] } ], "request_model_id": "glm-4-airx", "route_label": "Ultra", "status_reads": [], "tiers": [ { "cache_read": null, "cache_read_multiplier": null, "cache_write": null, "cache_write_1h": null, "cache_write_multiplier": null, "created_at": "0001-01-01T00:00:00Z", "in": 2.8, "input_multiplier": null, "interval_boundary": "min_exclusive_max_inclusive", "max": null, "max_tokens": null, "min": 0, "min_tokens": 0, "null_rate_policy": "unknown or inherited; never zero", "out": 2.8, "output_multiplier": null, "per_request_price": null, "sort_order": 0, "tier_label": "", "updated_at": "0001-01-01T00:00:00Z" } ], "units": [ { "amount": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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" } ] }, { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "authentication": { "api_key_source": "AILOOT_API_KEY", "headers": [ "Authorization: Bearer", "x-api-key", "anthropic-version" ], "version_example": "2023-06-01" }, "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" } ], "example_body": { "max_tokens": 128, "messages": [ { "content": "Summarize the incident report.", "role": "user" } ], "model": "glm-4-airx", "stream": false }, "lifecycle": { "create": { "http": "POST", "path": "/v1/messages" }, "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 satri", "required_material": "UTF-8 matni" } ], "operation_modes": [ "synchronous_json", "streamed_SSE" ], "outputs": { "all_results": "Barcha kontent bloklari, fikrlash imzolari va haqiqiy vosita ishlatish IDlarini saqlang. SSE bloklarining barcha o‘zgarish qismlarini yig‘ing; birinchi matn bloki to‘liq chiqish emas.", "family": "protocol", "fields": [ { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "content[]", "type": "text|thinking|tool_use|other content block" }, { "description": null, "name": "stop_reason", "type": "string|null" }, { "description": null, "name": "usage", "type": "object" } ], "format": "JSON yoki SSE" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "glm-4-airx" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages", "required": true, "type": "message[]" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "user", "assistant" ], "enum_known": true, "name": "messages[].role", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].content", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "max_tokens", "required": true, "type": "integer" }, { "availability": "source_interface", "default": false, "default_known": true, "enum": null, "enum_known": false, "name": "stream", "required": false, "type": "boolean" } ], "profile_binding": "Documented current stock Messages bridge; optional model capabilities are separate from this minimal text request.", "request_schema": { "additionalProperties": false, "properties": { "max_tokens": { "minimum": 1, "type": "integer" }, "messages": { "items": { "additionalProperties": false, "properties": { "content": { "oneOf": [ { "type": "string" }, { "items": { "oneOf": [ { "properties": { "text": { "type": "string" }, "type": { "const": "text" } }, "required": [ "type", "text" ], "type": "object" }, { "properties": { "source": { "oneOf": [ { "properties": { "type": { "const": "url" }, "url": { "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "properties": { "data": { "type": "string" }, "media_type": { "type": "string" }, "type": { "const": "base64" } }, "required": [ "type", "media_type", "data" ], "type": "object" } ] }, "type": { "const": "image" } }, "required": [ "type", "source" ], "type": "object" } ] }, "type": "array" } ] }, "role": { "enum": [ "user", "assistant" ], "type": "string" } }, "required": [ "role", "content" ], "type": "object" }, "minItems": 1, "type": "array" }, "model": { "enum": [ "glm-4-airx" ], "type": "string" }, "stream": { "default": false, "type": "boolean" } }, "required": [ "model", "messages", "max_tokens" ], "type": "object" }, "schema_template": { "max_tokens": 128, "messages": [ { "content": "Summarize the incident report.", "role": "user" } ], "model": "glm-4-airx", "stream": false }, "unsupported": [ "No query API key.", "Hosted/server-side tools and model-specific optional controls are not part of this minimal family example." ] }, "input": { "content_type": "application/json", "material_types": [ "text" ], "optional_controls": [ "stream" ], "required": [ "model", "messages", "max_tokens" ], "text_file_material": "Haqiqiy UTF-8 hujjatini o‘qing va uning matnini ko‘rsatilgan kirish yoki xabar kontenti satriga joylashtiring." }, "lifecycle": { "create": { "http": "POST", "path": "/v1/messages" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "Keep request/response IDs and headers. No generic text job retrieval URL is supplied." }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/messages" }, "output": { "all_results": "Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.", "family": "protocol", "format": "JSON or SSE" } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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": { "max_context_tokens_inclusive": null, "min_context_tokens_exclusive": 0 }, "label": "So‘rov uzunligi bo‘yicha 1-daraja", "units": [ { "amount": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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" } ] } ], "examples": [ { "code": "{\n \"model\": \"glm-4-airx\",\n \"max_tokens\": 128,\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"Summarize the incident report.\"\n }\n ],\n \"stream\": false\n}", "label": "Complete minimal JSON body", "language": "json" }, { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'glm-4-airx', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H \"x-api-key: $AILOOT_API_KEY\" -H 'anthropic-version: 2023-06-01' -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\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\n", "label": "curl · use your UTF-8 document", "language": "bash" }, { "code": "import json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'glm-4-airx', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\n", "label": "Python · save the complete result", "language": "python" } ], "family": "anthropic", "fast_multiplier": 1, "flex_multiplier": 1, "fmt": "messages", "http": "POST", "in": 2.8, "input": [ "text" ], "label": "Anthropic Messages", "max_out": null, "max_reasoning_effort_multiplier": 1, "method_aliases": [], "out": 2.8, "output": [ "json", "sse" ], "path": "/v1/messages", "price_conditions": [ { "label": "Tezkor rejim", "value": "Standart tarif", "when": "Tezkor rejim tanlanganda" }, { "label": "Flex rejimi", "value": "Standart tarif", "when": "flex rejimi tanlanganda" }, { "label": "Maksimal fikrlash darajasi", "value": "Standart tarif", "when": "Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda" } ], "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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": { "max_context_tokens_inclusive": null, "min_context_tokens_exclusive": 0 }, "label": "Request-length tier 1", "units": [ { "amount": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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" } ] } ], "request_model_id": "glm-4-airx", "route_label": "Ultra", "status_reads": [], "tiers": [ { "cache_read": null, "cache_read_multiplier": null, "cache_write": null, "cache_write_1h": null, "cache_write_multiplier": null, "created_at": "0001-01-01T00:00:00Z", "in": 2.8, "input_multiplier": null, "max": null, "max_tokens": null, "min": 0, "min_tokens": 0, "out": 2.8, "output_multiplier": null, "per_request_price": null, "sort_order": 0, "tier_label": "", "updated_at": "0001-01-01T00:00:00Z" } ], "units": [ { "amount": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.0000028", "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": "0.0028000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.0000028", "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" } ] } ], "model_aliases": [ { "canonical_model_id": "glm-4-airx", "request_model_id": "glm-4-airx", "route_label": "Ultra" } ], "name": "GLM-4-AirX", "price": { "in": 2.8, "in_label": null, "out": 2.8, "out_label": null, "scope_label": "Token rates", "sort_in": 2.8, "sort_out": 2.8, "varies": true }, "short": "GLM-4-AirX — kam kechikishli dialog, kodlash va vosita yordamidagi jarayonlar uchun Z.ai’ning tezlikka yo‘naltirilgan matn modeli. E’lon qilingan profilida 8K kontekst va 4K gacha chiqish belgilangan; bu uni qisqa, tuzilgan natijali aniq vazifalarga mos qiladi." }, "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 }, { "contract_json_pointer": "/methods/1/rawMethod/contract", "html_json_pointer": "/methods/1/html", "method_ordinal": 1 } ], "role": "current-semantic-reference" }, "raw_input": { "examples_role": "historical-input-not-current-ready-examples", "json_pointers": [ "/rawModel", "/methods/0/rawMethod", "/methods/1/rawMethod" ], "role": "historical-owner-input" }, "schema": "ailoot-native-model-representations-v1" }, "schema": "ailoot-static-model-facts-v1", "schema_version": 1, "shortText": "GLM-4-AirX — kam kechikishli dialog, kodlash va vosita yordamidagi jarayonlar uchun Z.ai’ning tezlikka yo‘naltirilgan matn modeli. E’lon qilingan profilida 8K kontekst va 4K gacha chiqish belgilangan; bu uni qisqa, tuzilgan natijali aniq vazifalarga mos qiladi.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "4b224be22d2e2323f1cc2ae4f455fcee1838ab019009b0681fb9da19082d5a2d", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "6441300af7f0ce3c5c97a3b030b8532115a3b1b874222dd46121d60252a2b45d" }