Doubao Pro API Guide

Why Doubao Pro for Speed?

Doubao (from ByteDance) is optimized for low-latency inference. While DeepSeek and GLM focus on reasoning quality, Doubao prioritizes speed:

• Fastest TTFT - First token arrives in ~100ms from Asia
• Highest throughput - 60+ tokens per second
• Efficient architecture - Optimized for chat and quick responses
• Lowest cost - Among the cheapest per 1K tokens

When to Use Doubao vs Other Models

Scenario	Best Model	Latency
Live chat assistant	Doubao Pro	~100ms TTFT
Real-time translation	Doubao Pro	~100ms TTFT
Quick autocomplete	Doubao Pro	~100ms TTFT
Complex coding	DeepSeek V4	~200ms TTFT
Long document analysis	Kimi K2.6	~300ms TTFT
Chinese creative writing	GLM-5.1	~150ms TTFT

API Setup

from openai import OpenAI

client = OpenAI(
    base_url="https://tokenease.io/v1",
    api_key="your_tokenease_api_key"
)

# Use Doubao for fast responses
response = client.chat.completions.create(
    model="doubao",
    messages=[{"role": "user", "content": "Quick hello!"}],
    max_tokens=150  # Keep responses short for speed
)

Latency Benchmarks

Measured from Singapore (representative of Asia-Pacific users):

Model	TTFT	Tokens/Second	Total (500 tokens)
Doubao Pro	95ms	62 t/s	8.0s
DeepSeek V4	180ms	45 t/s	11.1s
GLM-5.1	150ms	50 t/s	10.0s
Qwen-Plus	160ms	48 t/s	10.4s
Kimi K2.6	280ms	35 t/s	14.3s

Real-Time Use Cases

1. Live Chat Widget

def chat_response(user_message):
    """Ultra-fast chat response for website widget"""
    response = client.chat.completions.create(
        model="doubao",
        messages=[
            {"role": "system", "content": "You are a helpful support assistant. Keep responses under 100 words."},
            {"role": "user", "content": user_message}
        ],
        max_tokens=200,
        temperature=0.5  # Lower = more predictable/faster
    )
    return response.choices[0].message.content

# Average response time: ~2 seconds for a complete answer

2. Real-Time Translation

def translate_stream(text, target_lang):
    """Stream translation for real-time apps"""
    stream = client.chat.completions.create(
        model="doubao",
        messages=[
            {"role": "system", "content": f"Translate to {target_lang}. Output only the translation."},
            {"role": "user", "content": text}
        ],
        stream=True,
        max_tokens=500
    )
    
    for chunk in stream:
        if chunk.choices[0].delta.content:
            yield chunk.choices[0].delta.content

# Usage in web app
for word in translate_stream("Hello world", "Chinese"):
    print(word, end="", flush=True)  # Appears in real-time

3. Command-Line AI Assistant

#!/usr/bin/env python3
"""Fast CLI assistant using Doubao"""
from openai import OpenAI
import sys

client = OpenAI(base_url="https://tokenease.io/v1", api_key="your_key")

query = " ".join(sys.argv[1:])

response = client.chat.completions.create(
    model="doubao",
    messages=[
        {"role": "system", "content": "Give concise, accurate answers."},
        {"role": "user", "content": query}
    ],
    max_tokens=300
)

print(response.choices[0].message.content)

4. Autocomplete for Forms

def autocomplete_suggestion(partial_text, context=""):
    """Fast autocomplete for input fields"""
    response = client.chat.completions.create(
        model="doubao",
        messages=[
            {"role": "system", "content": "Complete the user's text naturally. Max 10 words."},
            {"role": "user", "content": f"Context: {context}\nText: {partial_text}"}
        ],
        max_tokens=50,
        temperature=0.3
    )
    return partial_text + response.choices[0].message.content

# Usage
suggestion = autocomplete_suggestion(
    "I want to learn about",
    context="User is browsing programming tutorials"
)
# Returns: "I want to learn about Python programming..."

Pricing

Model	Input/M	Output/M
Doubao Pro	$0.20	$0.60
Doubao Lite	$0.10	$0.30

Doubao is not just the fastest - it's also among the cheapest. For high-volume, low-complexity tasks, it's the optimal choice.