modellar

MiniMax: MiniMax M2

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MiniMax: MiniMax M2

MiniMax M2 — MiniMax’ning jami 230B parametrdan 10B tasi faol bo‘lgan, boshidan oxirigacha dasturlash va agent ish jarayonlari uchun mo‘ljallangan matn modeli. Uning e’lon qilingan profilida mulohaza, funksiya vositalari, tuzilmali chiqishlar va katalog bo‘yicha 204,800 tokenli kontekst mavjud.

Model haqida

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

MiniMax M2 — MiniMax’ning boshidan oxirigacha dasturlash va agent ish jarayonlari uchun til modeli. Unda jami 230 milliard parametr bor, ulardan o‘n milliardi faollashadi. Matnli kirish matnli chiqish hosil qiladi. E’lon qilingan model metama’lumotlarida mulohaza yuritish, funksiya vositalari va tuzilmali chiqishlar ko‘rsatilgan; katalogdagi kontekst oynasi 204,800 token.

Natijasi aniq dasturlash vazifasi

Tegishli amalga oshirish kodini, kuzatilgan xatti-harakatni va o‘zgarishdan keyin kerak bo‘ladigan natijani bering. Tashxisni belgilaydigan bo‘lsa, ishlamayotgan kirish va haqiqiy xato chiqishini saqlang. Yangi imkoniyat bo‘yicha topshiriqda ta’sir qilinadigan interfeyslar hamda o‘zgarish kiritilayotganda to‘g‘ri qolishi kerak bo‘lgan xatti-harakat belgilanishi mumkin.

Aniq materialni so‘rang: muayyan patch, manbaga asoslangan tashxis yoki ochiq savolni hal qiladigan keyingi tekshiruv. Taklif qilingan ishni ilovaning mo‘ljallangan qismida tekshirish mumkin bo‘lishi uchun fayllarning joylashuvi va tegishli interfeys shartlarini natija bilan birga saqlang.

Mulohaza va vosita kuzatuvlari

Modeldan foydalanadigan ilovada mavjud amallarni belgilang. Tanlangan interfeys funksiya vositalarini taqdim etsa, model vosita chaqiruvini taklif qilishi va qaytarilgan kuzatuvdan keyingi qadam uchun dalil sifatida foydalanishi mumkin. Taklif qilingan amalni bajarilgan ish deb hisoblash o‘rniga haqiqiy buyruq, ma’lumot olish yoki fayl natijalarini saqlang.

Tuzilmali natija uchun maydonlar, turlar va yetishmayotgan ma’lumotni qanday ifodalashni belgilang. Qaytgan shaklni ham, mazmuniy qiymatlarni ham tekshiring. Rejalashtirish yozuvi yoki ajratib olingan fakt unga ma’no beradigan manba dalillari bilan bog‘langan holda qolishi kerak.

Shakllangan holatdan davom ettirish

Interfeysning haqiqiy kontekst va chiqish limitiga qarab rejalashtiring va javobga joy qoldiring. Uzunroq vazifada qabul qilingan qarorlar, joriy kuzatuvlar va qolgan savollarni ko‘rinadigan holda saqlang. Foydali M2 materiali natijani u ko‘rib chiqadigan xatti-harakat hamda dasturlash yoki ilovaning keyingi bosqichidan oldin kerak bo‘ladigan tekshiruv bilan birlashtiradi.

Amaliy vazifa

Amaliy vazifa

Ingliz tilidagi texnik misol

Preserve omitted fields in an update

A profile update handler uses name = body.get('name') and email = body.get('email'), then writes both fields. A request containing only name erases the stored email. Propose a minimal Python update-building function that changes only supplied fields and preserves explicit null values. Include tests distinguishing an omitted email from an email set to null.

Kutiladigan natija

A focused partial-update function and two tests showing omission and explicit-null behavior.

Natijani tekshirish

The tests demonstrate that absent and present-with-null fields produce different update records.
Ailoot · Anthropic Messages POST /v1/messages MiniMax-M2

MiniMax-M2

MiniMax /MiniMax-M2
Model ID
  • MiniMax-M2
  • minimax/minimax-m2

Boshlash

MiniMax M2 — MiniMax’ning jami 230B parametrdan 10B tasi faol bo‘lgan, boshidan oxirigacha dasturlash va agent ish jarayonlari uchun mo‘ljallangan matn modeli. Uning e’lon qilingan profilida mulohaza, funksiya vositalari, tuzilmali chiqishlar va katalog bo‘yicha 204,800 tokenli kontekst mavjud.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
Matn

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot · Anthropic Messages POST /v1/messages
Kirish
text
Chiqish
text
Kontekst
204 800 tokens
Eng katta chiqish
204 800 tokens
Chiqish formati
json yoki SSE
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
MiniMax-M2
Standart—
MaydonmessagesTurmessage[]TalabMajburiyQiymatlar va cheklovlar
Elementlarning eng kam soni: 1
Standart—
Maydonmessages[].roleTurstringTalabMajburiyQiymatlar va cheklovlar
user, assistant
Standart—
Maydonmessages[].contentTurstring|content_block[]TalabMajburiyQiymatlar va cheklovlarStandart—
MaydonsystemTurstring|content_block[]TalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydonmax_tokensTurintegerTalabMajburiyQiymatlar va cheklovlar
Eng kam: 1
Cheklov qo‘llanadigan obyekt: Tanlangan model yoki kartaning aniq yuqori chegarasi
Standart—
MaydonstreamTurbooleanTalabIxtiyoriyQiymatlar va cheklovlarStandartfalse
MaydontemperatureTurnumberTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontop_pTurnumberTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonstop_sequencesTurstring[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydontoolsTurtool[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontools[].nameTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: Vosita taqdim etilgan
Standart—
Maydontools[].descriptionTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydontools[].input_schemaTurJSON_schemaTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydontool_choiceTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonthinking.typeTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonthinking.budget_tokensTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonoutput_config.effortTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydonmetadataTurobjectTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydoncache_controlTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Kirish materiallari birikmalari

Matn · UTF8 matni

messages[].content yoki type=text bloki

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrmaterial_typesParametr talablaritext
ParametrrequiredParametr talablarimodel, messages, max_tokens
Parametroptional_controlsParametr talablarinull
Parametrcontent_typeParametr talablariapplication/json
Parametrtext_file_materialParametr 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.

Tariflar

Ailoot usullari tariflari

Ailoot Anthropic MessagesPOST /v1/messages
Kirish
0,0003 US$ / 1K
Chiqish
0,0012 US$ / 1K
Keshdan o‘qish
≈0,00003 US$ / 1K
Keshga yozish
0,000375 US$ / 1K
Keshga yozish, 5 daqiqa
0,000375 US$ / 1K tokens

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 →

Ma’lumot uchun tarif tafsilotlari

Modelning ma’lumot jadvallari usul tanlash, narx hisobi yoki to‘lovlarni o‘zgartirmaydi. Amaldagi to‘lovlar tanlangan Ailoot usuli tariflari bo‘yicha hisoblanadi.

Modelning ma’lumot jadvallari

Sozlamalar bo‘yicha narxlar

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

SozlamaTariflar jadvaliTo‘lov
SozlamaOpenAI Chat · Tokenlar uchun asosiy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token
SozlamaAnthropic Messages · Tokenlar uchun asosiy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

So‘rov namunalari

Ailoot · Anthropic Messages

Matn namunasiPython SDKNode.js SDKPython oqimi
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "MiniMax-M2", "max_tokens": 1024, "messages": []}')
f=Path(os.environ["AILOOT_TEXT_FILE"])
if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")
text=f.read_text(encoding="utf-8")
if not text.strip():raise SystemExit("Source material must contain text")
p["messages"]=[{"role":"user","content":"Summarize this material and quote supporting source passages.\n\n"+text}]
Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \
  -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
print(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))
PY_RESULT
import anthropic
import os

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client = anthropic.Anthropic(base_url="https://api.ailoot.ai", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)
msg = client.messages.create(
    model="MiniMax-M2",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}],
)
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(msg.to_dict(mode="json"))
import Anthropic from "@anthropic-ai/sdk";

if (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");
const client = new Anthropic({ baseURL: "https://api.ailoot.ai", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });
const msg = await client.messages.create({
  model: "MiniMax-M2",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Explain the difference between an observation and a hypothesis in three sentences." }],
});
import { writeFileSync } from "node:fs";

function saveResult(data, stem = "result") {
  writeFileSync(stem + ".json", JSON.stringify(data, null, 2));
  console.log(JSON.stringify(data, null, 2));
  const saveBlob = (encoded, suffix, label) => {
    const raw = Buffer.from(encoded, "base64");
    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;
    const file = `${stem}-${label}.${ext}`;
    writeFileSync(file, raw); console.log("Saved", file);
  };
  for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block));
  for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {}));
  for (const [i, item] of (data.output || []).entries()) {
    console.log("Output", i, JSON.stringify(item));
    if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`);
  }
  for (const [ci, candidate] of (data.candidates || []).entries()) {
    for (const [pi, part] of (candidate.content?.parts || []).entries()) {
      console.log("Candidate/part", ci, pi, JSON.stringify(part));
      const blob = part.inlineData || part.inline_data;
      if (blob?.data) {
        const mime = blob.mimeType || blob.mime_type;
        const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin";
        saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`);
      }
    }
  }
}

saveResult(msg);
import anthropic,os
from pathlib import Path

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client=anthropic.Anthropic(base_url="https://api.ailoot.ai",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

with client.messages.stream(model='MiniMax-M2',max_tokens=1024,messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:
    with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:
        for event in stream:
            payload=event.to_dict(mode="json")
            log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush()
            print(json.dumps(payload,ensure_ascii=False))
    final=stream.get_final_message()
    save_result(final.to_dict(mode="json"),"stream-result")

Natijalarni olish va saqlash

Ailoot · Anthropic Messages

json yoki SSE

Barcha tayyor natijalarni saqlash

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

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

Asl javobni tekshirish kerak bo‘lsa

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

Javob maydonlari
MaydonTur
MaydonidTurstring
MaydonmodelTurstring
Maydoncontent[]Turtext|thinking|tool_use|other content block
Maydonstop_reasonTurstring|null
MaydonusageTurobject
Xatolar va keyingi qadamlar
JavobShartKeyingi qadam
Javob400 · invalid_request_errorShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘riKeyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.

Taxalluslar

Model taxalluslari

Ailoot MiniMax-M2, minimax/minimax-m2
Ailoot Ultra MiniMax-M2, minimax/minimax-m2

Usul yo‘llari taxalluslari

/chat/completions

Chaqirish uchun tegishli ID lar

minimax/minimax-m2, minimax/minimax-m2, MiniMax-M2, MiniMax-M2

Ailoot · OpenAI Responses POST /v1/responses MiniMax-M2

MiniMax-M2

MiniMax /MiniMax-M2
Model ID
  • MiniMax-M2
  • minimax/minimax-m2

Boshlash

MiniMax M2 — MiniMax’ning jami 230B parametrdan 10B tasi faol bo‘lgan, boshidan oxirigacha dasturlash va agent ish jarayonlari uchun mo‘ljallangan matn modeli. Uning e’lon qilingan profilida mulohaza, funksiya vositalari, tuzilmali chiqishlar va katalog bo‘yicha 204,800 tokenli kontekst mavjud.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
Matn

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot · OpenAI Responses POST /v1/responses
Kirish
text
Chiqish
text
Kontekst
204 800 tokens
Eng katta chiqish
204 800 tokens
Chiqish formati
json yoki SSE
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
MiniMax-M2
Standart—
MaydoninputTurstring|input_item[]TalabMajburiyQiymatlar va cheklovlarStandart—
MaydoninstructionsTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydoninput[].typeTurstringTalabIxtiyoriyQiymatlar va cheklovlar
message, reasoning, function_call, function_call_output
Standart—
Maydoninput[].contentTurstring|content_part[]TalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydoninput[].call_idTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: function_call_output; haqiqatan qaytarilgan call_id qiymatini saqlang
Standart—
Maydoninput[].outputTurstring|content_part[]TalabIxtiyoriyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: function_call_output
Standart—
Maydonmax_output_tokensTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Modelga bogʻliq
Standart—
MaydonstreamTurbooleanTalabIxtiyoriyQiymatlar va cheklovlarStandartfalse
MaydontemperatureTurnumberTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontop_pTurnumberTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydontoolsTurtool[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontool_choiceTurstring|objectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonparallel_tool_callsTurbooleanTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonreasoning.effortTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonreasoning.summaryTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontext.formatTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontext.verbosityTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydonincludeTurstring[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydonstoreTurbooleanTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonprevious_response_idTurstringTalabIxtiyoriyQiymatlar va cheklovlar
ID talabi: Faqat qo‘llab-quvvatlanadigan davom ettirishdan olingan haqiqiy javob IDsi
Modelga bogʻliq
Standart—
Maydonservice_tierTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonprompt_cache_keyTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonprompt_cache_optionsTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Kirish materiallari birikmalari

Matn · UTF8 matni

Kirish matni yoki input_text qismi

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrmaterial_typesParametr talablaritext
ParametrrequiredParametr talablarimodel, input
Parametroptional_controlsParametr talablarinull
Parametrcontent_typeParametr talablariapplication/json
Parametrtext_file_materialParametr 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.

Tariflar

Ailoot usullari tariflari

Ailoot OpenAI ResponsesPOST /v1/responses
Kirish
0,0003 US$ / 1K
Chiqish
0,0012 US$ / 1K
Keshdan o‘qish
≈0,00003 US$ / 1K
Keshga yozish
0,000375 US$ / 1K
Keshga yozish, 5 daqiqa
0,000375 US$ / 1K tokens

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 →

Ma’lumot uchun tarif tafsilotlari

Modelning ma’lumot jadvallari usul tanlash, narx hisobi yoki to‘lovlarni o‘zgartirmaydi. Amaldagi to‘lovlar tanlangan Ailoot usuli tariflari bo‘yicha hisoblanadi.

Modelning ma’lumot jadvallari

Sozlamalar bo‘yicha narxlar

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

SozlamaTariflar jadvaliTo‘lov
SozlamaOpenAI Chat · Tokenlar uchun asosiy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token
SozlamaAnthropic Messages · Tokenlar uchun asosiy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

So‘rov namunalari

Ailoot · OpenAI Responses

Matn namunasiPython SDKNode.js SDKPython oqimi
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "MiniMax-M2", "input": ""}')
f=Path(os.environ["AILOOT_TEXT_FILE"])
if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")
text=f.read_text(encoding="utf-8")
if not text.strip():raise SystemExit("Source material must contain text")
p["input"]="Summarize this material and quote supporting source passages.\n\n"+text
Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/responses \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(json.loads(Path("result.json").read_text(encoding="utf-8")))
PY_RESULT
from openai import OpenAI
import os

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client = OpenAI(base_url="https://api.ailoot.ai/v1", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)
resp = client.responses.create(model="MiniMax-M2", input="Explain the difference between an observation and a hypothesis in three sentences.")
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(resp.to_dict(mode="json"))
import OpenAI from "openai";

if (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");
const client = new OpenAI({ baseURL: "https://api.ailoot.ai/v1", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });
const resp = await client.responses.create({ model: "MiniMax-M2", input: "Explain the difference between an observation and a hypothesis in three sentences." });
import { writeFileSync } from "node:fs";

function saveResult(data, stem = "result") {
  writeFileSync(stem + ".json", JSON.stringify(data, null, 2));
  console.log(JSON.stringify(data, null, 2));
  const saveBlob = (encoded, suffix, label) => {
    const raw = Buffer.from(encoded, "base64");
    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;
    const file = `${stem}-${label}.${ext}`;
    writeFileSync(file, raw); console.log("Saved", file);
  };
  for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block));
  for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {}));
  for (const [i, item] of (data.output || []).entries()) {
    console.log("Output", i, JSON.stringify(item));
    if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`);
  }
  for (const [ci, candidate] of (data.candidates || []).entries()) {
    for (const [pi, part] of (candidate.content?.parts || []).entries()) {
      console.log("Candidate/part", ci, pi, JSON.stringify(part));
      const blob = part.inlineData || part.inline_data;
      if (blob?.data) {
        const mime = blob.mimeType || blob.mime_type;
        const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin";
        saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`);
      }
    }
  }
}

saveResult(resp);
from openai import OpenAI
import os
from pathlib import Path
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
with client.responses.stream(model="MiniMax-M2", input="Explain the difference between an observation and a hypothesis in three sentences.") as stream:
    with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:
        for event in stream:
            payload=event.to_dict(mode="json")
            log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush()
            print(json.dumps(payload,ensure_ascii=False))
    final=stream.get_final_response()
    save_result(final.to_dict(mode="json"),"stream-result")

Natijalarni olish va saqlash

Ailoot · OpenAI Responses

json yoki SSE

Barcha tayyor natijalarni saqlash

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.

Asl javobni tekshirish kerak bo‘lsa

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

Javob maydonlari
MaydonTur
MaydonidTurstring
MaydonstatusTurstring
Maydonoutput[]Turmessage|reasoning|function_call|other item
Maydonoutput[].content[]Turcontent part
MaydonusageTurobject
Xatolar va keyingi qadamlar
JavobShartKeyingi qadam
Javob400 · invalid_request_errorShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘riKeyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.

Taxalluslar

Model taxalluslari

Ailoot MiniMax-M2, minimax/minimax-m2
Ailoot Ultra MiniMax-M2, minimax/minimax-m2

Usul yo‘llari taxalluslari

/chat/completions

Chaqirish uchun tegishli ID lar

minimax/minimax-m2, minimax/minimax-m2, MiniMax-M2, MiniMax-M2

Ailoot · OpenAI Chat POST /v1/chat/completions MiniMax-M2

MiniMax-M2

MiniMax /MiniMax-M2
Model ID
  • MiniMax-M2
  • minimax/minimax-m2

Boshlash

MiniMax M2 — MiniMax’ning jami 230B parametrdan 10B tasi faol bo‘lgan, boshidan oxirigacha dasturlash va agent ish jarayonlari uchun mo‘ljallangan matn modeli. Uning e’lon qilingan profilida mulohaza, funksiya vositalari, tuzilmali chiqishlar va katalog bo‘yicha 204,800 tokenli kontekst mavjud.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
Matn

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot · OpenAI Chat POST /v1/chat/completions
Kirish
text
Chiqish
text
Kontekst
204 800 tokens
Eng katta chiqish
204 800 tokens
Chiqish formati
json yoki SSE
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
MiniMax-M2
Standart—
MaydonmessagesTurmessage[]TalabMajburiyQiymatlar va cheklovlar
Elementlarning eng kam soni: 1
Standart—
Maydonmessages[].roleTurstringTalabMajburiyQiymatlar 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[].contentTurstring|content_part[]|nullTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydonmessages[].tool_callsTurtool_call[]TalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydonmessages[].tool_call_idTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: role=tool; haqiqatan qaytarilgan IDdan foydalaning
Standart—
Maydonmessages[].nameTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydonmessages[].reasoning_contentTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: Ushbu model foydalanganda, haqiqatan qaytarilgan fikrlash ma’lumotlarini keyingi so‘rovda qayta yuboring
Standart—
Maydonmessages[].reasoning_detailsTurobject|arrayTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonmax_tokensTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Modelga bogʻliq
Standart—
Maydonmax_completion_tokensTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Modelga bogʻliq
Standart—
MaydonstreamTurbooleanTalabIxtiyoriyQiymatlar va cheklovlarStandartfalse
Maydonstream_options.include_usageTurbooleanTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydontemperatureTurnumberTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontop_pTurnumberTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydonstopTurstring|string[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydontoolsTurfunction_tool[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontools[].typeTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontools[].function.nameTurstringTalabMajburiyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: Funksiya vositasi taqdim etilgan
Standart—
Maydontools[].function.descriptionTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydontools[].function.parametersTurJSON_schemaTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydontool_choiceTurstring|objectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonparallel_tool_callsTurbooleanTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonresponse_formatTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonreasoning_effortTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydonreasoningTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonservice_tierTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydoninstructionsTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Moslik maydoni
Standart—
MaydonfunctionsTurfunction[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonfunction_callTurstring|objectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonprompt_cache_optionsTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Kirish materiallari birikmalari

Matn · UTF8 matni

messages[].content

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrmaterial_typesParametr talablaritext
ParametrrequiredParametr talablarimodel, messages
Parametroptional_controlsParametr talablarinull
Parametrcontent_typeParametr talablariapplication/json
Parametrtext_file_materialParametr 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.

Tariflar

Ailoot usullari tariflari

Ailoot OpenAI ChatPOST /v1/chat/completions
Kirish
0,0003 US$ / 1K
Chiqish
0,0012 US$ / 1K
Keshdan o‘qish
≈0,00003 US$ / 1K
Keshga yozish
0,000375 US$ / 1K
Keshga yozish, 5 daqiqa
0,000375 US$ / 1K tokens

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 →

Ma’lumot uchun tarif tafsilotlari

Modelning ma’lumot jadvallari usul tanlash, narx hisobi yoki to‘lovlarni o‘zgartirmaydi. Amaldagi to‘lovlar tanlangan Ailoot usuli tariflari bo‘yicha hisoblanadi.

Modelning ma’lumot jadvallari

Sozlamalar bo‘yicha narxlar

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

SozlamaTariflar jadvaliTo‘lov
SozlamaOpenAI Chat · Tokenlar uchun asosiy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token
SozlamaAnthropic Messages · Tokenlar uchun asosiy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

So‘rov namunalari

Ailoot · OpenAI Chat

Matn namunasiPython SDKNode.js SDKPython oqimi
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "MiniMax-M2", "messages": []}')
f=Path(os.environ["AILOOT_TEXT_FILE"])
if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")
text=f.read_text(encoding="utf-8")
if not text.strip():raise SystemExit("Source material must contain text")
p["messages"]=[{"role":"user","content":"Summarize this material and quote supporting source passages.\n\n"+text}]
Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
print(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))
PY_RESULT
from openai import OpenAI
import os

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client = OpenAI(base_url="https://api.ailoot.ai/v1", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)
resp = client.chat.completions.create(
    model="MiniMax-M2",
    messages=[{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}],
)
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(resp.to_dict(mode="json"))
import OpenAI from "openai";

if (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");
const client = new OpenAI({ baseURL: "https://api.ailoot.ai/v1", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });
const resp = await client.chat.completions.create({
  model: "MiniMax-M2",
  messages: [{ role: "user", content: "Explain the difference between an observation and a hypothesis in three sentences." }],
});
import { writeFileSync } from "node:fs";

function saveResult(data, stem = "result") {
  writeFileSync(stem + ".json", JSON.stringify(data, null, 2));
  console.log(JSON.stringify(data, null, 2));
  const saveBlob = (encoded, suffix, label) => {
    const raw = Buffer.from(encoded, "base64");
    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;
    const file = `${stem}-${label}.${ext}`;
    writeFileSync(file, raw); console.log("Saved", file);
  };
  for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block));
  for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {}));
  for (const [i, item] of (data.output || []).entries()) {
    console.log("Output", i, JSON.stringify(item));
    if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`);
  }
  for (const [ci, candidate] of (data.candidates || []).entries()) {
    for (const [pi, part] of (candidate.content?.parts || []).entries()) {
      console.log("Candidate/part", ci, pi, JSON.stringify(part));
      const blob = part.inlineData || part.inline_data;
      if (blob?.data) {
        const mime = blob.mimeType || blob.mime_type;
        const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin";
        saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`);
      }
    }
  }
}

saveResult(resp);
from openai import OpenAI
import os
from pathlib import Path

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

with client.chat.completions.stream(model='MiniMax-M2',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:
    with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:
        for event in stream:
            payload=event.to_dict(mode="json")
            log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush()
            print(json.dumps(payload,ensure_ascii=False))
    final=stream.get_final_completion()
    save_result(final.to_dict(mode="json"),"stream-result")

Natijalarni olish va saqlash

Ailoot · OpenAI Chat

json yoki SSE

Barcha tayyor natijalarni saqlash

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

Har bir javob variantini va uning kontenti, vosita chaqiruvlari hamda fikrlash ma’lumotlarini saqlang. SSEda o‘zgarish qismlarini variant va vosita indeksi bo‘yicha yig‘ing, yakuniy tugash sababi va sarf ma’lumotlarini saqlang.

Asl javobni tekshirish kerak bo‘lsa

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

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

Taxalluslar

Model taxalluslari

Ailoot MiniMax-M2, minimax/minimax-m2
Ailoot Ultra MiniMax-M2, minimax/minimax-m2

Usul yo‘llari taxalluslari

/chat/completions

Chaqirish uchun tegishli ID lar

minimax/minimax-m2, minimax/minimax-m2, MiniMax-M2, MiniMax-M2

Ailoot Ultra · OpenAI Chat POST /v1/chat/completions MiniMax-M2

MiniMax-M2

MiniMax /MiniMax-M2
Model ID
  • MiniMax-M2
  • minimax/minimax-m2

Boshlash

MiniMax M2 — MiniMax’ning jami 230B parametrdan 10B tasi faol bo‘lgan, boshidan oxirigacha dasturlash va agent ish jarayonlari uchun mo‘ljallangan matn modeli. Uning e’lon qilingan profilida mulohaza, funksiya vositalari, tuzilmali chiqishlar va katalog bo‘yicha 204,800 tokenli kontekst mavjud.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
Matn

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · OpenAI Chat POST /v1/chat/completions
Kirish
text
Chiqish
text
Chiqish formati
json yoki SSE
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
MiniMax-M2
Standart—
MaydonmessagesTurmessage[]TalabMajburiyQiymatlar va cheklovlar
Elementlarning eng kam soni: 1
Standart—
Maydonmessages[].roleTurstringTalabMajburiyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonmessages[].contentTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: Matnli suhbat
Standart—
MaydonstreamTurbooleanTalabIxtiyoriyQiymatlar va cheklovlarStandartfalse
Kirish materiallari birikmalari

Matn · UTF8 matni

messages[].content

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

Usul taxalluslari: /chat/completions

Hayot sikli: synchronous_or_streamed

Aniq model IDsini messages bilan yuboring.

Kontekst uzunligi chiqishning yuqori chegarasi emas.

Matn fayli aynan matnni taqdim etishi mumkin; tasvir, audio va fayl obyekti kirishlari aniq bog‘lashni talab qiladi.

Tariflar

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

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

Ailoot tariflari

Sozlamalar bo‘yicha narxlar

SozlamaTariflar jadvaliTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. Summani band qilish va to‘lovni hisoblash tartibi →

So‘rov namunalari

Ailoot Ultra · OpenAI Chat

curlPython SDK
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model":"MiniMax-M2","messages":[{"role":"user","content":"hello"}],"stream":false}')

Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
r=json.loads(Path("result.json").read_text())
for x in r.get("choices",[]):
    if x.get("message",{}).get("content"): print(x["message"]["content"])
PY_RESULT
from openai import OpenAI
import os

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
result=client.chat.completions.create(model="MiniMax-M2", messages=[{"role":"user","content":"hello"}], stream=False)
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(result.to_dict(mode="json"))

Natijalarni olish va saqlash

Ailoot Ultra · OpenAI Chat

json yoki SSE

Barcha tayyor natijalarni saqlash

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

Har bir javob variantini va uning kontenti, vosita chaqiruvlari hamda fikrlash ma’lumotlarini saqlang. SSEda o‘zgarish qismlarini variant va vosita indeksi bo‘yicha yig‘ing, yakuniy tugash sababi va sarf ma’lumotlarini saqlang.

Asl javobni tekshirish kerak bo‘lsa

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

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

Taxalluslar

Model taxalluslari

Ailoot MiniMax-M2, minimax/minimax-m2
Ailoot Ultra MiniMax-M2, minimax/minimax-m2

Usul yo‘llari taxalluslari

/chat/completions

Chaqirish uchun tegishli ID lar

minimax/minimax-m2, minimax/minimax-m2, MiniMax-M2, MiniMax-M2

Ailoot Ultra · Anthropic Messages POST /v1/messages MiniMax-M2

MiniMax-M2

MiniMax /MiniMax-M2
Model ID
  • MiniMax-M2
  • minimax/minimax-m2

Boshlash

MiniMax M2 — MiniMax’ning jami 230B parametrdan 10B tasi faol bo‘lgan, boshidan oxirigacha dasturlash va agent ish jarayonlari uchun mo‘ljallangan matn modeli. Uning e’lon qilingan profilida mulohaza, funksiya vositalari, tuzilmali chiqishlar va katalog bo‘yicha 204,800 tokenli kontekst mavjud.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
json · sse

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · Anthropic Messages POST /v1/messages
Kirish
text
Chiqish
json, sse
Chiqish formati
JSON yoki SSE
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
MiniMax-M2
Standart—
MaydonmessagesTurmessage[]TalabMajburiyQiymatlar va cheklovlarStandart—
Maydonmessages[].roleTurstringTalabMajburiyQiymatlar va cheklovlar
user, assistant
Standart—
Maydonmessages[].contentTurstringTalabMajburiyQiymatlar va cheklovlarStandart—
Maydonmax_tokensTurintegerTalabMajburiyQiymatlar va cheklovlarStandart—
MaydonstreamTurbooleanTalabIxtiyoriyQiymatlar va cheklovlarStandartfalse
Kirish materiallari birikmalari

Matn · UTF-8 matni

messages[].content satri

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

Hayot sikli: synchronous_or_streamed

Tariflar

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

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

Ailoot tariflari

Sozlamalar bo‘yicha narxlar

SozlamaTariflar jadvaliTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. Summani band qilish va to‘lovni hisoblash tartibi →

So‘rov namunalari

Ailoot Ultra · Anthropic Messages

To‘liq minimal JSON so‘rov tanasicurl · O‘zingizning UTF-8 hujjatingizdan foydalaningPython · Natijani to‘liq saqlang
{
  "model": "MiniMax-M2",
  "max_tokens": 128,
  "messages": [
    {
      "role": "user",
      "content": "Summarize the incident report."
    }
  ],
  "stream": false
}
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json, os
from pathlib import Path

source_file = Path(os.environ['AILOOT_TEXT_FILE'])
if not source_file.is_file():
    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')
text = source_file.read_text(encoding='utf-8')
if not text.strip():
    raise SystemExit('Choose a document containing text')
payload = {'model': 'MiniMax-M2', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}
payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text
Path('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \
  -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path

result = json.loads(Path('result.json').read_text(encoding='utf-8'))
print(json.dumps(result, ensure_ascii=False, indent=2))
PY_RESULT
import json, os
from pathlib import Path

source_file = Path(os.environ['AILOOT_TEXT_FILE'])
if not source_file.is_file():
    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')
text = source_file.read_text(encoding='utf-8')
if not text.strip():
    raise SystemExit('Choose a document containing text')
payload = {'model': 'MiniMax-M2', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}
payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text
from urllib.request import Request, urlopen
from urllib.error import HTTPError

api_key = os.environ['AILOOT_API_KEY']
if not api_key.strip():
    raise SystemExit('Set AILOOT_API_KEY before making a request')
headers = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}
headers['x-api-key'] = api_key
request = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')
try:
    with urlopen(request, timeout=120) as response:
        raw = response.read()
        Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')
except HTTPError as error:
    Path('result.json').write_bytes(error.read())
    Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')
    raise
Path('result.json').write_bytes(raw)
import json
from pathlib import Path

result = json.loads(Path('result.json').read_text(encoding='utf-8'))
print(json.dumps(result, ensure_ascii=False, indent=2))

Natijalarni olish va saqlash

Ailoot Ultra · Anthropic Messages

JSON yoki SSE

Barcha tayyor natijalarni saqlash

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

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

Asl javobni tekshirish kerak bo‘lsa

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

Javob maydonlari
MaydonTur
MaydonidTurstring
MaydonmodelTurstring
Maydoncontent[]Turtext|thinking|tool_use|other content block
Maydonstop_reasonTurstring|null
MaydonusageTurobject
Xatolar va keyingi qadamlar
JavobShartKeyingi qadam
Javob400 · invalid_request_errorShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘riKeyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.

Taxalluslar

Model taxalluslari

Ailoot MiniMax-M2, minimax/minimax-m2
Ailoot Ultra MiniMax-M2, minimax/minimax-m2

Usul yo‘llari taxalluslari

/chat/completions

Chaqirish uchun tegishli ID lar

minimax/minimax-m2, minimax/minimax-m2, MiniMax-M2, MiniMax-M2

Ailoot · Anthropic Messages POST /v1/messages minimax/minimax-m2

minimax/minimax-m2

MiniMax /minimax/minimax-m2
Model ID
  • MiniMax-M2
  • minimax/minimax-m2

Boshlash

MiniMax M2 — MiniMax’ning jami 230B parametrdan 10B tasi faol bo‘lgan, boshidan oxirigacha dasturlash va agent ish jarayonlari uchun mo‘ljallangan matn modeli. Uning e’lon qilingan profilida mulohaza, funksiya vositalari, tuzilmali chiqishlar va katalog bo‘yicha 204,800 tokenli kontekst mavjud.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
Matn

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot · Anthropic Messages POST /v1/messages
Kirish
text
Chiqish
text
Kontekst
204 800 tokens
Eng katta chiqish
204 800 tokens
Chiqish formati
json yoki SSE
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
minimax/minimax-m2
Standart—
MaydonmessagesTurmessage[]TalabMajburiyQiymatlar va cheklovlar
Elementlarning eng kam soni: 1
Standart—
Maydonmessages[].roleTurstringTalabMajburiyQiymatlar va cheklovlar
user, assistant
Standart—
Maydonmessages[].contentTurstring|content_block[]TalabMajburiyQiymatlar va cheklovlarStandart—
MaydonsystemTurstring|content_block[]TalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydonmax_tokensTurintegerTalabMajburiyQiymatlar va cheklovlar
Eng kam: 1
Cheklov qo‘llanadigan obyekt: Tanlangan model yoki kartaning aniq yuqori chegarasi
Standart—
MaydonstreamTurbooleanTalabIxtiyoriyQiymatlar va cheklovlarStandartfalse
MaydontemperatureTurnumberTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontop_pTurnumberTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonstop_sequencesTurstring[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydontoolsTurtool[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontools[].nameTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: Vosita taqdim etilgan
Standart—
Maydontools[].descriptionTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydontools[].input_schemaTurJSON_schemaTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydontool_choiceTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonthinking.typeTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonthinking.budget_tokensTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonoutput_config.effortTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydonmetadataTurobjectTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydoncache_controlTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Kirish materiallari birikmalari

Matn · UTF8 matni

messages[].content yoki type=text bloki

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrmaterial_typesParametr talablaritext
ParametrrequiredParametr talablarimodel, messages, max_tokens
Parametroptional_controlsParametr talablarinull
Parametrcontent_typeParametr talablariapplication/json
Parametrtext_file_materialParametr 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.

Tariflar

Ailoot usullari tariflari

Ailoot Anthropic MessagesPOST /v1/messages
Kirish
0,0003 US$ / 1K
Chiqish
0,0012 US$ / 1K
Keshdan o‘qish
≈0,00003 US$ / 1K
Keshga yozish
0,000375 US$ / 1K
Keshga yozish, 5 daqiqa
0,000375 US$ / 1K tokens

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 →

Ma’lumot uchun tarif tafsilotlari

Modelning ma’lumot jadvallari usul tanlash, narx hisobi yoki to‘lovlarni o‘zgartirmaydi. Amaldagi to‘lovlar tanlangan Ailoot usuli tariflari bo‘yicha hisoblanadi.

Modelning ma’lumot jadvallari

Sozlamalar bo‘yicha narxlar

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

SozlamaTariflar jadvaliTo‘lov
SozlamaOpenAI Chat · Tokenlar uchun asosiy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token
SozlamaAnthropic Messages · Tokenlar uchun asosiy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

So‘rov namunalari

Ailoot · Anthropic Messages

Matn namunasiPython SDKNode.js SDKPython oqimi
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "minimax/minimax-m2", "max_tokens": 1024, "messages": []}')
f=Path(os.environ["AILOOT_TEXT_FILE"])
if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")
text=f.read_text(encoding="utf-8")
if not text.strip():raise SystemExit("Source material must contain text")
p["messages"]=[{"role":"user","content":"Summarize this material and quote supporting source passages.\n\n"+text}]
Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \
  -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
print(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))
PY_RESULT
import anthropic
import os

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client = anthropic.Anthropic(base_url="https://api.ailoot.ai", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)
msg = client.messages.create(
    model="minimax/minimax-m2",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}],
)
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(msg.to_dict(mode="json"))
import Anthropic from "@anthropic-ai/sdk";

if (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");
const client = new Anthropic({ baseURL: "https://api.ailoot.ai", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });
const msg = await client.messages.create({
  model: "minimax/minimax-m2",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Explain the difference between an observation and a hypothesis in three sentences." }],
});
import { writeFileSync } from "node:fs";

function saveResult(data, stem = "result") {
  writeFileSync(stem + ".json", JSON.stringify(data, null, 2));
  console.log(JSON.stringify(data, null, 2));
  const saveBlob = (encoded, suffix, label) => {
    const raw = Buffer.from(encoded, "base64");
    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;
    const file = `${stem}-${label}.${ext}`;
    writeFileSync(file, raw); console.log("Saved", file);
  };
  for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block));
  for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {}));
  for (const [i, item] of (data.output || []).entries()) {
    console.log("Output", i, JSON.stringify(item));
    if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`);
  }
  for (const [ci, candidate] of (data.candidates || []).entries()) {
    for (const [pi, part] of (candidate.content?.parts || []).entries()) {
      console.log("Candidate/part", ci, pi, JSON.stringify(part));
      const blob = part.inlineData || part.inline_data;
      if (blob?.data) {
        const mime = blob.mimeType || blob.mime_type;
        const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin";
        saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`);
      }
    }
  }
}

saveResult(msg);
import anthropic,os
from pathlib import Path

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client=anthropic.Anthropic(base_url="https://api.ailoot.ai",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

with client.messages.stream(model='minimax/minimax-m2',max_tokens=1024,messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:
    with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:
        for event in stream:
            payload=event.to_dict(mode="json")
            log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush()
            print(json.dumps(payload,ensure_ascii=False))
    final=stream.get_final_message()
    save_result(final.to_dict(mode="json"),"stream-result")

Natijalarni olish va saqlash

Ailoot · Anthropic Messages

json yoki SSE

Barcha tayyor natijalarni saqlash

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

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

Asl javobni tekshirish kerak bo‘lsa

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

Javob maydonlari
MaydonTur
MaydonidTurstring
MaydonmodelTurstring
Maydoncontent[]Turtext|thinking|tool_use|other content block
Maydonstop_reasonTurstring|null
MaydonusageTurobject
Xatolar va keyingi qadamlar
JavobShartKeyingi qadam
Javob400 · invalid_request_errorShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘riKeyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.

Taxalluslar

Model taxalluslari

Ailoot MiniMax-M2, minimax/minimax-m2
Ailoot Ultra MiniMax-M2, minimax/minimax-m2

Usul yo‘llari taxalluslari

/chat/completions

Chaqirish uchun tegishli ID lar

minimax/minimax-m2, minimax/minimax-m2, MiniMax-M2, MiniMax-M2

Ailoot · OpenAI Responses POST /v1/responses minimax/minimax-m2

minimax/minimax-m2

MiniMax /minimax/minimax-m2
Model ID
  • MiniMax-M2
  • minimax/minimax-m2

Boshlash

MiniMax M2 — MiniMax’ning jami 230B parametrdan 10B tasi faol bo‘lgan, boshidan oxirigacha dasturlash va agent ish jarayonlari uchun mo‘ljallangan matn modeli. Uning e’lon qilingan profilida mulohaza, funksiya vositalari, tuzilmali chiqishlar va katalog bo‘yicha 204,800 tokenli kontekst mavjud.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
Matn

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot · OpenAI Responses POST /v1/responses
Kirish
text
Chiqish
text
Kontekst
204 800 tokens
Eng katta chiqish
204 800 tokens
Chiqish formati
json yoki SSE
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
minimax/minimax-m2
Standart—
MaydoninputTurstring|input_item[]TalabMajburiyQiymatlar va cheklovlarStandart—
MaydoninstructionsTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydoninput[].typeTurstringTalabIxtiyoriyQiymatlar va cheklovlar
message, reasoning, function_call, function_call_output
Standart—
Maydoninput[].contentTurstring|content_part[]TalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydoninput[].call_idTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: function_call_output; haqiqatan qaytarilgan call_id qiymatini saqlang
Standart—
Maydoninput[].outputTurstring|content_part[]TalabIxtiyoriyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: function_call_output
Standart—
Maydonmax_output_tokensTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Modelga bogʻliq
Standart—
MaydonstreamTurbooleanTalabIxtiyoriyQiymatlar va cheklovlarStandartfalse
MaydontemperatureTurnumberTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontop_pTurnumberTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydontoolsTurtool[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontool_choiceTurstring|objectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonparallel_tool_callsTurbooleanTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonreasoning.effortTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonreasoning.summaryTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontext.formatTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontext.verbosityTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydonincludeTurstring[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydonstoreTurbooleanTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonprevious_response_idTurstringTalabIxtiyoriyQiymatlar va cheklovlar
ID talabi: Faqat qo‘llab-quvvatlanadigan davom ettirishdan olingan haqiqiy javob IDsi
Modelga bogʻliq
Standart—
Maydonservice_tierTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonprompt_cache_keyTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonprompt_cache_optionsTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Kirish materiallari birikmalari

Matn · UTF8 matni

Kirish matni yoki input_text qismi

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrmaterial_typesParametr talablaritext
ParametrrequiredParametr talablarimodel, input
Parametroptional_controlsParametr talablarinull
Parametrcontent_typeParametr talablariapplication/json
Parametrtext_file_materialParametr 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.

Tariflar

Ailoot usullari tariflari

Ailoot OpenAI ResponsesPOST /v1/responses
Kirish
0,0003 US$ / 1K
Chiqish
0,0012 US$ / 1K
Keshdan o‘qish
≈0,00003 US$ / 1K
Keshga yozish
0,000375 US$ / 1K
Keshga yozish, 5 daqiqa
0,000375 US$ / 1K tokens

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 →

Ma’lumot uchun tarif tafsilotlari

Modelning ma’lumot jadvallari usul tanlash, narx hisobi yoki to‘lovlarni o‘zgartirmaydi. Amaldagi to‘lovlar tanlangan Ailoot usuli tariflari bo‘yicha hisoblanadi.

Modelning ma’lumot jadvallari

Sozlamalar bo‘yicha narxlar

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

SozlamaTariflar jadvaliTo‘lov
SozlamaOpenAI Chat · Tokenlar uchun asosiy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token
SozlamaAnthropic Messages · Tokenlar uchun asosiy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

So‘rov namunalari

Ailoot · OpenAI Responses

Matn namunasiPython SDKNode.js SDKPython oqimi
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "minimax/minimax-m2", "input": ""}')
f=Path(os.environ["AILOOT_TEXT_FILE"])
if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")
text=f.read_text(encoding="utf-8")
if not text.strip():raise SystemExit("Source material must contain text")
p["input"]="Summarize this material and quote supporting source passages.\n\n"+text
Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/responses \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(json.loads(Path("result.json").read_text(encoding="utf-8")))
PY_RESULT
from openai import OpenAI
import os

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client = OpenAI(base_url="https://api.ailoot.ai/v1", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)
resp = client.responses.create(model="minimax/minimax-m2", input="Explain the difference between an observation and a hypothesis in three sentences.")
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(resp.to_dict(mode="json"))
import OpenAI from "openai";

if (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");
const client = new OpenAI({ baseURL: "https://api.ailoot.ai/v1", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });
const resp = await client.responses.create({ model: "minimax/minimax-m2", input: "Explain the difference between an observation and a hypothesis in three sentences." });
import { writeFileSync } from "node:fs";

function saveResult(data, stem = "result") {
  writeFileSync(stem + ".json", JSON.stringify(data, null, 2));
  console.log(JSON.stringify(data, null, 2));
  const saveBlob = (encoded, suffix, label) => {
    const raw = Buffer.from(encoded, "base64");
    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;
    const file = `${stem}-${label}.${ext}`;
    writeFileSync(file, raw); console.log("Saved", file);
  };
  for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block));
  for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {}));
  for (const [i, item] of (data.output || []).entries()) {
    console.log("Output", i, JSON.stringify(item));
    if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`);
  }
  for (const [ci, candidate] of (data.candidates || []).entries()) {
    for (const [pi, part] of (candidate.content?.parts || []).entries()) {
      console.log("Candidate/part", ci, pi, JSON.stringify(part));
      const blob = part.inlineData || part.inline_data;
      if (blob?.data) {
        const mime = blob.mimeType || blob.mime_type;
        const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin";
        saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`);
      }
    }
  }
}

saveResult(resp);
from openai import OpenAI
import os
from pathlib import Path
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
with client.responses.stream(model="minimax/minimax-m2", input="Explain the difference between an observation and a hypothesis in three sentences.") as stream:
    with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:
        for event in stream:
            payload=event.to_dict(mode="json")
            log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush()
            print(json.dumps(payload,ensure_ascii=False))
    final=stream.get_final_response()
    save_result(final.to_dict(mode="json"),"stream-result")

Natijalarni olish va saqlash

Ailoot · OpenAI Responses

json yoki SSE

Barcha tayyor natijalarni saqlash

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.

Asl javobni tekshirish kerak bo‘lsa

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

Javob maydonlari
MaydonTur
MaydonidTurstring
MaydonstatusTurstring
Maydonoutput[]Turmessage|reasoning|function_call|other item
Maydonoutput[].content[]Turcontent part
MaydonusageTurobject
Xatolar va keyingi qadamlar
JavobShartKeyingi qadam
Javob400 · invalid_request_errorShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘riKeyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.

Taxalluslar

Model taxalluslari

Ailoot MiniMax-M2, minimax/minimax-m2
Ailoot Ultra MiniMax-M2, minimax/minimax-m2

Usul yo‘llari taxalluslari

/chat/completions

Chaqirish uchun tegishli ID lar

minimax/minimax-m2, minimax/minimax-m2, MiniMax-M2, MiniMax-M2

Ailoot · OpenAI Chat POST /v1/chat/completions minimax/minimax-m2

minimax/minimax-m2

MiniMax /minimax/minimax-m2
Model ID
  • MiniMax-M2
  • minimax/minimax-m2

Boshlash

MiniMax M2 — MiniMax’ning jami 230B parametrdan 10B tasi faol bo‘lgan, boshidan oxirigacha dasturlash va agent ish jarayonlari uchun mo‘ljallangan matn modeli. Uning e’lon qilingan profilida mulohaza, funksiya vositalari, tuzilmali chiqishlar va katalog bo‘yicha 204,800 tokenli kontekst mavjud.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
Matn

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot · OpenAI Chat POST /v1/chat/completions
Kirish
text
Chiqish
text
Kontekst
204 800 tokens
Eng katta chiqish
204 800 tokens
Chiqish formati
json yoki SSE
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
minimax/minimax-m2
Standart—
MaydonmessagesTurmessage[]TalabMajburiyQiymatlar va cheklovlar
Elementlarning eng kam soni: 1
Standart—
Maydonmessages[].roleTurstringTalabMajburiyQiymatlar 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[].contentTurstring|content_part[]|nullTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydonmessages[].tool_callsTurtool_call[]TalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydonmessages[].tool_call_idTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: role=tool; haqiqatan qaytarilgan IDdan foydalaning
Standart—
Maydonmessages[].nameTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydonmessages[].reasoning_contentTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: Ushbu model foydalanganda, haqiqatan qaytarilgan fikrlash ma’lumotlarini keyingi so‘rovda qayta yuboring
Standart—
Maydonmessages[].reasoning_detailsTurobject|arrayTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonmax_tokensTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Modelga bogʻliq
Standart—
Maydonmax_completion_tokensTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Modelga bogʻliq
Standart—
MaydonstreamTurbooleanTalabIxtiyoriyQiymatlar va cheklovlarStandartfalse
Maydonstream_options.include_usageTurbooleanTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydontemperatureTurnumberTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontop_pTurnumberTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydonstopTurstring|string[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydontoolsTurfunction_tool[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontools[].typeTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontools[].function.nameTurstringTalabMajburiyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: Funksiya vositasi taqdim etilgan
Standart—
Maydontools[].function.descriptionTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydontools[].function.parametersTurJSON_schemaTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydontool_choiceTurstring|objectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonparallel_tool_callsTurbooleanTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonresponse_formatTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonreasoning_effortTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydonreasoningTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonservice_tierTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydoninstructionsTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Moslik maydoni
Standart—
MaydonfunctionsTurfunction[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonfunction_callTurstring|objectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonprompt_cache_optionsTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Kirish materiallari birikmalari

Matn · UTF8 matni

messages[].content

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrmaterial_typesParametr talablaritext
ParametrrequiredParametr talablarimodel, messages
Parametroptional_controlsParametr talablarinull
Parametrcontent_typeParametr talablariapplication/json
Parametrtext_file_materialParametr 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.

Tariflar

Ailoot usullari tariflari

Ailoot OpenAI ChatPOST /v1/chat/completions
Kirish
0,0003 US$ / 1K
Chiqish
0,0012 US$ / 1K
Keshdan o‘qish
≈0,00003 US$ / 1K
Keshga yozish
0,000375 US$ / 1K
Keshga yozish, 5 daqiqa
0,000375 US$ / 1K tokens

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 →

Ma’lumot uchun tarif tafsilotlari

Modelning ma’lumot jadvallari usul tanlash, narx hisobi yoki to‘lovlarni o‘zgartirmaydi. Amaldagi to‘lovlar tanlangan Ailoot usuli tariflari bo‘yicha hisoblanadi.

Modelning ma’lumot jadvallari

Sozlamalar bo‘yicha narxlar

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

SozlamaTariflar jadvaliTo‘lov
SozlamaOpenAI Chat · Tokenlar uchun asosiy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token
SozlamaAnthropic Messages · Tokenlar uchun asosiy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

So‘rov namunalari

Ailoot · OpenAI Chat

Matn namunasiPython SDKNode.js SDKPython oqimi
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "minimax/minimax-m2", "messages": []}')
f=Path(os.environ["AILOOT_TEXT_FILE"])
if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")
text=f.read_text(encoding="utf-8")
if not text.strip():raise SystemExit("Source material must contain text")
p["messages"]=[{"role":"user","content":"Summarize this material and quote supporting source passages.\n\n"+text}]
Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
print(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))
PY_RESULT
from openai import OpenAI
import os

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client = OpenAI(base_url="https://api.ailoot.ai/v1", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)
resp = client.chat.completions.create(
    model="minimax/minimax-m2",
    messages=[{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}],
)
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(resp.to_dict(mode="json"))
import OpenAI from "openai";

if (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");
const client = new OpenAI({ baseURL: "https://api.ailoot.ai/v1", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });
const resp = await client.chat.completions.create({
  model: "minimax/minimax-m2",
  messages: [{ role: "user", content: "Explain the difference between an observation and a hypothesis in three sentences." }],
});
import { writeFileSync } from "node:fs";

function saveResult(data, stem = "result") {
  writeFileSync(stem + ".json", JSON.stringify(data, null, 2));
  console.log(JSON.stringify(data, null, 2));
  const saveBlob = (encoded, suffix, label) => {
    const raw = Buffer.from(encoded, "base64");
    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;
    const file = `${stem}-${label}.${ext}`;
    writeFileSync(file, raw); console.log("Saved", file);
  };
  for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block));
  for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {}));
  for (const [i, item] of (data.output || []).entries()) {
    console.log("Output", i, JSON.stringify(item));
    if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`);
  }
  for (const [ci, candidate] of (data.candidates || []).entries()) {
    for (const [pi, part] of (candidate.content?.parts || []).entries()) {
      console.log("Candidate/part", ci, pi, JSON.stringify(part));
      const blob = part.inlineData || part.inline_data;
      if (blob?.data) {
        const mime = blob.mimeType || blob.mime_type;
        const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin";
        saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`);
      }
    }
  }
}

saveResult(resp);
from openai import OpenAI
import os
from pathlib import Path

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

with client.chat.completions.stream(model='minimax/minimax-m2',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:
    with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:
        for event in stream:
            payload=event.to_dict(mode="json")
            log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush()
            print(json.dumps(payload,ensure_ascii=False))
    final=stream.get_final_completion()
    save_result(final.to_dict(mode="json"),"stream-result")

Natijalarni olish va saqlash

Ailoot · OpenAI Chat

json yoki SSE

Barcha tayyor natijalarni saqlash

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

Har bir javob variantini va uning kontenti, vosita chaqiruvlari hamda fikrlash ma’lumotlarini saqlang. SSEda o‘zgarish qismlarini variant va vosita indeksi bo‘yicha yig‘ing, yakuniy tugash sababi va sarf ma’lumotlarini saqlang.

Asl javobni tekshirish kerak bo‘lsa

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

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

Taxalluslar

Model taxalluslari

Ailoot MiniMax-M2, minimax/minimax-m2
Ailoot Ultra MiniMax-M2, minimax/minimax-m2

Usul yo‘llari taxalluslari

/chat/completions

Chaqirish uchun tegishli ID lar

minimax/minimax-m2, minimax/minimax-m2, MiniMax-M2, MiniMax-M2

Ailoot Ultra · OpenAI Chat POST /v1/chat/completions minimax/minimax-m2

minimax/minimax-m2

MiniMax /minimax/minimax-m2
Model ID
  • MiniMax-M2
  • minimax/minimax-m2

Boshlash

MiniMax M2 — MiniMax’ning jami 230B parametrdan 10B tasi faol bo‘lgan, boshidan oxirigacha dasturlash va agent ish jarayonlari uchun mo‘ljallangan matn modeli. Uning e’lon qilingan profilida mulohaza, funksiya vositalari, tuzilmali chiqishlar va katalog bo‘yicha 204,800 tokenli kontekst mavjud.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
Matn

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · OpenAI Chat POST /v1/chat/completions
Kirish
text
Chiqish
text
Chiqish formati
json yoki SSE
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
minimax/minimax-m2
Standart—
MaydonmessagesTurmessage[]TalabMajburiyQiymatlar va cheklovlar
Elementlarning eng kam soni: 1
Standart—
Maydonmessages[].roleTurstringTalabMajburiyQiymatlar 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[].contentTurstring|content_part[]|nullTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydonmessages[].tool_callsTurtool_call[]TalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydonmessages[].tool_call_idTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: role=tool; haqiqatan qaytarilgan IDdan foydalaning
Standart—
Maydonmessages[].nameTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydonmessages[].reasoning_contentTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: Ushbu model foydalanganda, haqiqatan qaytarilgan fikrlash ma’lumotlarini keyingi so‘rovda qayta yuboring
Standart—
Maydonmessages[].reasoning_detailsTurobject|arrayTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonmax_tokensTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Modelga bogʻliq
Standart—
Maydonmax_completion_tokensTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Modelga bogʻliq
Standart—
MaydonstreamTurbooleanTalabIxtiyoriyQiymatlar va cheklovlarStandartfalse
Maydonstream_options.include_usageTurbooleanTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydontemperatureTurnumberTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontop_pTurnumberTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydonstopTurstring|string[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydontoolsTurfunction_tool[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydontools[].typeTurstringTalabIxtiyoriyQiymatlar va cheklovlar
function
Modelga bogʻliq
Standart—
Maydontools[].function.nameTurstringTalabMajburiyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: Funksiya vositasi taqdim etilgan
Standart—
Maydontools[].function.descriptionTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydontools[].function.parametersTurJSON_schemaTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydontool_choiceTurstring|objectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonparallel_tool_callsTurbooleanTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonresponse_formatTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonreasoning_effortTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydonreasoningTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonservice_tierTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydoninstructionsTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Moslik maydoni
Standart—
MaydonfunctionsTurfunction[]TalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonfunction_callTurstring|objectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonprompt_cache_optionsTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
MaydonmodalitiesTurstring[]TalabIxtiyoriyQiymatlar va cheklovlar
text
Standart—
MaydonproviderTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Shartlar: Ushbu Ultra matn marshrutida provider.options rad etiladi
Standart—
Maydoncache_control.ttlTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Shartlar: Haqiqiy prompt-kesh joylarida 1h rad etiladi
Standart—
Kirish materiallari birikmalari

Matn · UTF8 matni

messages[].content

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrcontent_typeParametr talablariapplication/json
ParametrrequiredParametr talablarimodel, messages
Parametrmaterial_typesParametr talablaritext
ParametrmessagesParametr talablarirole/content tuzilishidagi xabarlar massivi
Parametrfile_materialParametr talablariHaqiqiy UTF-8 hujjati aynan matn sifatida kiritiladi; fayl IDsi faraz qilinmaydi
Parametroptional_controlsParametr talablaristream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice
Parametrcontrol_scopeParametr talablariModelga xos; foydalanuvchi funksiya vositalari hostingdagi vositalardan farq qiladi
Parametroutput_modalitiesParametr talablaritext
Parametrhosted_operationsParametr talablarifalse
Parametrmultimodal_input_bindingParametr 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.

Tariflar

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

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

Ailoot tariflari

Sozlamalar bo‘yicha narxlar

SozlamaTariflar jadvaliTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. Summani band qilish va to‘lovni hisoblash tartibi →

So‘rov namunalari

Ailoot Ultra · OpenAI Chat

curlPython SDKPython oqimi
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "minimax/minimax-m2", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')

Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
r=json.loads(Path("result.json").read_text())
for x in r.get("choices",[]):
    if x.get("message",{}).get("content"): print(x["message"]["content"])
PY_RESULT
from openai import OpenAI
import os

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
result=client.chat.completions.create(model='minimax/minimax-m2',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(result.to_dict(mode="json"))
from openai import OpenAI
import os
from pathlib import Path

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

with client.chat.completions.stream(model='minimax/minimax-m2',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:
    with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:
        for event in stream:
            payload=event.to_dict(mode="json")
            log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush()
            print(json.dumps(payload,ensure_ascii=False))
    final=stream.get_final_completion()
    save_result(final.to_dict(mode="json"),"stream-result")

Natijalarni olish va saqlash

Ailoot Ultra · OpenAI Chat

json yoki SSE

Barcha tayyor natijalarni saqlash

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

Har bir javob variantini va uning kontenti, vosita chaqiruvlari hamda fikrlash ma’lumotlarini saqlang. SSEda o‘zgarish qismlarini variant va vosita indeksi bo‘yicha yig‘ing, yakuniy tugash sababi va sarf ma’lumotlarini saqlang.

Asl javobni tekshirish kerak bo‘lsa

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

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

Taxalluslar

Model taxalluslari

Ailoot MiniMax-M2, minimax/minimax-m2
Ailoot Ultra MiniMax-M2, minimax/minimax-m2

Usul yo‘llari taxalluslari

/chat/completions

Chaqirish uchun tegishli ID lar

minimax/minimax-m2, minimax/minimax-m2, MiniMax-M2, MiniMax-M2

Ailoot Ultra · Anthropic Messages POST /v1/messages minimax/minimax-m2

minimax/minimax-m2

MiniMax /minimax/minimax-m2
Model ID
  • MiniMax-M2
  • minimax/minimax-m2

Boshlash

MiniMax M2 — MiniMax’ning jami 230B parametrdan 10B tasi faol bo‘lgan, boshidan oxirigacha dasturlash va agent ish jarayonlari uchun mo‘ljallangan matn modeli. Uning e’lon qilingan profilida mulohaza, funksiya vositalari, tuzilmali chiqishlar va katalog bo‘yicha 204,800 tokenli kontekst mavjud.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
json · sse

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · Anthropic Messages POST /v1/messages
Kirish
text
Chiqish
json, sse
Chiqish formati
JSON yoki SSE
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
minimax/minimax-m2
Standart—
MaydonmessagesTurmessage[]TalabMajburiyQiymatlar va cheklovlarStandart—
Maydonmessages[].roleTurstringTalabMajburiyQiymatlar va cheklovlar
user, assistant
Standart—
Maydonmessages[].contentTurstringTalabMajburiyQiymatlar va cheklovlarStandart—
Maydonmax_tokensTurintegerTalabMajburiyQiymatlar va cheklovlarStandart—
MaydonstreamTurbooleanTalabIxtiyoriyQiymatlar va cheklovlarStandartfalse
Kirish materiallari birikmalari

Matn · UTF-8 matni

messages[].content satri

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

Hayot sikli: synchronous_or_streamed

Tariflar

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

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

Ailoot tariflari

Sozlamalar bo‘yicha narxlar

SozlamaTariflar jadvaliTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.0006 / 1K token
Chiqish · Chiqish tokenlari $0.0024 / 1K token
Kirish · Keshdan o‘qish $0.00006 / 1K token
Kirish · Keshga yozish $0.00075 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. Summani band qilish va to‘lovni hisoblash tartibi →

So‘rov namunalari

Ailoot Ultra · Anthropic Messages

To‘liq minimal JSON so‘rov tanasicurl · O‘zingizning UTF-8 hujjatingizdan foydalaningPython · Natijani to‘liq saqlang
{
  "model": "minimax/minimax-m2",
  "max_tokens": 128,
  "messages": [
    {
      "role": "user",
      "content": "Summarize the incident report."
    }
  ],
  "stream": false
}
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json, os
from pathlib import Path

source_file = Path(os.environ['AILOOT_TEXT_FILE'])
if not source_file.is_file():
    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')
text = source_file.read_text(encoding='utf-8')
if not text.strip():
    raise SystemExit('Choose a document containing text')
payload = {'model': 'minimax/minimax-m2', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}
payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text
Path('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \
  -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path

result = json.loads(Path('result.json').read_text(encoding='utf-8'))
print(json.dumps(result, ensure_ascii=False, indent=2))
PY_RESULT
import json, os
from pathlib import Path

source_file = Path(os.environ['AILOOT_TEXT_FILE'])
if not source_file.is_file():
    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')
text = source_file.read_text(encoding='utf-8')
if not text.strip():
    raise SystemExit('Choose a document containing text')
payload = {'model': 'minimax/minimax-m2', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}
payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text
from urllib.request import Request, urlopen
from urllib.error import HTTPError

api_key = os.environ['AILOOT_API_KEY']
if not api_key.strip():
    raise SystemExit('Set AILOOT_API_KEY before making a request')
headers = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}
headers['x-api-key'] = api_key
request = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')
try:
    with urlopen(request, timeout=120) as response:
        raw = response.read()
        Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')
except HTTPError as error:
    Path('result.json').write_bytes(error.read())
    Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')
    raise
Path('result.json').write_bytes(raw)
import json
from pathlib import Path

result = json.loads(Path('result.json').read_text(encoding='utf-8'))
print(json.dumps(result, ensure_ascii=False, indent=2))

Natijalarni olish va saqlash

Ailoot Ultra · Anthropic Messages

JSON yoki SSE

Barcha tayyor natijalarni saqlash

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

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

Asl javobni tekshirish kerak bo‘lsa

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

Javob maydonlari
MaydonTur
MaydonidTurstring
MaydonmodelTurstring
Maydoncontent[]Turtext|thinking|tool_use|other content block
Maydonstop_reasonTurstring|null
MaydonusageTurobject
Xatolar va keyingi qadamlar
JavobShartKeyingi qadam
Javob400 · invalid_request_errorShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘riKeyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.

Taxalluslar

Model taxalluslari

Ailoot MiniMax-M2, minimax/minimax-m2
Ailoot Ultra MiniMax-M2, minimax/minimax-m2

Usul yo‘llari taxalluslari

/chat/completions

Chaqirish uchun tegishli ID lar

minimax/minimax-m2, minimax/minimax-m2, MiniMax-M2, MiniMax-M2

FAQ · MiniMax: MiniMax M2
Qanday kirish ma’lumotlari va natijalar mavjud?

POST /v1/messages · MiniMax-M2: text → text

POST /v1/responses · MiniMax-M2: text → text

POST /v1/chat/completions · MiniMax-M2: text → text

POST /v1/chat/completions · MiniMax-M2: text → text

POST /v1/messages · MiniMax-M2: text → json, sse

POST /v1/messages · minimax/minimax-m2: text → text

POST /v1/responses · minimax/minimax-m2: text → text

POST /v1/chat/completions · minimax/minimax-m2: text → text

POST /v1/chat/completions · minimax/minimax-m2: text → text

POST /v1/messages · minimax/minimax-m2: text → json, sse

So‘rov qaysi maydonlardan foydalanadi?

POST /v1/messages · MiniMax-M2: model, messages, system, max_tokens, stream, metadata

POST /v1/responses · MiniMax-M2: model, input, instructions, stream

POST /v1/chat/completions · MiniMax-M2: model, messages, stream

POST /v1/chat/completions · MiniMax-M2: model, messages, stream

POST /v1/messages · MiniMax-M2: model, messages, max_tokens, stream

POST /v1/messages · minimax/minimax-m2: model, messages, system, max_tokens, stream, metadata

POST /v1/responses · minimax/minimax-m2: model, input, instructions, stream

POST /v1/chat/completions · minimax/minimax-m2: model, messages, stream

POST /v1/chat/completions · minimax/minimax-m2: model, messages, stream, modalities, provider

POST /v1/messages · minimax/minimax-m2: model, messages, max_tokens, stream

Qanday cheklovlar amal qiladi?

POST /v1/messages · MiniMax-M2: ctx: 204800; max_out: 204800; messages.min_items: 1; max_tokens.minimum: 1

POST /v1/responses · MiniMax-M2: ctx: 204800; max_out: 204800

POST /v1/chat/completions · MiniMax-M2: ctx: 204800; max_out: 204800; messages.min_items: 1

POST /v1/chat/completions · MiniMax-M2: messages.min_items: 1

POST /v1/messages · minimax/minimax-m2: ctx: 204800; max_out: 204800; messages.min_items: 1; max_tokens.minimum: 1

POST /v1/responses · minimax/minimax-m2: ctx: 204800; max_out: 204800

POST /v1/chat/completions · minimax/minimax-m2: ctx: 204800; max_out: 204800; messages.min_items: 1

POST /v1/chat/completions · minimax/minimax-m2: messages.min_items: 1

Batafsil

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Tokenlarni chegirma bilan xarid qiling.

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Obuna
Muntazam so‘rovlaringiz uchun chegirmali tarif tanlang.
50% gacha promokod
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Tarixdagi xarajatlar
Har so‘rov uchun model, token va summa saqlanadi.

Obuna tanlash · Model narxlarini ko‘rish

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SaaS, ilova va botlar Ailoot ofertasi hamda litsenziya shartnomasiga muvofiq. Qayta sotishga reseller litsenziyasi so‘rang.

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Reseller litsenziyasi
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Saytdagi hujjatlar
Oferta, litsenziya shartnomasi va ma’lumotlar siyosati ulashdan oldin ochiq.

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