Claude Opus 4.8 davomli agent ishi, professional tahlil, vizual tushunish va ilova taqdim etadigan xotiradan foydalanishni birlashtiradi. Anthropic xulosalarni tekshirishda modelning xatti-harakatini alohida ta’kidlaydi: u dalillar yetishmasligini payqash, noaniqlik haqida xabar berish va yetarli asos bo‘lmasa, ish bajarilganini da’vo qilmaslikka o‘rgatilgan.
Muhandislik vazifasida model bir nechta bog‘liq komponentni o‘rganishi, rejani ko‘rib chiqishi, xatolarni aniqlashi va yangi ma’lumotlar olingandan keyin bajarishni davom ettirishi mumkin. Hujjat tahlilida uning zich mazmun bilan ishlash qobiliyati hamda kiruvchi materiallar va xulosalardagi muammolarga e’tibori muhim. Tavsiflangan professional yo‘nalishlar orasida tadqiqot, tahlil, hujjatlar bilan ishlash va taqdimot mazmunini tayyorlash bor.
Ko‘rsatmalar, ayniqsa, past sa’y-harakat darajalarida so‘zma-so‘z talqin qilinadi. Model bitta elementga tegishli shartni qolganlariga bildirmasdan tatbiq etishi shart emas: amal qilish doirasi, format va natija mezonlarini aniq belgilash yaxshiroq. Kodni tekshirishda bu belgilangan jiddiylik chegarasi topilgan muammolarning qaysi biri javobga kirishiga ta’sir qilishini anglatadi. Barcha topilmalar haqida xabar berish talabi ularni saralash talabidan alohida ifodalanishi kerak.
Opus 4.8 ilovada yoqish kerak bo‘lgan adaptiv mulohaza yuritishni qo‘llab-quvvatlaydi. Bu versiya alohida budget_tokens bilan qo‘lda boshqariladigan extended thinking’ni qo‘llab-quvvatlamaydi. effort darajalari — low, medium, high, xhigh va max; standart daraja — high. Anthropic ma’lumotiga ko‘ra, chuqurroq darajalar tahlil hajmi, vositalarga murojaatlar va ish vaqtini oshirishi mumkin, qisqa, chegaralangan vazifalarda esa pastroq puxtalik darajasi foydali.
Model batafsillikni topshiriqni qanchalik murakkab deb bilishiga qarab tanlaydi: oddiy savol qisqa, ochiq tahlil esa keng javob olishi mumkin. Muayyan hajm yoki matn uslubi kerak bo‘lsa, ularni aniq, imkon qadar kerakli natija namunasi bilan belgilash lozim. Vositalar bilan davomli ishda bajarish jarayoni haqida oraliq xabarlar ko‘zda tutilgan; ularning mazmuni va chastotasini ham ko‘rsatma bilan yo‘naltirish mumkin.
Kirishga matn va tasvirlar beriladi, natija matn shaklida hosil qilinadi. Kontekst oynasi — 1 mln token, oddiy rejimda maksimal javob — 128 ming token. Ichki bilimlarning ishonchli chegarasi — 2026-yil yanvar.
Ishlab chiqaruvchida oddiy vizual kirish formatlari: JPEG/PNG/GIF/WebP, animatsiyaning birinchi kadri. Nativ tahlilda: uzun tomon — 2576 piksel, 4784 vizual token; katta tasvirlar tahlildan oldin kichraytiriladi. Fayllar va parametrlarning qo‘llab-quvvatlanishi ilova metodiga bog‘liq.
Veb-qidiruv, buyruqlarni bajarish, fayl xotirasi, interfeysni boshqarish va mustaqil kichik vazifalar ilovada qo‘llab-quvvatlanishni talab qiladi. Model mavjud vositalardan foydalanishi mumkin, lekin sa’y-harakat darajasi va vositalar vazifasining aniq tavsifi amallarni tanlashga ta’sir qiladi. Bu, ayniqsa, axborot qidirish, tahlil va texnik bajarishni bog‘laydigan ishda foydali.
Interfeyslar va slaydlar yaratishda Opus 4.8 bezatish bo‘yicha muayyan uslublarni muntazam afzal ko‘radi. Muayyan uslubdagi natija uchun aniq palitra, tipografika, kompozitsiya va harakat xususiyatini ko‘rsatish yoki avval bir nechta yo‘nalish so‘rash foydali. Tayyor fayl yoki ishlaydigan interfeys muhit vositalari orqali paydo bo‘ladi; modelning o‘z chiqishi matnli bo‘lib qoladi.
", "aboutText": "Claude Opus 4.8 davomli agent ishi, professional tahlil, vizual tushunish va ilova taqdim etadigan xotiradan foydalanishni birlashtiradi. Anthropic xulosalarni tekshirishda modelning xatti-harakatini alohida ta’kidlaydi: u dalillar yetishmasligini payqash, noaniqlik haqida xabar berish va yetarli asos bo‘lmasa, ish bajarilganini da’vo qilmaslikka o‘rgatilgan.\n\n### Chegaralarni aniqlashtirish va ishni yakuniga yetkazish\n\nMuhandislik vazifasida model bir nechta bog‘liq komponentni o‘rganishi, rejani ko‘rib chiqishi, xatolarni aniqlashi va yangi ma’lumotlar olingandan keyin bajarishni davom ettirishi mumkin. Hujjat tahlilida uning zich mazmun bilan ishlash qobiliyati hamda kiruvchi materiallar va xulosalardagi muammolarga e’tibori muhim. Tavsiflangan professional yo‘nalishlar orasida tadqiqot, tahlil, hujjatlar bilan ishlash va taqdimot mazmunini tayyorlash bor.\n\nKo‘rsatmalar, ayniqsa, past sa’y-harakat darajalarida so‘zma-so‘z talqin qilinadi. Model bitta elementga tegishli shartni qolganlariga bildirmasdan tatbiq etishi shart emas: amal qilish doirasi, format va natija mezonlarini aniq belgilash yaxshiroq. Kodni tekshirishda bu belgilangan jiddiylik chegarasi topilgan muammolarning qaysi biri javobga kirishiga ta’sir qilishini anglatadi. Barcha topilmalar haqida xabar berish talabi ularni saralash talabidan alohida ifodalanishi kerak.\n\n### Mulohaza yuritish va bayonni sozlash\n\nOpus 4.8 ilovada yoqish kerak bo‘lgan adaptiv mulohaza yuritishni qo‘llab-quvvatlaydi. Bu versiya alohida `budget_tokens` bilan qo‘lda boshqariladigan extended thinking’ni qo‘llab-quvvatlamaydi. `effort` darajalari — `low`, `medium`, `high`, `xhigh` va `max`; standart daraja — `high`. Anthropic ma’lumotiga ko‘ra, chuqurroq darajalar tahlil hajmi, vositalarga murojaatlar va ish vaqtini oshirishi mumkin, qisqa, chegaralangan vazifalarda esa pastroq puxtalik darajasi foydali.\n\nModel batafsillikni topshiriqni qanchalik murakkab deb bilishiga qarab tanlaydi: oddiy savol qisqa, ochiq tahlil esa keng javob olishi mumkin. Muayyan hajm yoki matn uslubi kerak bo‘lsa, ularni aniq, imkon qadar kerakli natija namunasi bilan belgilash lozim. Vositalar bilan davomli ishda bajarish jarayoni haqida oraliq xabarlar ko‘zda tutilgan; ularning mazmuni va chastotasini ham ko‘rsatma bilan yo‘naltirish mumkin.\n\n### Materiallar, vositalar va vizual bezatish\n\nKirishga **matn va tasvirlar** beriladi, natija **matn shaklida** hosil qilinadi. Kontekst oynasi — **1 mln token**, oddiy rejimda maksimal javob — **128 ming token**. Ichki bilimlarning ishonchli chegarasi — **2026-yil yanvar**.\n\nIshlab chiqaruvchida oddiy vizual kirish formatlari: JPEG/PNG/GIF/WebP, animatsiyaning birinchi kadri. Nativ tahlilda: uzun tomon — 2576 piksel, 4784 vizual token; katta tasvirlar tahlildan oldin kichraytiriladi. Fayllar va parametrlarning qo‘llab-quvvatlanishi ilova metodiga bog‘liq.\n\nVeb-qidiruv, buyruqlarni bajarish, fayl xotirasi, interfeysni boshqarish va mustaqil kichik vazifalar ilovada qo‘llab-quvvatlanishni talab qiladi. Model mavjud vositalardan foydalanishi mumkin, lekin sa’y-harakat darajasi va vositalar vazifasining aniq tavsifi amallarni tanlashga ta’sir qiladi. Bu, ayniqsa, axborot qidirish, tahlil va texnik bajarishni bog‘laydigan ishda foydali.\n\nInterfeyslar va slaydlar yaratishda Opus 4.8 bezatish bo‘yicha muayyan uslublarni muntazam afzal ko‘radi. Muayyan uslubdagi natija uchun aniq palitra, tipografika, kompozitsiya va harakat xususiyatini ko‘rsatish yoki avval bir nechta yo‘nalish so‘rash foydali. Tayyor fayl yoki ishlaydigan interfeys muhit vositalari orqali paydo bo‘ladi; modelning o‘z chiqishi matnli bo‘lib qoladi.", "editorialOverlay": { "copy_fields": { "practical_html": "Ingliz tilidagi texnik misol
Audit a project decision memo\n\nReview this memo against its evidence: 'All customers prefer weekly exports, so we will remove daily exports.' Evidence E1: 12 of 20 interviewed customers prefer weekly; E2: 4 prefer daily; E3: 4 gave no preference. Return the unsupported claims, a corrected recommendation and one question that would resolve a material uncertainty.A three-part evidence review with E1-E3 references and a narrower recommendation.The review must preserve the four daily-export preferences and avoid treating silence as agreement.claude-opus-4-8Claude Opus 4.8 davomli dasturlash va professional tahlil uchun mo‘ljallangan. Ko‘p bosqichli ish yo‘nalishini saqlaydi, noaniqlikni ko‘rsatadi va natijaga qo‘yilgan aniq talablarni hisobga oladi.
Bu tavsif model imkoniyatlarini yoritadi. Ailoot da tanlangan usul uchun mavjud kirish, chiqish va parametrlar quyida keltirilgan.
Claude Opus 4.8 davomli agent ishi, professional tahlil, vizual tushunish va ilova taqdim etadigan xotiradan foydalanishni birlashtiradi. Anthropic xulosalarni tekshirishda modelning xatti-harakatini alohida ta’kidlaydi: u dalillar yetishmasligini payqash, noaniqlik haqida xabar berish va yetarli asos bo‘lmasa, ish bajarilganini da’vo qilmaslikka o‘rgatilgan.
Muhandislik vazifasida model bir nechta bog‘liq komponentni o‘rganishi, rejani ko‘rib chiqishi, xatolarni aniqlashi va yangi ma’lumotlar olingandan keyin bajarishni davom ettirishi mumkin. Hujjat tahlilida uning zich mazmun bilan ishlash qobiliyati hamda kiruvchi materiallar va xulosalardagi muammolarga e’tibori muhim. Tavsiflangan professional yo‘nalishlar orasida tadqiqot, tahlil, hujjatlar bilan ishlash va taqdimot mazmunini tayyorlash bor.
Ko‘rsatmalar, ayniqsa, past sa’y-harakat darajalarida so‘zma-so‘z talqin qilinadi. Model bitta elementga tegishli shartni qolganlariga bildirmasdan tatbiq etishi shart emas: amal qilish doirasi, format va natija mezonlarini aniq belgilash yaxshiroq. Kodni tekshirishda bu belgilangan jiddiylik chegarasi topilgan muammolarning qaysi biri javobga kirishiga ta’sir qilishini anglatadi. Barcha topilmalar haqida xabar berish talabi ularni saralash talabidan alohida ifodalanishi kerak.
Opus 4.8 ilovada yoqish kerak bo‘lgan adaptiv mulohaza yuritishni qo‘llab-quvvatlaydi. Bu versiya alohida budget_tokens bilan qo‘lda boshqariladigan extended thinking’ni qo‘llab-quvvatlamaydi. effort darajalari — low, medium, high, xhigh va max; standart daraja — high. Anthropic ma’lumotiga ko‘ra, chuqurroq darajalar tahlil hajmi, vositalarga murojaatlar va ish vaqtini oshirishi mumkin, qisqa, chegaralangan vazifalarda esa pastroq puxtalik darajasi foydali.
Model batafsillikni topshiriqni qanchalik murakkab deb bilishiga qarab tanlaydi: oddiy savol qisqa, ochiq tahlil esa keng javob olishi mumkin. Muayyan hajm yoki matn uslubi kerak bo‘lsa, ularni aniq, imkon qadar kerakli natija namunasi bilan belgilash lozim. Vositalar bilan davomli ishda bajarish jarayoni haqida oraliq xabarlar ko‘zda tutilgan; ularning mazmuni va chastotasini ham ko‘rsatma bilan yo‘naltirish mumkin.
Kirishga matn va tasvirlar beriladi, natija matn shaklida hosil qilinadi. Kontekst oynasi — 1 mln token, oddiy rejimda maksimal javob — 128 ming token. Ichki bilimlarning ishonchli chegarasi — 2026-yil yanvar.
Ishlab chiqaruvchida oddiy vizual kirish formatlari: JPEG/PNG/GIF/WebP, animatsiyaning birinchi kadri. Nativ tahlilda: uzun tomon — 2576 piksel, 4784 vizual token; katta tasvirlar tahlildan oldin kichraytiriladi. Fayllar va parametrlarning qo‘llab-quvvatlanishi ilova metodiga bog‘liq.
Veb-qidiruv, buyruqlarni bajarish, fayl xotirasi, interfeysni boshqarish va mustaqil kichik vazifalar ilovada qo‘llab-quvvatlanishni talab qiladi. Model mavjud vositalardan foydalanishi mumkin, lekin sa’y-harakat darajasi va vositalar vazifasining aniq tavsifi amallarni tanlashga ta’sir qiladi. Bu, ayniqsa, axborot qidirish, tahlil va texnik bajarishni bog‘laydigan ishda foydali.
Interfeyslar va slaydlar yaratishda Opus 4.8 bezatish bo‘yicha muayyan uslublarni muntazam afzal ko‘radi. Muayyan uslubdagi natija uchun aniq palitra, tipografika, kompozitsiya va harakat xususiyatini ko‘rsatish yoki avval bir nechta yo‘nalish so‘rash foydali. Tayyor fayl yoki ishlaydigan interfeys muhit vositalari orqali paydo bo‘ladi; modelning o‘z chiqishi matnli bo‘lib qoladi.
POST /v1/messagestext, imagetext| Maydon | Tur | Talab | Qiymatlar va cheklovlar | Standart |
|---|---|---|---|---|
Maydonmodel | Turstring | TalabMajburiy | Qiymatlar va cheklovlar claude-opus-4-8 | Standart— |
Maydonmessages | Turmessage[] | TalabMajburiy | Qiymatlar va cheklovlar Elementlarning eng kam soni: 1 | Standart— |
Maydonmessages[].role | Turstring | TalabMajburiy | Qiymatlar va cheklovlar user, assistant | Standart— |
Maydonmessages[].content | Turstring|content_block[] | TalabMajburiy | Qiymatlar va cheklovlar | Standart— |
Maydonsystem | Turstring|content_block[] | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydonmax_tokens | Turinteger | TalabMajburiy | Qiymatlar va cheklovlar Eng kam: 1 Cheklov qo‘llanadigan obyekt: Tanlangan model yoki kartaning aniq yuqori chegarasi | Standart— |
Maydonstream | Turboolean | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standartfalse |
Maydontemperature | Turnumber | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontop_p | Turnumber | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonstop_sequences | Turstring[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontools | Turtool[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontools[].name | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Quyidagi holatda qo‘llanadi: Vosita taqdim etilgan | Standart— |
Maydontools[].description | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydontools[].input_schema | TurJSON_schema | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydontool_choice | Turobject | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonthinking.type | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonthinking.budget_tokens | Turinteger | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonoutput_config.effort | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonmetadata | Turobject | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydoncache_control | Turobject | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Matn · UTF8 matni
messages[].content yoki type=text bloki
Matn + rasm
{type:image,source:{type:base64,media_type,data}} yoki type=url turidagi manba
| Parametr | Parametr talablari |
|---|---|
Parametrmaterial_types | Parametr talablaritext, image |
Parametrrequired | Parametr talablarimodel, messages, max_tokens |
Parametroptional_controls | Parametr talablarinull |
Parametrcontent_type | Parametr talablariapplication/json |
Parametrtext_file_material | Parametr talablariHaqiqiy UTF-8 kontenti matn sifatida kiritiladi; fayl IDlari/URLs uchun alohida aniq qo‘llab-quvvatlash kerak |
Hayot sikli: exact retained published method
Metod tuzilishlari va IDlari aniq; yetkazib beruvchining imkoniyati so‘rovni qabul qilishga ruxsatdan alohida.
POST /v1/messagesAiloot 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 →
Modelning ma’lumot jadvallari usul tanlash, narx hisobi yoki to‘lovlarni o‘zgartirmaydi. Amaldagi to‘lovlar tanlangan Ailoot usuli tariflari bo‘yicha hisoblanadi.
Har bir to‘lovni ko‘rsatilgan shartlarga moslang. Har bir muqobil sozlamalar to‘plami uchun bitta tarifdan foydalaning.
| Sozlama | Tariflar jadvali | Kontekst tokenlari soni bundan ko‘p | Kontekst tokenlari soni bundan oshmaydi, chegara kiradi | To‘lov |
|---|---|---|---|---|
| SozlamaOpenAI Chat · Tokenlar uchun asosiy tariflar | Tariflar jadvaliAsosiy tarif | Kontekst tokenlari soni bundan ko‘p— | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
| SozlamaOpenAI Chat · So‘rov uzunligi bo‘yicha 1-daraja | Tariflar jadvali— | Kontekst tokenlari soni bundan ko‘p0 | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
| SozlamaAnthropic Messages · Tokenlar uchun asosiy tariflar | Tariflar jadvaliAsosiy tarif | Kontekst tokenlari soni bundan ko‘p— | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
| SozlamaAnthropic Messages · So‘rov uzunligi bo‘yicha 1-daraja | Tariflar jadvali— | Kontekst tokenlari soni bundan ko‘p0 | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "claude-opus-4-8", "max_tokens": 1024, "messages": []}')\nf=Path(os.environ["AILOOT_TEXT_FILE"])\nif not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")\ntext=f.read_text(encoding="utf-8")\nif not text.strip():raise SystemExit("Source material must contain text")\np["messages"]=[{"role":"user","content":"Summarize this material and quote supporting source passages.\\n\\n"+text}]\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/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nprint(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\nset -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_IMAGE_FILE:?Set your actual PNG JPEG or WebP image}"\nexport AILOOT_IMAGE_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "claude-opus-4-8", "max_tokens": 1024, "messages": [{"role": "user", "content": [{"type": "text", "text": "Read labels and numbers in this image; flag unreadable text."}]}]}')\nimport base64\nf=Path(os.environ["AILOOT_IMAGE_FILE"])\nif not f.is_file():raise SystemExit("Choose an actual PNG JPEG or WebP image")\nraw=f.read_bytes()\nmime="image/png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "image/jpeg" if raw.startswith(b"\\xff\\xd8") else "image/webp" if raw[:4]==b"RIFF" and raw[8:12]==b"WEBP" else None\nif not mime:raise SystemExit("Use PNG JPEG or WebP")\nb64=base64.b64encode(raw).decode()\np["messages"][0]["content"].append({"type":"image","source":{"type":"base64","media_type":mime,"data":b64}})\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/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\nprint(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\nimport anthropic\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient = anthropic.Anthropic(base_url="https://api.ailoot.ai", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)\nmsg = client.messages.create(\n model="claude-opus-4-8",\n max_tokens=1024,\n messages=[{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}],\n)\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(msg.to_dict(mode="json"))\nimport Anthropic from "@anthropic-ai/sdk";\n\nif (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");\nconst client = new Anthropic({ baseURL: "https://api.ailoot.ai", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });\nconst msg = await client.messages.create({\n model: "claude-opus-4-8",\n max_tokens: 1024,\n messages: [{ role: "user", content: "Explain the difference between an observation and a hypothesis in three sentences." }],\n});\nimport { writeFileSync } from "node:fs";\n\nfunction saveResult(data, stem = "result") {\n writeFileSync(stem + ".json", JSON.stringify(data, null, 2));\n console.log(JSON.stringify(data, null, 2));\n const saveBlob = (encoded, suffix, label) => {\n const raw = Buffer.from(encoded, "base64");\n const ext = raw.subarray(0,8).equals(Buffer.from([137,80,78,71,13,10,26,10])) ? "png" : raw[0]===255 && raw[1]===216 ? "jpg" : raw.subarray(0,4).toString()==="RIFF" && raw.subarray(8,12).toString()==="WEBP" ? "webp" : suffix;\n const file = `${stem}-${label}.${ext}`;\n writeFileSync(file, raw); console.log("Saved", file);\n };\n for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block));\n for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {}));\n for (const [i, item] of (data.output || []).entries()) {\n console.log("Output", i, JSON.stringify(item));\n if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`);\n }\n for (const [ci, candidate] of (data.candidates || []).entries()) {\n for (const [pi, part] of (candidate.content?.parts || []).entries()) {\n console.log("Candidate/part", ci, pi, JSON.stringify(part));\n const blob = part.inlineData || part.inline_data;\n if (blob?.data) {\n const mime = blob.mimeType || blob.mime_type;\n const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin";\n saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`);\n }\n }\n }\n}\n\nsaveResult(msg);\nimport anthropic,os\nfrom pathlib import Path\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=anthropic.Anthropic(base_url="https://api.ailoot.ai",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\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\nwith client.messages.stream(model='claude-opus-4-8',max_tokens=1024,messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_message()\n save_result(final.to_dict(mode="json"),"stream-result")\njson yoki SSE
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.
So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan.
| Maydon | Tur |
|---|---|
Maydonid | Turstring |
Maydonmodel | Turstring |
Maydoncontent[] | Turtext|thinking|tool_use|other content block |
Maydonstop_reason | Turstring|null |
Maydonusage | Turobject |
| Javob | Shart | Keyingi qadam |
|---|---|---|
Javob400 · invalid_request_error | ShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri | Keyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating. |
Ailoot claude-opus-4-8
Ailoot Ultra anthropic/claude-opus-4.8
Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.
claude-opus-4-8Claude Opus 4.8 davomli dasturlash va professional tahlil uchun mo‘ljallangan. Ko‘p bosqichli ish yo‘nalishini saqlaydi, noaniqlikni ko‘rsatadi va natijaga qo‘yilgan aniq talablarni hisobga oladi.
Bu tavsif model imkoniyatlarini yoritadi. Ailoot da tanlangan usul uchun mavjud kirish, chiqish va parametrlar quyida keltirilgan.
Claude Opus 4.8 davomli agent ishi, professional tahlil, vizual tushunish va ilova taqdim etadigan xotiradan foydalanishni birlashtiradi. Anthropic xulosalarni tekshirishda modelning xatti-harakatini alohida ta’kidlaydi: u dalillar yetishmasligini payqash, noaniqlik haqida xabar berish va yetarli asos bo‘lmasa, ish bajarilganini da’vo qilmaslikka o‘rgatilgan.
Muhandislik vazifasida model bir nechta bog‘liq komponentni o‘rganishi, rejani ko‘rib chiqishi, xatolarni aniqlashi va yangi ma’lumotlar olingandan keyin bajarishni davom ettirishi mumkin. Hujjat tahlilida uning zich mazmun bilan ishlash qobiliyati hamda kiruvchi materiallar va xulosalardagi muammolarga e’tibori muhim. Tavsiflangan professional yo‘nalishlar orasida tadqiqot, tahlil, hujjatlar bilan ishlash va taqdimot mazmunini tayyorlash bor.
Ko‘rsatmalar, ayniqsa, past sa’y-harakat darajalarida so‘zma-so‘z talqin qilinadi. Model bitta elementga tegishli shartni qolganlariga bildirmasdan tatbiq etishi shart emas: amal qilish doirasi, format va natija mezonlarini aniq belgilash yaxshiroq. Kodni tekshirishda bu belgilangan jiddiylik chegarasi topilgan muammolarning qaysi biri javobga kirishiga ta’sir qilishini anglatadi. Barcha topilmalar haqida xabar berish talabi ularni saralash talabidan alohida ifodalanishi kerak.
Opus 4.8 ilovada yoqish kerak bo‘lgan adaptiv mulohaza yuritishni qo‘llab-quvvatlaydi. Bu versiya alohida budget_tokens bilan qo‘lda boshqariladigan extended thinking’ni qo‘llab-quvvatlamaydi. effort darajalari — low, medium, high, xhigh va max; standart daraja — high. Anthropic ma’lumotiga ko‘ra, chuqurroq darajalar tahlil hajmi, vositalarga murojaatlar va ish vaqtini oshirishi mumkin, qisqa, chegaralangan vazifalarda esa pastroq puxtalik darajasi foydali.
Model batafsillikni topshiriqni qanchalik murakkab deb bilishiga qarab tanlaydi: oddiy savol qisqa, ochiq tahlil esa keng javob olishi mumkin. Muayyan hajm yoki matn uslubi kerak bo‘lsa, ularni aniq, imkon qadar kerakli natija namunasi bilan belgilash lozim. Vositalar bilan davomli ishda bajarish jarayoni haqida oraliq xabarlar ko‘zda tutilgan; ularning mazmuni va chastotasini ham ko‘rsatma bilan yo‘naltirish mumkin.
Kirishga matn va tasvirlar beriladi, natija matn shaklida hosil qilinadi. Kontekst oynasi — 1 mln token, oddiy rejimda maksimal javob — 128 ming token. Ichki bilimlarning ishonchli chegarasi — 2026-yil yanvar.
Ishlab chiqaruvchida oddiy vizual kirish formatlari: JPEG/PNG/GIF/WebP, animatsiyaning birinchi kadri. Nativ tahlilda: uzun tomon — 2576 piksel, 4784 vizual token; katta tasvirlar tahlildan oldin kichraytiriladi. Fayllar va parametrlarning qo‘llab-quvvatlanishi ilova metodiga bog‘liq.
Veb-qidiruv, buyruqlarni bajarish, fayl xotirasi, interfeysni boshqarish va mustaqil kichik vazifalar ilovada qo‘llab-quvvatlanishni talab qiladi. Model mavjud vositalardan foydalanishi mumkin, lekin sa’y-harakat darajasi va vositalar vazifasining aniq tavsifi amallarni tanlashga ta’sir qiladi. Bu, ayniqsa, axborot qidirish, tahlil va texnik bajarishni bog‘laydigan ishda foydali.
Interfeyslar va slaydlar yaratishda Opus 4.8 bezatish bo‘yicha muayyan uslublarni muntazam afzal ko‘radi. Muayyan uslubdagi natija uchun aniq palitra, tipografika, kompozitsiya va harakat xususiyatini ko‘rsatish yoki avval bir nechta yo‘nalish so‘rash foydali. Tayyor fayl yoki ishlaydigan interfeys muhit vositalari orqali paydo bo‘ladi; modelning o‘z chiqishi matnli bo‘lib qoladi.
POST /v1/responsestext, imagetext| Maydon | Tur | Talab | Qiymatlar va cheklovlar | Standart |
|---|---|---|---|---|
Maydonmodel | Turstring | TalabMajburiy | Qiymatlar va cheklovlar claude-opus-4-8 | Standart— |
Maydoninput | Turstring|input_item[] | TalabMajburiy | Qiymatlar va cheklovlar | Standart— |
Maydoninstructions | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydoninput[].type | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar message, reasoning, function_call, function_call_output | Standart— |
Maydoninput[].content | Turstring|content_part[] | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydoninput[].call_id | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Quyidagi holatda qo‘llanadi: function_call_output; haqiqatan qaytarilgan call_id qiymatini saqlang | Standart— |
Maydoninput[].output | Turstring|content_part[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Quyidagi holatda qo‘llanadi: function_call_output | Standart— |
Maydonmax_output_tokens | Turinteger | TalabIxtiyoriy | Qiymatlar va cheklovlar Eng kam: 1 Modelga bogʻliq | Standart— |
Maydonstream | Turboolean | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standartfalse |
Maydontemperature | Turnumber | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontop_p | Turnumber | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontools | Turtool[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontool_choice | Turstring|object | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonparallel_tool_calls | Turboolean | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonreasoning.effort | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonreasoning.summary | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontext.format | Turobject | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontext.verbosity | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydoninclude | Turstring[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonstore | Turboolean | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonprevious_response_id | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar ID talabi: Faqat qo‘llab-quvvatlanadigan davom ettirishdan olingan haqiqiy javob IDsi Modelga bogʻliq | Standart— |
Maydonservice_tier | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonprompt_cache_key | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonprompt_cache_options | Turobject | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Matn · UTF8 matni
Kirish matni yoki input_text qismi
Matn + rasm
{type:input_image,image_url}
| Parametr | Parametr talablari |
|---|---|
Parametrmaterial_types | Parametr talablaritext, image |
Parametrrequired | Parametr talablarimodel, input |
Parametroptional_controls | Parametr talablarinull |
Parametrcontent_type | Parametr talablariapplication/json |
Parametrtext_file_material | Parametr talablariHaqiqiy UTF-8 kontenti matn sifatida kiritiladi; fayl IDlari/URLs uchun alohida aniq qo‘llab-quvvatlash kerak |
Hayot sikli: exact retained published method
Metod tuzilishlari va IDlari aniq; yetkazib beruvchining imkoniyati so‘rovni qabul qilishga ruxsatdan alohida.
POST /v1/responsesAiloot 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 →
Modelning ma’lumot jadvallari usul tanlash, narx hisobi yoki to‘lovlarni o‘zgartirmaydi. Amaldagi to‘lovlar tanlangan Ailoot usuli tariflari bo‘yicha hisoblanadi.
Har bir to‘lovni ko‘rsatilgan shartlarga moslang. Har bir muqobil sozlamalar to‘plami uchun bitta tarifdan foydalaning.
| Sozlama | Tariflar jadvali | Kontekst tokenlari soni bundan ko‘p | Kontekst tokenlari soni bundan oshmaydi, chegara kiradi | To‘lov |
|---|---|---|---|---|
| SozlamaOpenAI Chat · Tokenlar uchun asosiy tariflar | Tariflar jadvaliAsosiy tarif | Kontekst tokenlari soni bundan ko‘p— | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
| SozlamaOpenAI Chat · So‘rov uzunligi bo‘yicha 1-daraja | Tariflar jadvali— | Kontekst tokenlari soni bundan ko‘p0 | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
| SozlamaAnthropic Messages · Tokenlar uchun asosiy tariflar | Tariflar jadvaliAsosiy tarif | Kontekst tokenlari soni bundan ko‘p— | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
| SozlamaAnthropic Messages · So‘rov uzunligi bo‘yicha 1-daraja | Tariflar jadvali— | Kontekst tokenlari soni bundan ko‘p0 | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "claude-opus-4-8", "input": ""}')\nf=Path(os.environ["AILOOT_TEXT_FILE"])\nif not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")\ntext=f.read_text(encoding="utf-8")\nif not text.strip():raise SystemExit("Source material must contain text")\np["input"]="Summarize this material and quote supporting source passages.\\n\\n"+text\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/responses \\\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 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(json.loads(Path("result.json").read_text(encoding="utf-8")))\nPY_RESULT\nset -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_IMAGE_FILE:?Set your actual PNG JPEG or WebP image}"\nexport AILOOT_IMAGE_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "claude-opus-4-8", "input": [{"role": "user", "content": [{"type": "input_text", "text": "Read labels and numbers in this image; flag unreadable text."}]}]}')\nimport base64\nf=Path(os.environ["AILOOT_IMAGE_FILE"])\nif not f.is_file():raise SystemExit("Choose an actual PNG JPEG or WebP image")\nraw=f.read_bytes()\nmime="image/png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "image/jpeg" if raw.startswith(b"\\xff\\xd8") else "image/webp" if raw[:4]==b"RIFF" and raw[8:12]==b"WEBP" else None\nif not mime:raise SystemExit("Use PNG JPEG or WebP")\nb64=base64.b64encode(raw).decode()\np["input"][0]["content"].append({"type":"input_image","image_url":"data:"+mime+";base64,"+b64})\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/responses \\\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 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(json.loads(Path("result.json").read_text(encoding="utf-8")))\nPY_RESULT\nfrom 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)\nresp = client.responses.create(model="claude-opus-4-8", input="Explain the difference between an observation and a hypothesis in three sentences.")\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(resp.to_dict(mode="json"))\nimport OpenAI from "openai";\n\nif (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");\nconst client = new OpenAI({ baseURL: "https://api.ailoot.ai/v1", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });\nconst resp = await client.responses.create({ model: "claude-opus-4-8", input: "Explain the difference between an observation and a hypothesis in three sentences." });\nimport { writeFileSync } from "node:fs";\n\nfunction saveResult(data, stem = "result") {\n writeFileSync(stem + ".json", JSON.stringify(data, null, 2));\n console.log(JSON.stringify(data, null, 2));\n const saveBlob = (encoded, suffix, label) => {\n const raw = Buffer.from(encoded, "base64");\n const ext = raw.subarray(0,8).equals(Buffer.from([137,80,78,71,13,10,26,10])) ? "png" : raw[0]===255 && raw[1]===216 ? "jpg" : raw.subarray(0,4).toString()==="RIFF" && raw.subarray(8,12).toString()==="WEBP" ? "webp" : suffix;\n const file = `${stem}-${label}.${ext}`;\n writeFileSync(file, raw); console.log("Saved", file);\n };\n for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block));\n for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {}));\n for (const [i, item] of (data.output || []).entries()) {\n console.log("Output", i, JSON.stringify(item));\n if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`);\n }\n for (const [ci, candidate] of (data.candidates || []).entries()) {\n for (const [pi, part] of (candidate.content?.parts || []).entries()) {\n console.log("Candidate/part", ci, pi, JSON.stringify(part));\n const blob = part.inlineData || part.inline_data;\n if (blob?.data) {\n const mime = blob.mimeType || blob.mime_type;\n const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin";\n saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`);\n }\n }\n }\n}\n\nsaveResult(resp);\nfrom openai import OpenAI\nimport os\nfrom pathlib import Path\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\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)\nwith client.responses.stream(model="claude-opus-4-8", input="Explain the difference between an observation and a hypothesis in three sentences.") as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_response()\n save_result(final.to_dict(mode="json"),"stream-result")\njson yoki SSE
Qaytarilgan har bir to‘liq javob, fayl va kontent qismini saqlang.
Har bir chiqish elementi va kontent qismini, funksiyaning haqiqiy call_id qiymatini, shifrlangan fikrlash ma’lumotlari va sarfni saqlang. Oqimli uzatishda hodisa va elementning to‘liq identifikatsiyasini saqlang.
So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan.
| Maydon | Tur |
|---|---|
Maydonid | Turstring |
Maydonstatus | Turstring |
Maydonoutput[] | Turmessage|reasoning|function_call|other item |
Maydonoutput[].content[] | Turcontent part |
Maydonusage | Turobject |
| Javob | Shart | Keyingi qadam |
|---|---|---|
Javob400 · invalid_request_error | ShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri | Keyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating. |
Ailoot claude-opus-4-8
Ailoot Ultra anthropic/claude-opus-4.8
Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.
claude-opus-4-8Claude Opus 4.8 davomli dasturlash va professional tahlil uchun mo‘ljallangan. Ko‘p bosqichli ish yo‘nalishini saqlaydi, noaniqlikni ko‘rsatadi va natijaga qo‘yilgan aniq talablarni hisobga oladi.
Bu tavsif model imkoniyatlarini yoritadi. Ailoot da tanlangan usul uchun mavjud kirish, chiqish va parametrlar quyida keltirilgan.
Claude Opus 4.8 davomli agent ishi, professional tahlil, vizual tushunish va ilova taqdim etadigan xotiradan foydalanishni birlashtiradi. Anthropic xulosalarni tekshirishda modelning xatti-harakatini alohida ta’kidlaydi: u dalillar yetishmasligini payqash, noaniqlik haqida xabar berish va yetarli asos bo‘lmasa, ish bajarilganini da’vo qilmaslikka o‘rgatilgan.
Muhandislik vazifasida model bir nechta bog‘liq komponentni o‘rganishi, rejani ko‘rib chiqishi, xatolarni aniqlashi va yangi ma’lumotlar olingandan keyin bajarishni davom ettirishi mumkin. Hujjat tahlilida uning zich mazmun bilan ishlash qobiliyati hamda kiruvchi materiallar va xulosalardagi muammolarga e’tibori muhim. Tavsiflangan professional yo‘nalishlar orasida tadqiqot, tahlil, hujjatlar bilan ishlash va taqdimot mazmunini tayyorlash bor.
Ko‘rsatmalar, ayniqsa, past sa’y-harakat darajalarida so‘zma-so‘z talqin qilinadi. Model bitta elementga tegishli shartni qolganlariga bildirmasdan tatbiq etishi shart emas: amal qilish doirasi, format va natija mezonlarini aniq belgilash yaxshiroq. Kodni tekshirishda bu belgilangan jiddiylik chegarasi topilgan muammolarning qaysi biri javobga kirishiga ta’sir qilishini anglatadi. Barcha topilmalar haqida xabar berish talabi ularni saralash talabidan alohida ifodalanishi kerak.
Opus 4.8 ilovada yoqish kerak bo‘lgan adaptiv mulohaza yuritishni qo‘llab-quvvatlaydi. Bu versiya alohida budget_tokens bilan qo‘lda boshqariladigan extended thinking’ni qo‘llab-quvvatlamaydi. effort darajalari — low, medium, high, xhigh va max; standart daraja — high. Anthropic ma’lumotiga ko‘ra, chuqurroq darajalar tahlil hajmi, vositalarga murojaatlar va ish vaqtini oshirishi mumkin, qisqa, chegaralangan vazifalarda esa pastroq puxtalik darajasi foydali.
Model batafsillikni topshiriqni qanchalik murakkab deb bilishiga qarab tanlaydi: oddiy savol qisqa, ochiq tahlil esa keng javob olishi mumkin. Muayyan hajm yoki matn uslubi kerak bo‘lsa, ularni aniq, imkon qadar kerakli natija namunasi bilan belgilash lozim. Vositalar bilan davomli ishda bajarish jarayoni haqida oraliq xabarlar ko‘zda tutilgan; ularning mazmuni va chastotasini ham ko‘rsatma bilan yo‘naltirish mumkin.
Kirishga matn va tasvirlar beriladi, natija matn shaklida hosil qilinadi. Kontekst oynasi — 1 mln token, oddiy rejimda maksimal javob — 128 ming token. Ichki bilimlarning ishonchli chegarasi — 2026-yil yanvar.
Ishlab chiqaruvchida oddiy vizual kirish formatlari: JPEG/PNG/GIF/WebP, animatsiyaning birinchi kadri. Nativ tahlilda: uzun tomon — 2576 piksel, 4784 vizual token; katta tasvirlar tahlildan oldin kichraytiriladi. Fayllar va parametrlarning qo‘llab-quvvatlanishi ilova metodiga bog‘liq.
Veb-qidiruv, buyruqlarni bajarish, fayl xotirasi, interfeysni boshqarish va mustaqil kichik vazifalar ilovada qo‘llab-quvvatlanishni talab qiladi. Model mavjud vositalardan foydalanishi mumkin, lekin sa’y-harakat darajasi va vositalar vazifasining aniq tavsifi amallarni tanlashga ta’sir qiladi. Bu, ayniqsa, axborot qidirish, tahlil va texnik bajarishni bog‘laydigan ishda foydali.
Interfeyslar va slaydlar yaratishda Opus 4.8 bezatish bo‘yicha muayyan uslublarni muntazam afzal ko‘radi. Muayyan uslubdagi natija uchun aniq palitra, tipografika, kompozitsiya va harakat xususiyatini ko‘rsatish yoki avval bir nechta yo‘nalish so‘rash foydali. Tayyor fayl yoki ishlaydigan interfeys muhit vositalari orqali paydo bo‘ladi; modelning o‘z chiqishi matnli bo‘lib qoladi.
POST /v1/chat/completionstext, imagetext| Maydon | Tur | Talab | Qiymatlar va cheklovlar | Standart |
|---|---|---|---|---|
Maydonmodel | Turstring | TalabMajburiy | Qiymatlar va cheklovlar claude-opus-4-8 | Standart— |
Maydonmessages | Turmessage[] | TalabMajburiy | Qiymatlar va cheklovlar Elementlarning eng kam soni: 1 | Standart— |
Maydonmessages[].role | Turstring | TalabMajburiy | Qiymatlar va cheklovlar Namunalar: system, user, assistant, tool, function Shartlar: Bu rollar manba kodining moslik turlarida ochiq ifodalangan; aniq rollar to‘plami har bir modelga xos bo‘lib qoladi. Modelga bogʻliq | Standart— |
Maydonmessages[].content | Turstring|content_part[]|null | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydonmessages[].tool_calls | Turtool_call[] | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydonmessages[].tool_call_id | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Quyidagi holatda qo‘llanadi: role=tool; haqiqatan qaytarilgan IDdan foydalaning | Standart— |
Maydonmessages[].name | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydonmessages[].reasoning_content | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Quyidagi holatda qo‘llanadi: Ushbu model foydalanganda, haqiqatan qaytarilgan fikrlash ma’lumotlarini keyingi so‘rovda qayta yuboring | Standart— |
Maydonmessages[].reasoning_details | Turobject|array | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonmax_tokens | Turinteger | TalabIxtiyoriy | Qiymatlar va cheklovlar Eng kam: 1 Modelga bogʻliq | Standart— |
Maydonmax_completion_tokens | Turinteger | TalabIxtiyoriy | Qiymatlar va cheklovlar Eng kam: 1 Modelga bogʻliq | Standart— |
Maydonstream | Turboolean | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standartfalse |
Maydonstream_options.include_usage | Turboolean | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontemperature | Turnumber | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontop_p | Turnumber | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonstop | Turstring|string[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontools | Turfunction_tool[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontools[].type | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontools[].function.name | Turstring | TalabMajburiy | Qiymatlar va cheklovlar Quyidagi holatda qo‘llanadi: Funksiya vositasi taqdim etilgan | Standart— |
Maydontools[].function.description | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydontools[].function.parameters | TurJSON_schema | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydontool_choice | Turstring|object | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonparallel_tool_calls | Turboolean | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonresponse_format | Turobject | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonreasoning_effort | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonreasoning | Turobject | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonservice_tier | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydoninstructions | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Moslik maydoni | Standart— |
Maydonfunctions | Turfunction[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonfunction_call | Turstring|object | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonprompt_cache_options | Turobject | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Matn · UTF8 matni
messages[].content
Matn + rasm
{type:image_url,image_url:{url,detail}}
| Parametr | Parametr talablari |
|---|---|
Parametrmaterial_types | Parametr talablaritext, image |
Parametrrequired | Parametr talablarimodel, messages |
Parametroptional_controls | Parametr talablarinull |
Parametrcontent_type | Parametr talablariapplication/json |
Parametrtext_file_material | Parametr talablariHaqiqiy UTF-8 kontenti matn sifatida kiritiladi; fayl IDlari/URLs uchun alohida aniq qo‘llab-quvvatlash kerak |
Hayot sikli: exact retained published method
Metod tuzilishlari va IDlari aniq; yetkazib beruvchining imkoniyati so‘rovni qabul qilishga ruxsatdan alohida.
POST /v1/chat/completionsAiloot 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 →
Modelning ma’lumot jadvallari usul tanlash, narx hisobi yoki to‘lovlarni o‘zgartirmaydi. Amaldagi to‘lovlar tanlangan Ailoot usuli tariflari bo‘yicha hisoblanadi.
Har bir to‘lovni ko‘rsatilgan shartlarga moslang. Har bir muqobil sozlamalar to‘plami uchun bitta tarifdan foydalaning.
| Sozlama | Tariflar jadvali | Kontekst tokenlari soni bundan ko‘p | Kontekst tokenlari soni bundan oshmaydi, chegara kiradi | To‘lov |
|---|---|---|---|---|
| SozlamaOpenAI Chat · Tokenlar uchun asosiy tariflar | Tariflar jadvaliAsosiy tarif | Kontekst tokenlari soni bundan ko‘p— | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
| SozlamaOpenAI Chat · So‘rov uzunligi bo‘yicha 1-daraja | Tariflar jadvali— | Kontekst tokenlari soni bundan ko‘p0 | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
| SozlamaAnthropic Messages · Tokenlar uchun asosiy tariflar | Tariflar jadvaliAsosiy tarif | Kontekst tokenlari soni bundan ko‘p— | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
| SozlamaAnthropic Messages · So‘rov uzunligi bo‘yicha 1-daraja | Tariflar jadvali— | Kontekst tokenlari soni bundan ko‘p0 | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "claude-opus-4-8", "messages": []}')\nf=Path(os.environ["AILOOT_TEXT_FILE"])\nif not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")\ntext=f.read_text(encoding="utf-8")\nif not text.strip():raise SystemExit("Source material must contain text")\np["messages"]=[{"role":"user","content":"Summarize this material and quote supporting source passages.\\n\\n"+text}]\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 $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nprint(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\nset -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_IMAGE_FILE:?Set your actual PNG JPEG or WebP image}"\nexport AILOOT_IMAGE_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "claude-opus-4-8", "messages": [{"role": "user", "content": [{"type": "text", "text": "Read labels and numbers in this image; flag unreadable text."}]}]}')\nimport base64\nf=Path(os.environ["AILOOT_IMAGE_FILE"])\nif not f.is_file():raise SystemExit("Choose an actual PNG JPEG or WebP image")\nraw=f.read_bytes()\nmime="image/png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "image/jpeg" if raw.startswith(b"\\xff\\xd8") else "image/webp" if raw[:4]==b"RIFF" and raw[8:12]==b"WEBP" else None\nif not mime:raise SystemExit("Use PNG JPEG or WebP")\nb64=base64.b64encode(raw).decode()\np["messages"][0]["content"].append({"type":"image_url","image_url":{"url":"data:"+mime+";base64,"+b64}})\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\nprint(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\nfrom 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)\nresp = client.chat.completions.create(\n model="claude-opus-4-8",\n messages=[{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}],\n)\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(resp.to_dict(mode="json"))\nimport OpenAI from "openai";\n\nif (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");\nconst client = new OpenAI({ baseURL: "https://api.ailoot.ai/v1", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });\nconst resp = await client.chat.completions.create({\n model: "claude-opus-4-8",\n messages: [{ role: "user", content: "Explain the difference between an observation and a hypothesis in three sentences." }],\n});\nimport { writeFileSync } from "node:fs";\n\nfunction saveResult(data, stem = "result") {\n writeFileSync(stem + ".json", JSON.stringify(data, null, 2));\n console.log(JSON.stringify(data, null, 2));\n const saveBlob = (encoded, suffix, label) => {\n const raw = Buffer.from(encoded, "base64");\n const ext = raw.subarray(0,8).equals(Buffer.from([137,80,78,71,13,10,26,10])) ? "png" : raw[0]===255 && raw[1]===216 ? "jpg" : raw.subarray(0,4).toString()==="RIFF" && raw.subarray(8,12).toString()==="WEBP" ? "webp" : suffix;\n const file = `${stem}-${label}.${ext}`;\n writeFileSync(file, raw); console.log("Saved", file);\n };\n for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block));\n for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {}));\n for (const [i, item] of (data.output || []).entries()) {\n console.log("Output", i, JSON.stringify(item));\n if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`);\n }\n for (const [ci, candidate] of (data.candidates || []).entries()) {\n for (const [pi, part] of (candidate.content?.parts || []).entries()) {\n console.log("Candidate/part", ci, pi, JSON.stringify(part));\n const blob = part.inlineData || part.inline_data;\n if (blob?.data) {\n const mime = blob.mimeType || blob.mime_type;\n const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin";\n saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`);\n }\n }\n }\n}\n\nsaveResult(resp);\nfrom openai import OpenAI\nimport os\nfrom pathlib import Path\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)\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\nwith client.chat.completions.stream(model='claude-opus-4-8',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson yoki SSE
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.
So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan.
| Maydon | Tur |
|---|---|
Maydonchoices[].index | Turinteger |
Maydonchoices[].message.content | Turstring|null |
Maydonchoices[].message.tool_calls | Turtool_call[] |
Maydonchoices[].message.reasoning_content | Turstring |
Maydonchoices[].message.reasoning_details | Turobject|array |
Maydonchoices[].finish_reason | Turstring|null |
Maydonusage | Turobject |
| Javob | Shart | Keyingi qadam |
|---|---|---|
Javob400 · invalid_request_error | ShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri | Keyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating. |
Ailoot claude-opus-4-8
Ailoot Ultra anthropic/claude-opus-4.8
Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.
anthropic/claude-opus-4.8Claude Opus 4.8 davomli dasturlash va professional tahlil uchun mo‘ljallangan. Ko‘p bosqichli ish yo‘nalishini saqlaydi, noaniqlikni ko‘rsatadi va natijaga qo‘yilgan aniq talablarni hisobga oladi.
Bu tavsif model imkoniyatlarini yoritadi. Ailoot da tanlangan usul uchun mavjud kirish, chiqish va parametrlar quyida keltirilgan.
Claude Opus 4.8 davomli agent ishi, professional tahlil, vizual tushunish va ilova taqdim etadigan xotiradan foydalanishni birlashtiradi. Anthropic xulosalarni tekshirishda modelning xatti-harakatini alohida ta’kidlaydi: u dalillar yetishmasligini payqash, noaniqlik haqida xabar berish va yetarli asos bo‘lmasa, ish bajarilganini da’vo qilmaslikka o‘rgatilgan.
Muhandislik vazifasida model bir nechta bog‘liq komponentni o‘rganishi, rejani ko‘rib chiqishi, xatolarni aniqlashi va yangi ma’lumotlar olingandan keyin bajarishni davom ettirishi mumkin. Hujjat tahlilida uning zich mazmun bilan ishlash qobiliyati hamda kiruvchi materiallar va xulosalardagi muammolarga e’tibori muhim. Tavsiflangan professional yo‘nalishlar orasida tadqiqot, tahlil, hujjatlar bilan ishlash va taqdimot mazmunini tayyorlash bor.
Ko‘rsatmalar, ayniqsa, past sa’y-harakat darajalarida so‘zma-so‘z talqin qilinadi. Model bitta elementga tegishli shartni qolganlariga bildirmasdan tatbiq etishi shart emas: amal qilish doirasi, format va natija mezonlarini aniq belgilash yaxshiroq. Kodni tekshirishda bu belgilangan jiddiylik chegarasi topilgan muammolarning qaysi biri javobga kirishiga ta’sir qilishini anglatadi. Barcha topilmalar haqida xabar berish talabi ularni saralash talabidan alohida ifodalanishi kerak.
Opus 4.8 ilovada yoqish kerak bo‘lgan adaptiv mulohaza yuritishni qo‘llab-quvvatlaydi. Bu versiya alohida budget_tokens bilan qo‘lda boshqariladigan extended thinking’ni qo‘llab-quvvatlamaydi. effort darajalari — low, medium, high, xhigh va max; standart daraja — high. Anthropic ma’lumotiga ko‘ra, chuqurroq darajalar tahlil hajmi, vositalarga murojaatlar va ish vaqtini oshirishi mumkin, qisqa, chegaralangan vazifalarda esa pastroq puxtalik darajasi foydali.
Model batafsillikni topshiriqni qanchalik murakkab deb bilishiga qarab tanlaydi: oddiy savol qisqa, ochiq tahlil esa keng javob olishi mumkin. Muayyan hajm yoki matn uslubi kerak bo‘lsa, ularni aniq, imkon qadar kerakli natija namunasi bilan belgilash lozim. Vositalar bilan davomli ishda bajarish jarayoni haqida oraliq xabarlar ko‘zda tutilgan; ularning mazmuni va chastotasini ham ko‘rsatma bilan yo‘naltirish mumkin.
Kirishga matn va tasvirlar beriladi, natija matn shaklida hosil qilinadi. Kontekst oynasi — 1 mln token, oddiy rejimda maksimal javob — 128 ming token. Ichki bilimlarning ishonchli chegarasi — 2026-yil yanvar.
Ishlab chiqaruvchida oddiy vizual kirish formatlari: JPEG/PNG/GIF/WebP, animatsiyaning birinchi kadri. Nativ tahlilda: uzun tomon — 2576 piksel, 4784 vizual token; katta tasvirlar tahlildan oldin kichraytiriladi. Fayllar va parametrlarning qo‘llab-quvvatlanishi ilova metodiga bog‘liq.
Veb-qidiruv, buyruqlarni bajarish, fayl xotirasi, interfeysni boshqarish va mustaqil kichik vazifalar ilovada qo‘llab-quvvatlanishni talab qiladi. Model mavjud vositalardan foydalanishi mumkin, lekin sa’y-harakat darajasi va vositalar vazifasining aniq tavsifi amallarni tanlashga ta’sir qiladi. Bu, ayniqsa, axborot qidirish, tahlil va texnik bajarishni bog‘laydigan ishda foydali.
Interfeyslar va slaydlar yaratishda Opus 4.8 bezatish bo‘yicha muayyan uslublarni muntazam afzal ko‘radi. Muayyan uslubdagi natija uchun aniq palitra, tipografika, kompozitsiya va harakat xususiyatini ko‘rsatish yoki avval bir nechta yo‘nalish so‘rash foydali. Tayyor fayl yoki ishlaydigan interfeys muhit vositalari orqali paydo bo‘ladi; modelning o‘z chiqishi matnli bo‘lib qoladi.
POST /v1/chat/completionstexttext| Maydon | Tur | Talab | Qiymatlar va cheklovlar | Standart |
|---|---|---|---|---|
Maydonmodel | Turstring | TalabMajburiy | Qiymatlar va cheklovlar anthropic/claude-opus-4.8 | Standart— |
Maydonmessages | Turmessage[] | TalabMajburiy | Qiymatlar va cheklovlar Elementlarning eng kam soni: 1 | Standart— |
Maydonmessages[].role | Turstring | TalabMajburiy | Qiymatlar va cheklovlar Namunalar: system, user, assistant, tool, function Shartlar: Bu rollar manba kodining moslik turlarida ochiq ifodalangan; aniq rollar to‘plami har bir modelga xos bo‘lib qoladi. Modelga bogʻliq | Standart— |
Maydonmessages[].content | Turstring|content_part[]|null | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydonmessages[].tool_calls | Turtool_call[] | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydonmessages[].tool_call_id | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Quyidagi holatda qo‘llanadi: role=tool; haqiqatan qaytarilgan IDdan foydalaning | Standart— |
Maydonmessages[].name | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydonmessages[].reasoning_content | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Quyidagi holatda qo‘llanadi: Ushbu model foydalanganda, haqiqatan qaytarilgan fikrlash ma’lumotlarini keyingi so‘rovda qayta yuboring | Standart— |
Maydonmessages[].reasoning_details | Turobject|array | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonmax_tokens | Turinteger | TalabIxtiyoriy | Qiymatlar va cheklovlar Eng kam: 1 Modelga bogʻliq | Standart— |
Maydonmax_completion_tokens | Turinteger | TalabIxtiyoriy | Qiymatlar va cheklovlar Eng kam: 1 Modelga bogʻliq | Standart— |
Maydonstream | Turboolean | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standartfalse |
Maydonstream_options.include_usage | Turboolean | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontemperature | Turnumber | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontop_p | Turnumber | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonstop | Turstring|string[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontools | Turfunction_tool[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontools[].type | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar function Modelga bogʻliq | Standart— |
Maydontools[].function.name | Turstring | TalabMajburiy | Qiymatlar va cheklovlar Quyidagi holatda qo‘llanadi: Funksiya vositasi taqdim etilgan | Standart— |
Maydontools[].function.description | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydontools[].function.parameters | TurJSON_schema | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydontool_choice | Turstring|object | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonparallel_tool_calls | Turboolean | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonresponse_format | Turobject | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonreasoning_effort | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonreasoning | Turobject | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonservice_tier | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydoninstructions | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Moslik maydoni | Standart— |
Maydonfunctions | Turfunction[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonfunction_call | Turstring|object | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonprompt_cache_options | Turobject | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonmodalities | Turstring[] | TalabIxtiyoriy | Qiymatlar va cheklovlar text | Standart— |
Maydonprovider | Turobject | TalabIxtiyoriy | Qiymatlar va cheklovlar Shartlar: Ushbu Ultra matn marshrutida provider.options rad etiladi | Standart— |
Maydoncache_control.ttl | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Shartlar: Haqiqiy prompt-kesh joylarida 1h rad etiladi | Standart— |
Matn · UTF8 matni
messages[].content
| Parametr | Parametr talablari |
|---|---|
Parametrcontent_type | Parametr talablariapplication/json |
Parametrrequired | Parametr talablarimodel, messages |
Parametrmaterial_types | Parametr talablaritext |
Parametrmessages | Parametr talablarirole/content tuzilishidagi xabarlar massivi |
Parametrfile_material | Parametr talablariHaqiqiy UTF-8 hujjati aynan matn sifatida kiritiladi; fayl IDsi faraz qilinmaydi |
Parametroptional_controls | Parametr talablaristream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametrcontrol_scope | Parametr talablariModelga xos; foydalanuvchi funksiya vositalari hostingdagi vositalardan farq qiladi |
Parametroutput_modalities | Parametr talablaritext |
Parametrhosted_operations | Parametr talablarifalse |
Parametrmultimodal_input_binding | Parametr talablarinull |
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.
Tarif ma’lumotlari so‘rov imkoniyatlarini qo‘shmaydi. Qo‘llab-quvvatlanadigan parametrlar tanlangan usulda ko‘rsatilgan.
Har bir to‘lovni ko‘rsatilgan shartlarga moslang. Har bir muqobil sozlamalar to‘plami uchun bitta tarifdan foydalaning.
| Sozlama | Tariflar jadvali | Kontekst tokenlari soni bundan ko‘p | Kontekst tokenlari soni bundan oshmaydi, chegara kiradi | To‘lov |
|---|---|---|---|---|
| SozlamaTokenlar uchun bazaviy tariflar | Tariflar jadvaliAsosiy tarif | Kontekst tokenlari soni bundan ko‘p— | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
| SozlamaSo‘rov uzunligi bo‘yicha 1-daraja | Tariflar jadvali— | Kontekst tokenlari soni bundan ko‘p0 | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. Summani band qilish va to‘lovni hisoblash tartibi →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "anthropic/claude-opus-4.8", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')\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\nfrom 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='anthropic/claude-opus-4.8',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])\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"))\nfrom openai import OpenAI\nimport os\nfrom pathlib import Path\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)\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\nwith client.chat.completions.stream(model='anthropic/claude-opus-4.8',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson yoki SSE
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.
So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan.
| Maydon | Tur |
|---|---|
Maydonchoices[].index | Turinteger |
Maydonchoices[].message.content | Turstring|null |
Maydonchoices[].message.tool_calls | Turtool_call[] |
Maydonchoices[].message.reasoning_content | Turstring |
Maydonchoices[].message.reasoning_details | Turobject|array |
Maydonchoices[].finish_reason | Turstring|null |
Maydonusage | Turobject |
| Javob | Shart | Keyingi qadam |
|---|---|---|
Javob400 · invalid_request_error | ShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri | Keyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating. |
Ailoot claude-opus-4-8
Ailoot Ultra anthropic/claude-opus-4.8
Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.
anthropic/claude-opus-4.8Claude Opus 4.8 davomli dasturlash va professional tahlil uchun mo‘ljallangan. Ko‘p bosqichli ish yo‘nalishini saqlaydi, noaniqlikni ko‘rsatadi va natijaga qo‘yilgan aniq talablarni hisobga oladi.
Bu tavsif model imkoniyatlarini yoritadi. Ailoot da tanlangan usul uchun mavjud kirish, chiqish va parametrlar quyida keltirilgan.
Claude Opus 4.8 davomli agent ishi, professional tahlil, vizual tushunish va ilova taqdim etadigan xotiradan foydalanishni birlashtiradi. Anthropic xulosalarni tekshirishda modelning xatti-harakatini alohida ta’kidlaydi: u dalillar yetishmasligini payqash, noaniqlik haqida xabar berish va yetarli asos bo‘lmasa, ish bajarilganini da’vo qilmaslikka o‘rgatilgan.
Muhandislik vazifasida model bir nechta bog‘liq komponentni o‘rganishi, rejani ko‘rib chiqishi, xatolarni aniqlashi va yangi ma’lumotlar olingandan keyin bajarishni davom ettirishi mumkin. Hujjat tahlilida uning zich mazmun bilan ishlash qobiliyati hamda kiruvchi materiallar va xulosalardagi muammolarga e’tibori muhim. Tavsiflangan professional yo‘nalishlar orasida tadqiqot, tahlil, hujjatlar bilan ishlash va taqdimot mazmunini tayyorlash bor.
Ko‘rsatmalar, ayniqsa, past sa’y-harakat darajalarida so‘zma-so‘z talqin qilinadi. Model bitta elementga tegishli shartni qolganlariga bildirmasdan tatbiq etishi shart emas: amal qilish doirasi, format va natija mezonlarini aniq belgilash yaxshiroq. Kodni tekshirishda bu belgilangan jiddiylik chegarasi topilgan muammolarning qaysi biri javobga kirishiga ta’sir qilishini anglatadi. Barcha topilmalar haqida xabar berish talabi ularni saralash talabidan alohida ifodalanishi kerak.
Opus 4.8 ilovada yoqish kerak bo‘lgan adaptiv mulohaza yuritishni qo‘llab-quvvatlaydi. Bu versiya alohida budget_tokens bilan qo‘lda boshqariladigan extended thinking’ni qo‘llab-quvvatlamaydi. effort darajalari — low, medium, high, xhigh va max; standart daraja — high. Anthropic ma’lumotiga ko‘ra, chuqurroq darajalar tahlil hajmi, vositalarga murojaatlar va ish vaqtini oshirishi mumkin, qisqa, chegaralangan vazifalarda esa pastroq puxtalik darajasi foydali.
Model batafsillikni topshiriqni qanchalik murakkab deb bilishiga qarab tanlaydi: oddiy savol qisqa, ochiq tahlil esa keng javob olishi mumkin. Muayyan hajm yoki matn uslubi kerak bo‘lsa, ularni aniq, imkon qadar kerakli natija namunasi bilan belgilash lozim. Vositalar bilan davomli ishda bajarish jarayoni haqida oraliq xabarlar ko‘zda tutilgan; ularning mazmuni va chastotasini ham ko‘rsatma bilan yo‘naltirish mumkin.
Kirishga matn va tasvirlar beriladi, natija matn shaklida hosil qilinadi. Kontekst oynasi — 1 mln token, oddiy rejimda maksimal javob — 128 ming token. Ichki bilimlarning ishonchli chegarasi — 2026-yil yanvar.
Ishlab chiqaruvchida oddiy vizual kirish formatlari: JPEG/PNG/GIF/WebP, animatsiyaning birinchi kadri. Nativ tahlilda: uzun tomon — 2576 piksel, 4784 vizual token; katta tasvirlar tahlildan oldin kichraytiriladi. Fayllar va parametrlarning qo‘llab-quvvatlanishi ilova metodiga bog‘liq.
Veb-qidiruv, buyruqlarni bajarish, fayl xotirasi, interfeysni boshqarish va mustaqil kichik vazifalar ilovada qo‘llab-quvvatlanishni talab qiladi. Model mavjud vositalardan foydalanishi mumkin, lekin sa’y-harakat darajasi va vositalar vazifasining aniq tavsifi amallarni tanlashga ta’sir qiladi. Bu, ayniqsa, axborot qidirish, tahlil va texnik bajarishni bog‘laydigan ishda foydali.
Interfeyslar va slaydlar yaratishda Opus 4.8 bezatish bo‘yicha muayyan uslublarni muntazam afzal ko‘radi. Muayyan uslubdagi natija uchun aniq palitra, tipografika, kompozitsiya va harakat xususiyatini ko‘rsatish yoki avval bir nechta yo‘nalish so‘rash foydali. Tayyor fayl yoki ishlaydigan interfeys muhit vositalari orqali paydo bo‘ladi; modelning o‘z chiqishi matnli bo‘lib qoladi.
POST /v1/messagestextjson, sse| Maydon | Tur | Talab | Qiymatlar va cheklovlar | Standart |
|---|---|---|---|---|
Maydonmodel | Turstring | TalabMajburiy | Qiymatlar va cheklovlar anthropic/claude-opus-4.8 | Standart— |
Maydonmessages | Turmessage[] | TalabMajburiy | Qiymatlar va cheklovlar | Standart— |
Maydonmessages[].role | Turstring | TalabMajburiy | Qiymatlar va cheklovlar user, assistant | Standart— |
Maydonmessages[].content | Turstring | TalabMajburiy | Qiymatlar va cheklovlar | Standart— |
Maydonmax_tokens | Turinteger | TalabMajburiy | Qiymatlar va cheklovlar | Standart— |
Maydonstream | Turboolean | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standartfalse |
Matn · UTF-8 matni
messages[].content satri
| Parametr | Parametr talablari |
|---|---|
Parametrcontent_type | Parametr talablariapplication/json |
Parametrrequired | Parametr talablarimodel, messages, max_tokens |
Parametroptional_controls | Parametr talablaristream |
Parametrmaterial_types | Parametr talablaritext |
Parametrtext_file_material | Parametr talablariHaqiqiy UTF-8 hujjatini o‘qing va uning matnini ko‘rsatilgan kirish yoki xabar kontenti satriga joylashtiring. |
Hayot sikli: synchronous_or_streamed
Tarif ma’lumotlari so‘rov imkoniyatlarini qo‘shmaydi. Qo‘llab-quvvatlanadigan parametrlar tanlangan usulda ko‘rsatilgan.
Har bir to‘lovni ko‘rsatilgan shartlarga moslang. Har bir muqobil sozlamalar to‘plami uchun bitta tarifdan foydalaning.
| Sozlama | Tariflar jadvali | Kontekst tokenlari soni bundan ko‘p | Kontekst tokenlari soni bundan oshmaydi, chegara kiradi | To‘lov |
|---|---|---|---|---|
| SozlamaTokenlar uchun bazaviy tariflar | Tariflar jadvaliAsosiy tarif | Kontekst tokenlari soni bundan ko‘p— | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
| SozlamaSo‘rov uzunligi bo‘yicha 1-daraja | Tariflar jadvali— | Kontekst tokenlari soni bundan ko‘p0 | Kontekst tokenlari soni bundan oshmaydi, chegara kiradiYuqori chegara yo‘q | To‘lov Kirish · Kirish tokenlari $0.01 / 1K token Chiqish · Chiqish tokenlari $0.05 / 1K token Kirish · Keshdan o‘qish $0.001 / 1K token Kirish · Keshga yozish $0.0125 / 1K token Kirish · Keshga yozish, 1 soat $0.02 / 1K token |
Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. Summani band qilish va to‘lovni hisoblash tartibi →
{\n "model": "anthropic/claude-opus-4.8",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'anthropic/claude-opus-4.8', '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\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': 'anthropic/claude-opus-4.8', '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))\nJSON yoki SSE
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.
So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan.
| Maydon | Tur |
|---|---|
Maydonid | Turstring |
Maydonmodel | Turstring |
Maydoncontent[] | Turtext|thinking|tool_use|other content block |
Maydonstop_reason | Turstring|null |
Maydonusage | Turobject |
| Javob | Shart | Keyingi qadam |
|---|---|---|
Javob400 · invalid_request_error | ShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri | Keyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating. |
Ailoot claude-opus-4-8
Ailoot Ultra anthropic/claude-opus-4.8
Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.
Ingliz tilidagi texnik misol
Audit a project decision memo\n\nReview this memo against its evidence: 'All customers prefer weekly exports, so we will remove daily exports.' Evidence E1: 12 of 20 interviewed customers prefer weekly; E2: 4 prefer daily; E3: 4 gave no preference. Return the unsupported claims, a corrected recommendation and one question that would resolve a material uncertainty.A three-part evidence review with E1-E3 references and a narrower recommendation.The review must preserve the four daily-export preferences and avoid treating silence as agreement.