Source: https://ailoot.ai/models/ms/deepseek-ai-deepseek-v4-1-flash/
Canonical: https://ailoot.ai/models/ms/deepseek-ai-deepseek-v4-1-flash/
Language: ms

# DeepSeek v4.1 Flash

[Markdown](https://ailoot.ai/models/ms/deepseek-ai-deepseek-v4-1-flash/index.md) · [JSON](https://ailoot.ai/models/ms/deepseek-ai-deepseek-v4-1-flash/facts.json)

## [DeepSeek v4.1 Flash](https://ailoot.ai/models/ms/deepseek-ai-deepseek-v4-1-flash/)

DeepSeek V4.1 Flash ialah model multimodal untuk analisis teks dan imej, pengaturcaraan serta kerja ejen dengan konteks panjang.

- [**Ailoot · Anthropic Messages** `POST /v1/messages` `deepseek-ai/deepseek-v4.1-flash`](https://ailoot.ai/models/ms/deepseek-ai-deepseek-v4-1-flash/#method-0)
- [**Ailoot · OpenAI Chat** `POST /v1/chat/completions` `deepseek-ai/deepseek-v4.1-flash`](https://ailoot.ai/models/ms/deepseek-ai-deepseek-v4-1-flash/#method-1)

Tentang model

Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.

#### Pemprosesan teks dan imej secara bersepadu

DeepSeek V4.1 Flash ialah model yang dibina sebagai model multimodal sejak awal: pengekod visual menukarkan imej kepada perwakilan yang diproses bersama teks. Hasilnya berupa teks. Pemahaman visual, pengaturcaraan dan tugasan ejen disediakan; konteks mencapai sejuta token.

#### Seni bina untuk input besar

Rangkaian utama mengandungi 552 bilion parameter. Pengekod dan penyahkod kausal masing-masing mempunyai 20 lapisan. Semasa pemprosesan input, 8 bilion parameter aktif bagi setiap token, manakala semasa penjanaan, 16 bilion. Pembahagian ini direka untuk tugasan yang bahan inputnya jauh lebih besar daripada respons.

Perhatian jarang termampat menggunakan semula data antara lapisan dan mengehadkan pencarian berulang bahagian konteks yang penting. Keadaan perhatian setempat yang tiada dibina semula daripada bahagian terkini. Memori bersyarat Engram mengandungi 196 bilion parameter dan mengakses rekod melalui carian jarang berdasarkan token. Ini ialah blok memori berasingan, bukannya nama baharu bagi saiz rangkaian utama. Setiap lapisan pakar mempunyai satu pakar bersama dan 384 pakar yang boleh dirutekan; enam pakar yang dirutekan dipilih bagi satu token. Sambungan antara lapisan yang diubah serta DSpark untuk penyahkodan spekulatif turut digunakan.

#### Latihan dan persekitaran ejen

Model dilatih dari awal menggunakan korpus multimodal sebanyak 45 trilion token. Persediaan seterusnya mengikut urutan: latihan berdasarkan contoh, pembelajaran pengukuhan, kemudian penyulingan berdasarkan urutan model sendiri.

DeepSeek memperluas data tugasan sintetik, persekitaran kerja dan trajektori ejen. Persediaan menghubungkan tugasan itu sendiri, persekitaran yang tersedia dan cara memeriksa hasil; data ditapis, dibersihkan daripada pendua dan dipilih mengikut kerumitan. Ini ialah struktur latihan, bukannya ketersediaan automatik alat luaran ketika model diakses.

#### Kedalaman analisis yang boleh dikawal

Penaakulan dikawal oleh nombor bulat dari 1 hingga 100. Skala berterusan ini menentukan usaha analisis; penaakulan mendalam mungkin memerlukan lebih banyak masa dan token.

#### Penerusan dialog dan tindakan

Format model menyediakan dialog berbilang giliran, panggilan alat, penaakulan tersimpan dan teks yang diselang-seli dengan imej. Alat dilaksanakan oleh aplikasi; hasil yang diterima menjadi bahan bagi langkah seterusnya.

Respons kekal berupa teks. Keupayaan visual memerlukan kaedah input yang sepadan. Saiz konteks dan panjang penjanaan yang disyorkan tidak menetapkan saiz respons maksimum yang sejagat.

Tugas praktikal

### Tugas praktikal

Contoh teknikal dalam bahasa Inggeris

```
Trace a data-loss bug from a text log

Use this log and code sketch to identify the likely lost-update mechanism: A reads stock=5; B reads stock=5; A writes 4; B writes 4. Both requests call read(), subtract one, then write(). Explain the failure, propose one atomic update strategy and a concurrent test. Do not assume a specific database or execute anything.
```

#### Hasil yang dijangka

```
A causal diagnosis, database-neutral pseudocode for an atomic decrement and a concurrent-test scenario with the expected final count.
```

#### Semak hasil

```
The test expects stock 3 after two successful decrements from 5, with a stated insufficient-stock rule.
```

**Ailoot · Anthropic Messages** `POST /v1/messages` `deepseek-ai/deepseek-v4.1-flash`

[`deepseek-ai/deepseek-v4.1-flash`](https://ailoot.ai/models/ms/#m=deepseek-ai%2Fdeepseek-v4.1-flash&route=Core&caller=deepseek-ai%2Fdeepseek-v4.1-flash&fmt=messages)

DeepSeek /`deepseek-ai/deepseek-v4.1-flash`

### Pengenalan

DeepSeek V4.1 Flash ialah model multimodal untuk analisis teks dan imej, pengaturcaraan serta kerja ejen dengan konteks panjang.

Input yang diterima

Teks

Hasil

Teks

### Tersedia di Ailoot · input & kaedah

**Ailoot · Anthropic Messages** `POST /v1/messages`

Input

`text`

Output

`text`

Konteks

1,048,576 tokens

Output maksimum

32,768 tokens

Format output

json atau SSE

| Medan | Jenis | Keperluan | Nilai & had | Lalai |
| --- | --- | --- | --- | --- |
| Medan`model` | Jenis`string` | KeperluanDiperlukan | Nilai & had deepseek-ai/deepseek-v4.1-flash | Lalai— |
| Medan`messages` | Jenis`message[]` | KeperluanDiperlukan | Nilai & had Item minimum: 1 | Lalai— |
| Medan`messages[].role` | Jenis`string` | KeperluanDiperlukan | Nilai & had user, assistant | Lalai— |
| Medan`messages[].content` | Jenis`string\|content_block[]` | KeperluanDiperlukan | Nilai & had | Lalai— |
| Medan`system` | Jenis`string\|content_block[]` | KeperluanPilihan | Nilai & had | Lalai— |
| Medan`max_tokens` | Jenis`integer` | KeperluanDiperlukan | Nilai & had Minimum: 1 Had terpakai pada: had maksimum tepat bagi model/kad yang dipilih | Lalai— |
| Medan`stream` | Jenis`boolean` | KeperluanPilihan | Nilai & had | Lalaifalse |
| Medan`temperature` | Jenis`number` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`top_p` | Jenis`number` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`stop_sequences` | Jenis`string[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`tools` | Jenis`tool[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`tools[].name` | Jenis`string` | KeperluanPilihan | Nilai & had Terpakai apabila: alat dibekalkan | Lalai— |
| Medan`tools[].description` | Jenis`string` | KeperluanPilihan | Nilai & had | Lalai— |
| Medan`tools[].input_schema` | Jenis`JSON_schema` | KeperluanPilihan | Nilai & had | Lalai— |
| Medan`tool_choice` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`thinking.type` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`thinking.budget_tokens` | Jenis`integer` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`output_config.effort` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`metadata` | Jenis`object` | KeperluanPilihan | Nilai & had | Lalai— |
| Medan`cache_control` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |

Gabungan bahan input

**Teks** · Teks UTF8

`messages[].content atau blok type=text`

Parameter kaedah dan kitar hayat

| Parameter | Kontrak |
| --- | --- |
| Parameter`material_types` | Kontraktext |
| Parameter`required` | Kontrakmodel, messages, max\_tokens |
| Parameter`optional_controls` | Kontraknull |
| Parameter`content_type` | Kontrakapplication/json |
| Parameter`text_file_material` | Kontrakkandungan UTF-8 sebenar dimasukkan sebagai teks; ID fail atau alamat URL (URLs) memerlukan sokongan tepat yang berasingan |

Kitar hayat: `exact retained published method`

Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan.

### Pelan harga

#### Kadar kaedah Ailoot

Input

USD 0.0003 */ 1K*

Output

USD 0.0012 */ 1K*

Bacaan cache

USD 0.000006 */ 1K*

Penulisan cache

USD 0.0003 */ 1K*

Penulisan cache, 5 minit

USD 0.0003 */ 1K tokens*

Ailoot merizabkan anggaran amaun sebelum permintaan. Selepas hasil diterima, caj akhir diselesaikan berdasarkan penggunaan terukur. Penyelesaian caj penstriman menggunakan rekod penggunaan akhir. [Cara rizab dan penyelesaian caj berfungsi →](https://ailoot.ai/docs/ms/billing/reserve/)

### Contoh permintaan

#### Ailoot · Anthropic Messages

Contoh teksPython SDKNode.js SDKPenstriman Python

```bash
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": "deepseek-ai/deepseek-v4.1-flash", "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
```

```python
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="deepseek-ai/deepseek-v4.1-flash",
    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"))
```

```js
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: "deepseek-ai/deepseek-v4.1-flash",
  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);
```

```python
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='deepseek-ai/deepseek-v4.1-flash',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")
```

### Terima dan simpan hasil

#### Ailoot · Anthropic Messages

json atau SSE

##### Simpan semua hasil yang selesai

Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.

Kekalkan semua blok kandungan, tandatangan penaakulan dan ID penggunaan alat sebenar. Gabungkan semua delta blok SSE; blok teks pertama bukan keseluruhan output.

##### Jika respons asal perlu disemak

Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.

Medan respons

| Medan | Jenis |
| --- | --- |
| Medan`id` | Jenisstring |
| Medan`model` | Jenisstring |
| Medan`content[]` | Jenistext\|thinking\|tool\_use\|other content block |
| Medan`stop_reason` | Jenisstring\|null |
| Medan`usage` | Jenisobject |

Ralat dan langkah seterusnya

| Respons | Syarat | Langkah seterusnya |
| --- | --- | --- |
| Respons400 · `invalid_request_error` | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. |

### Alias

#### Alias model

**Ailoot** [`deepseek-ai/deepseek-v4.1-flash`](https://ailoot.ai/models/ms/#m=deepseek-ai%2Fdeepseek-v4.1-flash&route=Core&caller=deepseek-ai%2Fdeepseek-v4.1-flash&fmt=messages)

#### Alias laluan kaedah

Gunakan laluan kaedah yang dipaparkan di atas.

#### ID pemanggil berkaitan

[`deepseek-ai/deepseek-v4.1-flash`](https://ailoot.ai/models/ms/#m=deepseek-ai%2Fdeepseek-v4.1-flash&route=Core&caller=deepseek-ai%2Fdeepseek-v4.1-flash&fmt=messages)

**Ailoot · OpenAI Chat** `POST /v1/chat/completions` `deepseek-ai/deepseek-v4.1-flash`

[`deepseek-ai/deepseek-v4.1-flash`](https://ailoot.ai/models/ms/#m=deepseek-ai%2Fdeepseek-v4.1-flash&route=Core&caller=deepseek-ai%2Fdeepseek-v4.1-flash&fmt=chat)

DeepSeek /`deepseek-ai/deepseek-v4.1-flash`

### Pengenalan

DeepSeek V4.1 Flash ialah model multimodal untuk analisis teks dan imej, pengaturcaraan serta kerja ejen dengan konteks panjang.

Input yang diterima

Teks

Hasil

Teks

### Tersedia di Ailoot · input & kaedah

**Ailoot · OpenAI Chat** `POST /v1/chat/completions`

Input

`text`

Output

`text`

Konteks

4,096 tokens

Output maksimum

1,024 tokens

Format output

json atau SSE

| Medan | Jenis | Keperluan | Nilai & had | Lalai |
| --- | --- | --- | --- | --- |
| Medan`model` | Jenis`string` | KeperluanDiperlukan | Nilai & had deepseek-ai/deepseek-v4.1-flash | Lalai— |
| Medan`messages` | Jenis`message[]` | KeperluanDiperlukan | Nilai & had Item minimum: 1 | Lalai— |
| Medan`messages[].role` | Jenis`string` | KeperluanDiperlukan | Nilai & had Contoh: system, user, assistant, tool, function Syarat: Peranan ini diwakili secara eksplisit oleh jenis keserasian dalam kod sumber; set peranan model yang tepat kekal khusus untuk setiap model. Bergantung pada model | Lalai— |
| Medan`messages[].content` | Jenis`string\|content_part[]\|null` | KeperluanPilihan | Nilai & had | Lalai— |
| Medan`messages[].tool_calls` | Jenis`tool_call[]` | KeperluanPilihan | Nilai & had | Lalai— |
| Medan`messages[].tool_call_id` | Jenis`string` | KeperluanPilihan | Nilai & had Terpakai apabila: role=tool; gunakan ID sebenar yang dipulangkan | Lalai— |
| Medan`messages[].name` | Jenis`string` | KeperluanPilihan | Nilai & had | Lalai— |
| Medan`messages[].reasoning_content` | Jenis`string` | KeperluanPilihan | Nilai & had Terpakai apabila: Hantar semula penaakulan sebenar yang dipulangkan apabila model ini menggunakannya | Lalai— |
| Medan`messages[].reasoning_details` | Jenis`object\|array` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`max_tokens` | Jenis`integer` | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— |
| Medan`max_completion_tokens` | Jenis`integer` | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— |
| Medan`stream` | Jenis`boolean` | KeperluanPilihan | Nilai & had | Lalaifalse |
| Medan`stream_options.include_usage` | Jenis`boolean` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`temperature` | Jenis`number` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`top_p` | Jenis`number` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`stop` | Jenis`string\|string[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`tools` | Jenis`function_tool[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`tools[].type` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`tools[].function.name` | Jenis`string` | KeperluanDiperlukan | Nilai & had Terpakai apabila: alat fungsi dibekalkan | Lalai— |
| Medan`tools[].function.description` | Jenis`string` | KeperluanPilihan | Nilai & had | Lalai— |
| Medan`tools[].function.parameters` | Jenis`JSON_schema` | KeperluanPilihan | Nilai & had | Lalai— |
| Medan`tool_choice` | Jenis`string\|object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`parallel_tool_calls` | Jenis`boolean` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`response_format` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`reasoning_effort` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`reasoning` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`service_tier` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`instructions` | Jenis`string` | KeperluanPilihan | Nilai & had Medan keserasian | Lalai— |
| Medan`functions` | Jenis`function[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`function_call` | Jenis`string\|object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
| Medan`prompt_cache_options` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |

Gabungan bahan input

**Teks** · Teks UTF8

`messages[].content`

Parameter kaedah dan kitar hayat

| Parameter | Kontrak |
| --- | --- |
| Parameter`material_types` | Kontraktext |
| Parameter`required` | Kontrakmodel, messages |
| Parameter`optional_controls` | Kontraknull |
| Parameter`content_type` | Kontrakapplication/json |
| Parameter`text_file_material` | Kontrakkandungan UTF-8 sebenar dimasukkan sebagai teks; ID fail atau alamat URL (URLs) memerlukan sokongan tepat yang berasingan |

Kitar hayat: `exact retained published method`

Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan.

### Pelan harga

#### Kadar kaedah Ailoot

Input

USD 0.0003 */ 1K*

Output

USD 0.0012 */ 1K*

Bacaan cache

USD 0.000006 */ 1K*

Penulisan cache

USD 0.0003 */ 1K*

Penulisan cache, 5 minit

USD 0.0003 */ 1K tokens*

Ailoot merizabkan anggaran amaun sebelum permintaan. Selepas hasil diterima, caj akhir diselesaikan berdasarkan penggunaan terukur. Penyelesaian caj penstriman menggunakan rekod penggunaan akhir. [Cara rizab dan penyelesaian caj berfungsi →](https://ailoot.ai/docs/ms/billing/reserve/)

### Contoh permintaan

#### Ailoot · OpenAI Chat

Contoh teksPython SDKNode.js SDKPenstriman Python

```bash
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": "deepseek-ai/deepseek-v4.1-flash", "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
```

```python
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="deepseek-ai/deepseek-v4.1-flash",
    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"))
```

```js
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: "deepseek-ai/deepseek-v4.1-flash",
  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);
```

```python
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='deepseek-ai/deepseek-v4.1-flash',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")
```

### Terima dan simpan hasil

#### Ailoot · OpenAI Chat

json atau SSE

##### Simpan semua hasil yang selesai

Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.

Kekalkan setiap pilihan jawapan bersama kandungan, panggilan alat dan penaakulannya. Dalam SSE, gabungkan delta mengikut indeks pilihan jawapan/alat dan kekalkan sebab penamatan akhir serta data penggunaan.

##### Jika respons asal perlu disemak

Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.

Medan respons

| Medan | Jenis |
| --- | --- |
| Medan`choices[].index` | Jenisinteger |
| Medan`choices[].message.content` | Jenisstring\|null |
| Medan`choices[].message.tool_calls` | Jenistool\_call\[\] |
| Medan`choices[].message.reasoning_content` | Jenisstring |
| Medan`choices[].message.reasoning_details` | Jenisobject\|array |
| Medan`choices[].finish_reason` | Jenisstring\|null |
| Medan`usage` | Jenisobject |

Ralat dan langkah seterusnya

| Respons | Syarat | Langkah seterusnya |
| --- | --- | --- |
| Respons400 · `invalid_request_error` | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. |

### Alias

#### Alias model

**Ailoot** [`deepseek-ai/deepseek-v4.1-flash`](https://ailoot.ai/models/ms/#m=deepseek-ai%2Fdeepseek-v4.1-flash&route=Core&caller=deepseek-ai%2Fdeepseek-v4.1-flash&fmt=chat)

#### Alias laluan kaedah

Gunakan laluan kaedah yang dipaparkan di atas.

#### ID pemanggil berkaitan

[`deepseek-ai/deepseek-v4.1-flash`](https://ailoot.ai/models/ms/#m=deepseek-ai%2Fdeepseek-v4.1-flash&route=Core&caller=deepseek-ai%2Fdeepseek-v4.1-flash&fmt=chat)

FAQ · deepseek-ai/deepseek-v4.1-flash

Apakah input dan hasil yang tersedia?

`POST /v1/messages` · `deepseek-ai/deepseek-v4.1-flash`: `text` → `text` `POST /v1/chat/completions` · `deepseek-ai/deepseek-v4.1-flash`: `text` → `text`

Apakah medan yang digunakan oleh permintaan?

`POST /v1/messages` · `deepseek-ai/deepseek-v4.1-flash`: `model`, `messages`, `system`, `max_tokens`, `stream`, `metadata` `POST /v1/chat/completions` · `deepseek-ai/deepseek-v4.1-flash`: `model`, `messages`, `stream`

Apakah had yang terpakai?

`POST /v1/messages` · `deepseek-ai/deepseek-v4.1-flash`: `ctx`: `1048576`; `max_out`: `32768`; `messages.min_items`: `1`; `max_tokens.minimum`: `1` `POST /v1/chat/completions` · `deepseek-ai/deepseek-v4.1-flash`: `ctx`: `4096`; `max_out`: `1024`; `messages.min_items`: `1`

Sambungkan Ailoot kepada Claude Code, OpenCode, Hermes atau aplikasi anda.

## Syarikat dan portfolio projeknya

Syarikat dan portfolio projeknya: jutaan pelanggan; jualan bernilai berbilion setiap bulan.

## Beli token dengan diskaun.

Diskaun langganan untuk penggunaan berkala. Kod promosi — sehingga 50% bagi pembelian token.

Langganan

Pilih pelan berdiskaun untuk permintaan berkala anda.

Kod promosi sehingga 50%

Masukkan kod semasa pembelian. Harga dikira semula mengikut promosi semasa.

Perbelanjaan dalam sejarah

Model, token dan jumlah disimpan bagi setiap permintaan.

[Pilih langganan](https://ailoot.ai/docs/ms/billing/subscriptions/) · [Lihat harga model](https://ailoot.ai/models/ms/)

## Sambungkan API kepada produk yang anda jual.

SaaS, aplikasi dan bot — di bawah tawaran rasmi serta perjanjian lesen Ailoot. Untuk jualan semula, mohon lesen penjual semula.

Aplikasi komersial

Tambahkan penjanaan teks, imej, video dan audio pada ciri berbayar perkhidmatan anda.

Lesen penjual semula

Hantar keterangan projek dan susunan akses. Minta terma penggunaan serta jualan semula secara bertulis.

Dokumen pada laman

Tawaran, perjanjian lesen dan dasar pemprosesan data tersedia sebelum sambungan.

[Mohon lesen penjual semula](mailto:support@ailoot.ai) · [Tawaran dan perjanjian lesen](https://ailoot.ai/legal/offer?lang=ms)

## Ailoot · Syarikat dan portfolio projeknya





Ailoot berteraskan pengalaman operasi yang mantap, pangkalan pelanggan yang besar, perkhidmatan yang stabil dan pemantauan KPI berterusan.





Syarikat dan portfolio projeknya: jutaan pelanggan; jualan bernilai berbilion setiap bulan.





Ailoot menggunakan pengalaman operasi ini untuk akses API kepada ciri AI dalam produk pelanggan, ejen, aplikasi dan automasi.