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API DOCSPRICING
TEMPLATES
doubaoText Chat

Doubao Seed 2.1 Turbo API: high-throughput Chinese and business text workflows

Test Doubao Seed 2.1 Turbo for Chinese chat, content operations, classification, extraction, and cost-sensitive API workloads.

InputAIReiter $0.43 per 1M tokensOutputAIReiter $2.14 per 1M tokensCache readAIReiter $0.09 per 1M tokens
Run with API
PlaygroundReadmeAPI

INPUT

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Install the official Anthropic client — AIReiter speaks the same protocol, so only the base URL changes:

npm install @anthropic-ai/sdk

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: process.env.AIREITER_API_KEY,
  baseURL: "https://aireiter.com/api",
});

Run doubao-seed-2.1-turbo:

const message = await client.messages.create({
    "model": "doubao-seed-2.1-turbo",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": "把这段客服对话总结成问题类型、用户情绪和下一步处理建议。"
      }
    ],
    "temperature": 0.7,
    "top_p": 1
  });

console.log(message.content);

Stream the response instead:

const stream = client.messages.stream({
    "model": "doubao-seed-2.1-turbo",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": "把这段客服对话总结成问题类型、用户情绪和下一步处理建议。"
      }
    ],
    "temperature": 0.7,
    "top_p": 1
  });

stream.on("text", (text) => process.stdout.write(text));
const message = await stream.finalMessage();

Install the official Anthropic client — AIReiter speaks the same protocol, so only the base URL changes:

pip install anthropic

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

import os
import anthropic

client = anthropic.Anthropic(
    api_key=os.environ["AIREITER_API_KEY"],
    base_url="https://aireiter.com/api",
)

Run doubao-seed-2.1-turbo:

message = client.messages.create(
      model = "doubao-seed-2.1-turbo",
      max_tokens = 1024,
      messages = [
        {
          role = "user",
          content = "把这段客服对话总结成问题类型、用户情绪和下一步处理建议。"
        }
      ],
      temperature = 0.7,
      top_p = 1
)

print(message.content)

Stream the response instead:

with client.messages.stream(
      model = "doubao-seed-2.1-turbo",
      max_tokens = 1024,
      messages = [
        {
          role = "user",
          content = "把这段客服对话总结成问题类型、用户情绪和下一步处理建议。"
        }
      ],
      temperature = 0.7,
      top_p = 1
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Run doubao-seed-2.1-turbo against AIReiter's API:

curl -s -X POST \
  -H "x-api-key: $AIREITER_API_KEY" \
  -H "Content-Type: application/json" \
  "https://aireiter.com/api/v1/messages" \
  -d '{
  "model": "doubao-seed-2.1-turbo",
  "max_tokens": 1024,
  "messages": [
    {
      "role": "user",
      "content": "把这段客服对话总结成问题类型、用户情绪和下一步处理建议。"
    }
  ],
  "temperature": 0.7,
  "top_p": 1
}'

Add "stream": true to the body to receive the response as server-sent events.

OUTPUT

Example

A rate limit caps how many requests an API accepts from you in a given window. Once you exceed it, the server stops doing work for you and answers 429 Too Many Requests instead.

Handling 429

  1. Read the Retry-After response header. When present it tells you exactly how long to wait, in seconds.
  2. When it is absent, back off exponentially with jitter so retries from many clients do not line up.
  3. Cap the number of retries, then surface the failure instead of looping forever.
async function withRetry(request, maxRetries = 4) {
  for (let attempt = 0; ; attempt++) {
    const response = await request();
    if (response.status !== 429 || attempt === maxRetries) return response;
    const retryAfter = Number(response.headers.get("retry-after"));
    const backoff = Number.isFinite(retryAfter) ? retryAfter * 1000 : 2 ** attempt * 500 + Math.random() * 250;
    await new Promise((resolve) => setTimeout(resolve, backoff));
  }
}

Treat the limit as a budget you plan around, not an error you retry your way out of: batch requests where you can, cache repeated reads, and spread bulk work over time.

{
  "model": "doubao-seed-2.1-turbo",
  "input": {
    "model": "doubao-seed-2.1-turbo",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": "把这段客服对话总结成问题类型、用户情绪和下一步处理建议。"
      }
    ],
    "temperature": 0.7,
    "top_p": 1
  },
  "output": "A rate limit caps how many requests an API accepts from you in a given window. Once you exceed it, the server stops doing work for you and answers **429 Too Many Requests** instead.\n\n## Handling 429\n\n1. Read the `Retry-After` response header. When present it tells you exactly how long to wait, in seconds.\n2. When it is absent, back off exponentially with jitter so retries from many clients do not line up.\n3. Cap the number of retries, then surface the failure instead of looping forever.\n\n```js\nasync function withRetry(request, maxRetries = 4) {\n  for (let attempt = 0; ; attempt++) {\n    const response = await request();\n    if (response.status !== 429 || attempt === maxRetries) return response;\n    const retryAfter = Number(response.headers.get(\"retry-after\"));\n    const backoff = Number.isFinite(retryAfter) ? retryAfter * 1000 : 2 ** attempt * 500 + Math.random() * 250;\n    await new Promise((resolve) => setTimeout(resolve, backoff));\n  }\n}\n```\n\nTreat the limit as a budget you plan around, not an error you retry your way out of: batch requests where you can, cache repeated reads, and spread bulk work over time.",
  "metrics": {
    "input_tokens": 26,
    "output_tokens": 214,
    "generated_in_seconds": 4.1
  },
  "example": true
}
Generated in
4.1 seconds
Input tokens
26
Output tokens
214
Tokens per second
52.20 tokens / second
Time to first token
-

Model details

Use the same model key in Playground, API requests, and internal workflows.

Model ID
doubao-seed-2.1-turbo
Provider
doubao
Protocol
Anthropic Messages
Context window
262,144 tokens
Max output
-
Input tokens
42.8571 credits / 1M tokens
Output tokens
214.2857 credits / 1M tokens
Cache read
8.5714 credits / 1M tokens
Cache write
-

Fast Chinese-language production traffic

Doubao Seed 2.1 Turbo is strongest as a practical business model: responsive Chinese conversations, content operations, classification, and customer-support automation.

Doubao Seed 2.1 Turbo API cover

Should you choose Doubao Seed 2.1 Turbo?

Position it as the default route for Chinese high-throughput workflows, then escalate harder reasoning or code-heavy prompts elsewhere.

Choose it when

You need fast Chinese chat, customer support summaries, content classification, extraction, or repeated business text automation.

Use another model when

The task depends on deep code reasoning, long repository context, or careful multi-step research.

Public API protocol

Call POST https://aireiter.com/api/v1/messages with model "doubao-seed-2.1-turbo". Streaming is supported through the same Messages-compatible endpoint.

Token and cache usage

Doubao pricing includes input, cache-read, and output token fields. Treat provider cache storage/hour pricing separately from API cache-read settlement.

Doubao Seed 2.1 Turbo production workloads

Best for Chinese product traffic where speed, cost, and clear output structure matter.
01

Customer support operations

Summarize conversations, classify issues, identify sentiment, and produce next-action suggestions.

02

Chinese content workflows

Draft, rewrite, tag, and normalize business content for product and marketing teams.

03

Classification and extraction

Turn repeated inputs into labels, fields, and short operational summaries.

04

Assistant backends

Power lightweight assistants that need quick, consistent Chinese answers.

How Doubao Seed 2.1 Turbo fits your model stack

Do not route every request to the newest model. Pick the cheapest model that still passes your quality bar, then reserve deeper models for failures or high-risk tasks.

For fast batches

Use Doubao for Chinese high-volume batches; use DeepSeek V4 Flash for more technical batches.

For deeper reasoning

Use GLM 5.2 or DeepSeek V4 Pro when reasoning depth matters more than throughput.

For long context

Use Kimi K2.7 Code or MiniMax M3 when long context, documents, or repository state dominate.

For production rollout

Validate business terminology and tone on real Chinese prompts before moving large traffic.

Doubao Seed 2.1 Turbo API questions

Questions developers usually check before moving a text model from playground testing to production API traffic.

/ 01

What model ID should I send for Doubao Seed 2.1 Turbo?

Use "doubao-seed-2.1-turbo" in the API request body. The internal DB key is only used by AIReiter routing.

/ 02

Which endpoint should Doubao Seed 2.1 Turbo use?

Use POST https://aireiter.com/api/v1/messages for public API calls. Keep x-api-key / Authorization authentication consistent with your AIReiter API key setup.

/ 03

Does Doubao Seed 2.1 Turbo support streaming?

Yes. Send stream=true and read server-sent events until the message completes. Test non-streaming first when debugging authentication or model ID issues.

/ 04

How do I confirm token and cache billing for Doubao Seed 2.1 Turbo?

Check the usage object returned by the API. Input, output, and cache-read token fields are the source of truth for settlement; a repeated prompt alone does not prove a cache hit.

/ 05

Should I always set max_tokens for Doubao Seed 2.1 Turbo?

For chat and extraction, a moderate max_tokens limit is usually enough. Raise it for long summaries or multi-step Chinese reports.

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