DEV Community: q409605362

I Built an API Gateway That Lets Global Developers Access Chinese LLMs

q409605362 — Mon, 15 Jun 2026 08:52:53 +0000

Hey Dev.to!

I just launched Asiatek AI - an API gateway that gives developers worldwide access to top Chinese large language models through a single, OpenAI-compatible API endpoint.

The Problem

Chinese LLMs like Qwen, DeepSeek, GLM, Moonshot, Baichuan, Stepfun, and MiniMax are incredibly powerful. But accessing them from outside China is painful:

Domestic phone number verification
Chinese payment methods only
Separate APIs for each provider
Different request formats

The Solution

curl https://api.asiatekai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d x27{
    "model": "qwen-plus",
    "messages": [{"role": "user", "content": "Hello!"}]
  }x27

OpenAI-compatible - switch base URL, it just works.

Available Models (15+)

Provider	Models
Alibaba	Qwen-Plus, Qwen-Turbo, Qwen-Max
DeepSeek	DeepSeek-V3, DeepSeek-Reasoner
Zhipu	GLM-4-Flash
Moonshot	Moonshot-v1-8k
Baichuan	Baichuan2-Turbo
Stepfun	Step-1-8K
MiniMax	abab6.5s-chat

Quick Start with Python

from openai import OpenAI

client = OpenAI(
    base_url="https://api.asiatekai.com/v1",
    api_key="your-api-key"
)

response = client.chat.completions.create(
    model="qwen-plus",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

No SDK changes. No format conversion. If it works with OpenAI, it works with Asiatek AI.

Pricing

Models start from $0.06 per million tokens. Check all pricing at asiatekai.com.

$0.5 free credit on signup to get you started.

Why I Built This

Chinese LLMs are world-class but trapped behind China tech ecosystem. I wanted to bridge that gap for international developers.

What Next

More models (vision, audio, embeddings)
Expanding server regions
Better documentation

Feedback welcome! Try it at asiatekai.com

Email: service@asiatekai.com

DeepSeek Reasoner vs OpenAI o1-mini: Real-World Benchmark for API Developers

q409605362 — Tue, 09 Jun 2026 16:44:37 +0000

Everyone's talking about reasoning models, but most benchmarks are academic. I wanted to know: which reasoning model should I actually use in production, and how much does it cost?

I tested DeepSeek Reasoner (via Asiatek AI) against OpenAI o1-mini on 5 real-world tasks that developers actually encounter. No MMLU, no competition math — just practical stuff.

The Setup

DeepSeek Reasoner via Asiatek AI (Singapore gateway):

Input: $0.66/M tokens, Output: $2.63/M tokens
128K context window
Endpoint: https://api.asiatekai.com/v1

OpenAI o1-mini:

Input: $3.00/M tokens, Output: $12.00/M tokens
128K context window

Both called through the OpenAI Python SDK (yes, Asiatek AI is OpenAI-compatible, so same code):

from openai import OpenAI

# DeepSeek Reasoner
ds_client = OpenAI(
    api_key="your_asiatek_key",
    base_url="https://api.asiatekai.com/v1"
)

# OpenAI o1-mini
oai_client = OpenAI(api_key="your_openai_key")

Price difference: DeepSeek Reasoner is 4.5x cheaper on input, 4.6x cheaper on output.

Now let's see if the quality holds up.

Task 1: Math Problem Solving

Prompt: "A swimming pool has two pipes. Pipe A fills it in 4 hours, Pipe B fills it in 6 hours. If both are opened, but there's a leak that drains 1/12 of the pool per hour, how long to fill the pool?"

Model	Answer Correct?	Reasoning Steps	Total Tokens	Cost
DeepSeek Reasoner	✅ Yes (3.6 hours)	Clear, step-by-step	~1,200	$0.004
OpenAI o1-mini	✅ Yes (3.6 hours)	Clear, step-by-step	~1,800	$0.027

Result: Tie on accuracy, DeepSeek uses fewer tokens.

Task 2: Code Debugging

Prompt: Gave both models a 30-line Python function with 3 bugs (off-by-one error, wrong variable name, missing None check) and asked them to find and fix all bugs.

Model	Bugs Found	Fix Quality	Total Tokens	Cost
DeepSeek Reasoner	3/3 ✅	All fixes correct	~2,100	$0.007
OpenAI o1-mini	3/3 ✅	All fixes correct	~2,400	$0.036

Result: Tie. Both found all bugs with correct fixes.

Task 3: Multi-Step Logic Puzzle

Prompt: A classic logic puzzle involving 5 people, 5 houses, 5 colors — simplified Einstein riddle.

Model	Answer Correct?	Reasoning Quality	Total Tokens	Cost
DeepSeek Reasoner	✅ Yes	Systematic elimination	~3,500	$0.012
OpenAI o1-mini	✅ Yes	Systematic elimination	~3,800	$0.057

Result: Tie. Both solved it correctly.

Task 4: Data Analysis from Raw Text

Prompt: Gave both models a messy sales report (1,500 words of unstructured text with numbers scattered throughout) and asked them to extract: total revenue, top product, month-over-month growth rate, and one actionable insight.

Model	Revenue	Top Product	Growth Rate	Insight Quality	Cost
DeepSeek Reasoner	✅	✅	✅ 12.3%	Good, practical	$0.015
OpenAI o1-mini	✅	✅	✅ 12.3%	Slightly more nuanced	$0.068

Result: o1-mini had a slightly better insight, but both got the numbers right.

Task 5: Complex API Integration Design

Prompt: "Design the data flow and error handling for a system that: receives webhooks from Stripe, validates signatures, updates a PostgreSQL database, sends notifications via SendGrid, and handles retries with exponential backoff. Show me the architecture."

Model	Completeness	Edge Cases	Code Quality	Cost
DeepSeek Reasoner	9/10	Covered 7/8	Production-ready	$0.021
OpenAI o1-mini	9/10	Covered 8/8	Production-ready	$0.095

Result: o1-mini caught one more edge case (concurrent webhook delivery), but both were production-quality.

Summary Scorecard

Task	DeepSeek Reasoner	OpenAI o1-mini	Winner
Math	✅ Correct	✅ Correct	Tie
Code Debug	✅ 3/3 bugs	✅ 3/3 bugs	Tie
Logic Puzzle	✅ Correct	✅ Correct	Tie
Data Analysis	✅ Accurate	✅ Accurate	o1-mini (slight)
API Design	✅ 9/10	✅ 9/10	o1-mini (slight)

The Cost Reality

Here's where it gets interesting. Across all 5 tasks:

Metric	DeepSeek Reasoner	OpenAI o1-mini
Total tokens used	~12,100	~15,000
Total cost	$0.059	$0.283
Cost ratio	1x	4.8x

DeepSeek Reasoner delivered essentially the same results at 1/5 the cost.

If you're running 10,000 reasoning API calls per day:

Model	Daily Cost	Monthly Cost
DeepSeek Reasoner	$590	$17,700
OpenAI o1-mini	$2,830	$84,900
Savings	$2,240/day	$67,200/month

That's the difference between a viable product and a money-losing one.

When to Pay for o1-mini

To be fair, o1-mini does have advantages in specific scenarios:

Edge case coverage: It caught one more edge case in the API design task
Nuanced insights: Slightly better at "reading between the lines" in data analysis
Complex multi-domain reasoning: If your task spans 3+ very different domains (law + medicine + finance), o1-mini might hold together better

But for 90% of use cases — code debugging, math, data extraction, logic, system design — DeepSeek Reasoner is just as good.

Try It Yourself

You can test DeepSeek Reasoner right now:

from openai import OpenAI

client = OpenAI(
    api_key="your-key",
    base_url="https://api.asiatekai.com/v1"
)

response = client.chat.completions.create(
    model="deepseek-reasoner",
    messages=[{"role": "user", "content": "Explain why the sky is blue using Rayleigh scattering"}]
)

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

Full pricing: asiatekai.com/pricing

Building a Multilingual AI Chatbot for Southeast Asia: Qwen vs DeepSeek in Practice

q409605362 — Tue, 09 Jun 2026 10:06:43 +0000

The biggest challenge when building AI apps for Southeast Asia isn't the code — it's the latency and language support. Most developers default to OpenAI, but when your users are in Bangkok, Ho Chi Minh City, or Kuala Lumpur, that round-trip to US servers adds 300-500ms before the model even starts thinking.

I spent time testing Qwen and DeepSeek models through Asiatek AI's Singapore gateway, and the results were eye-opening. Here's what I learned building a multilingual support chatbot.

The Setup

I built a simple customer support chatbot that needs to handle 4 languages: English, Thai, Vietnamese, and Malay. The requirements:

Respond in the user's language automatically
Keep latency under 3 seconds end-to-end
Cost less than $50/month at moderate volume

Why Singapore Gateway Matters

Before we dive into code, let's talk about latency. I ran curl benchmarks from different Southeast Asian cities:

From	To OpenAI (US)	To Asiatek AI (SG)
Bangkok	~320ms	~28ms
Ho Chi Minh City	~310ms	~32ms
Kuala Lumpur	~290ms	~18ms
Singapore	~260ms	~8ms
Jakarta	~340ms	~35ms

That's a 10-15x improvement in network latency alone. When you're streaming tokens, this makes the difference between a snappy response and one that feels broken.

Model Selection: Qwen vs DeepSeek

This was the most interesting part. Both model families have strengths:

Qwen (Alibaba) — Best for Southeast Asian languages

Excellent Thai, Vietnamese, Malay support out of the box
Qwen Turbo is insanely cheap at $0.08/M input tokens
Qwen Plus hits the sweet spot for quality vs cost

DeepSeek — Best for reasoning and code

DeepSeek Chat handles multilingual conversations well
DeepSeek Reasoner excels at complex problem-solving
128K context window is great for long conversations

For our customer support bot, Qwen Plus was the winner. Here's why:

It correctly identifies and responds in Thai, Vietnamese, and Malay without explicit prompting
At $0.84/M input + $2.50/M output, it's still 60% cheaper than GPT-4o
Quality is comparable to GPT-4o for customer support scenarios

The Code

1. Basic Setup

from openai import OpenAI

client = OpenAI(
    api_key="your_asiatek_key",
    base_url="https://api.asiatekai.com/v1"
)

Yes, that's it. If you're already using the openai Python package, just change api_key and base_url.

2. Auto-Detect Language and Respond

import json

SYSTEM_PROMPT = """You are a helpful customer support agent for a Southeast Asian e-commerce platform.

Rules:
1. Detect the user's language automatically
2. Always respond in the same language as the user
3. Be concise and friendly
4. If you're unsure about something, say so honestly

Supported languages: English, Thai, Vietnamese, Malay, Chinese"""

def chat(user_message: str, history: list = None) -> str:
    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
    if history:
        messages.extend(history)
    messages.append({"role": "user", "content": user_message})

    response = client.chat.completions.create(
        model="qwen-plus",
        messages=messages,
        temperature=0.7,
        max_tokens=500
    )
    return response.choices[0].message.content

3. Test It

# English
print(chat("How do I track my order?"))
# → "You can track your order by going to 'My Orders' in the app..."

# Thai
print(chat("สอบถามวิธีติดตามสินค้าหน่อยค่ะ"))
# → "คุณสามารถติดตามสินค้าได้โดยไปที่ 'คำสั่งซื้อของฉัน' ในแอป..."

# Vietnamese
print(chat("Làm sao để theo dõi đơn hàng?"))
# → "Bạn có thể theo dõi đơn hàng bằng cách vào 'Đơn hàng của tôi' trong ứng dụng..."

# Malay
print(chat("Macam mana nak track order saya?"))
# → "Anda boleh menjejak pesanan anda dengan pergi ke 'Pesanan Saya' dalam aplikasi..."

Qwen Plus correctly detects and responds in all four languages without any explicit language parameter.

4. Add Streaming for Better UX

def chat_stream(user_message: str, history: list = None):
    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
    if history:
        messages.extend(history)
    messages.append({"role": "user", "content": user_message})

    stream = client.chat.completions.create(
        model="qwen-plus",
        messages=messages,
        temperature=0.7,
        max_tokens=500,
        stream=True
    )

    for chunk in stream:
        if chunk.choices[0].delta.content:
            yield chunk.choices[0].delta.content

# Usage
for token in chat_stream("ร้านค้าเปิดกี่โมงคะ"):
    print(token, end="", flush=True)

With the Singapore gateway, the first token arrives in under 200ms — users see the response start almost instantly.

Cost Analysis

Let's crunch the numbers for a real-world scenario. Assume:

1,000 conversations/day
Average 5 messages per conversation
Average 200 input tokens + 150 output tokens per message

Model	Daily Cost	Monthly Cost
GPT-4o	$9.75	$292.50
Qwen Turbo	$0.17	$5.10
Qwen Plus	$1.52	$45.60
DeepSeek Chat	$0.62	$18.60

Qwen Plus hits our $50/month budget with room to spare. And if you're building a high-volume, low-complexity bot, Qwen Turbo is practically free at $5/month.

When to Use Which Model

Use Case	Best Model	Why
Customer support (multilingual)	Qwen Plus	Best SEA language support
High-volume FAQ bot	Qwen Turbo	Cheapest, fast enough
Code review assistant	DeepSeek Coder	128K context, code-optimized
Complex reasoning tasks	DeepSeek Reasoner	Strong reasoning ability
Long document analysis	Qwen Long	Up to 10M token context
General chatbot	DeepSeek Chat	128K context, great value

Tips for Production

Set temperature=0 for FAQ bots — More consistent, cheaper (fewer output tokens from rambling)
Use max_tokens wisely — Customer support doesn't need 4K token responses
Cache common queries — If 30% of your questions are "where's my order", cache the response
Monitor your usage — Asiatek AI dashboard shows token usage per API key

Try It Yourself

Sign up at asiatekai.com — free trial credits, no credit card needed
Get your API key instantly
Change base_url in your existing OpenAI code and you're done

from openai import OpenAI

client = OpenAI(
    api_key="your-key",
    base_url="https://api.asiatekai.com/v1"
)

Full pricing: asiatekai.com/pricing

How to Migrate from OpenAI to Asiatek AI in 5 Minutes

q409605362 — Tue, 09 Jun 2026 02:37:50 +0000

How to Migrate from OpenAI to Asiatek AI in 5 Minutes

Asiatek AI is an AI model API service built for Southeast Asian developers. With Singapore nodes delivering sub-50ms latency across the region, it offers a compelling alternative to Western-based providers.

The killer feature? Full OpenAI API compatibility. Just change two lines of code.

Why Migrate?

Southeast Asia Latency Advantage

If your users are in Southeast Asia, latency matters. Here's the difference:

Region	US Endpoint	Singapore Endpoint
Singapore	~200ms	<10ms
Jakarta	~220ms	<30ms
Bangkok	~210ms	<35ms
Manila	~190ms	<25ms

Every millisecond counts for real-time applications.

Pricing That Makes Sense

Compare the costs (USD per 1M tokens):

Model	Input	Output	Use Case
qwen-turbo	$0.08	$0.16	Fast, cheap tasks
qwen-coder-turbo	$0.16	$0.48	Code generation
qwen-plus	$0.84	$2.50	High-quality multilingual
qwen-coder-plus	$1.12	$3.34	Code + reasoning
qwen-max	$5.56	$16.66	GPT-4o equivalent
qwen-long	$1.38	$4.16	Ultra-long context
qwen-math-plus	$0.84	$2.50	Math reasoning
qwen-vl-plus	$1.38	$4.16	Vision understanding
deepseek-chat	$0.32	$1.32	128K context
deepseek-coder	$0.32	$1.32	Code + 128K context
deepseek-reasoner	$0.66	$2.63	Advanced reasoning

qwen-turbo is 97% cheaper than GPT-4o for basic tasks.

Multi-Language Native Support

Built for Southeast Asia? Models like qwen-plus handle Thai, Vietnamese, Indonesian, Malay, and more—with actual cultural context, not just translation.

Python Migration Guide

Before (OpenAI)

from openai import OpenAI

client = OpenAI(
    api_key="sk-...",
    base_url="https://api.openai.com/v1"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

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

After (Asiatek AI)

from openai import OpenAI

client = OpenAI(
    api_key="ak-...",  # Your Asiatek AI API key
    base_url="https://api.asiatekai.com/v1"  # Changed!
)

response = client.chat.completions.create(
    model="qwen-plus",
    messages=[{"role": "user", "content": "Hello!"}]
)

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

Two lines changed. That's it.

Node.js Migration Guide

Before (OpenAI)

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: 'https://api.openai.com/v1'
});

const response = await client.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Hello!' }]
});

console.log(response.choices[0].message.content);

After (Asiatek AI)

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.ASIATEK_API_KEY,  // Changed!
  baseURL: 'https://api.asiatekai.com/v1'  // Changed!
});

const response = await client.chat.completions.create({
  model: 'qwen-plus',
  messages: [{ role: 'user', content: 'Hello!' }]
});

console.log(response.choices[0].message.content);

cURL Example

curl https://api.asiatekai.com/v1/chat/completions \\
  -H "Authorization: Bearer $ASIATEK_API_KEY" \\
  -H "Content-Type: application/json" \\
  -d '{
    "model": "qwen-plus",
    "messages": [{"role": "user", "content": "What is 2+2?"}]
  }'

Model Selection Guide

Choose based on your use case:

Use Case	Recommended Model	Why
Chatbot / General	`qwen-turbo` or `qwen-plus`	Fast or high-quality
Code Completion	`qwen-coder-turbo`	Optimized for code
Code + Reasoning	`qwen-coder-plus`	Complex code tasks
Complex Reasoning	`deepseek-reasoner`	Chain-of-thought
Long Documents	`qwen-long` or `deepseek-chat`	128K+ context
Math Problems	`qwen-math-plus`	Specialized math
Image Understanding	`qwen-vl-plus`	Vision + text
Budget Everything	`qwen-turbo`	Cheapest option
GPT-4o Replacement	`qwen-max`	Same capability tier

Quick Decision Tree

Need vision?
├── Yes → qwen-vl-plus
└── No
    ├── Need code?
    │   ├── Yes → deepseek-coder (context) or qwen-coder-turbo (speed)
    │   └── No
    │       ├── Need reasoning?
    │       │   ├── Yes → deepseek-reasoner
    │       │   └── No
    │       │       ├── Long context?
    │       │       │   ├── Yes → qwen-long
    │       │       │   └── No
    │       │       │       └── qwen-plus (quality) or qwen-turbo (speed/cost)

Advanced Features

Streaming Responses

from openai import OpenAI

client = OpenAI(
    api_key="ak-...",
    base_url="https://api.asiatekai.com/v1"
)

stream = client.chat.completions.create(
    model="qwen-plus",
    messages=[{"role": "user", "content": "Write a haiku about code"}],
    stream=True  # Enable streaming
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Function Calling / Tools

response = client.chat.completions.create(
    model="qwen-plus",
    messages=[
        {"role": "user", "content": "What's the weather in Singapore?"}
    ],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get current weather for a city",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "city": {"type": "string", "description": "City name"}
                    },
                    "required": ["city"]
                }
            }
        }
    ],
    tool_choice="auto"
)

message = response.choices[0].message
if message.tool_calls:
    tool_call = message.tool_calls[0]
    print(f"Function: {tool_call.function.name}")
    print(f"Arguments: {tool_call.function.arguments}")

JSON Mode

response = client.chat.completions.create(
    model="qwen-plus",
    messages=[
        {"role": "user", "content": "Return a JSON with name and age"}
    ],
    response_format={"type": "json_object"}
)

FAQ

Do I need to change my code beyond base_url?

No. Asiatek AI uses the exact same API shapes as OpenAI. If you're using the OpenAI SDK, only two things change: api_key and base_url.

Is there a free tier?

Yes — sign up and get started without a credit card.

Can I use existing OpenAI code?

Yes! Just change:

The api_key to your Asiatek AI key
The base_url to https://api.asiatekai.com/v1
The model name to one from Asiatek AI's model list

Everything else works.

Conclusion

Migrating to Asiatek AI is genuinely this simple:

Get an API key from asiatekai.com
Change base_url to https://api.asiatekai.com/v1
Select a model from the available options

You get:

4x faster latency for Southeast Asian users
97% cost savings on basic tasks
Native multilingual support
Full OpenAI API compatibility

Stop paying premium prices for premium latency to US servers. Your users in Jakarta, Bangkok, and Manila will thank you.

Ready to migrate? Get your API key at asiatekai.com

Asiatek AI: OpenAI-Compatible API with 97% Cost Savings & 4x Faster Latency for Southeast Asia

q409605362 — Mon, 08 Jun 2026 16:21:01 +0000

Asiatek AI: OpenAI-Compatible API — 97% Cheaper, 4x Faster for Southeast Asia 🚀

If you're building for users in Singapore, Jakarta, Bangkok, or Manila — you're paying US-level prices for US-level latency, and your users are getting the short end of both sticks.

Asiatek AI fixes that. Same OpenAI SDK you already use. Just change 2 lines of code.

The Problem

Problem	Impact
US-based API endpoints	200ms+ latency for SE Asian users
GPT-4o pricing	$2.50/$10 per 1M tokens (input/output)
No regional optimization	No native Thai/Vietnamese/Indonesian support

The Solution

Metric	OpenAI (US)	Asiatek AI (SG)
Latency from Singapore	~200ms	<10ms
Latency from Jakarta	~220ms	<30ms
Cheapest chat model	~$0.15/1M tokens	$0.08/1M tokens
GPT-4o equivalent	$2.50/$10	$5.56/$16.66 (qwen-max)
Code model (128K)	$3/$15	$0.32/$1.32 (deepseek-coder)

That deepseek-coder at $0.32/$1.32 vs GPT-4o's $2.50/$10? That's a 97% cost reduction.

Migration: Change 2 Lines

Before (OpenAI)

from openai import OpenAI

client = OpenAI(
    api_key="sk-...",
    base_url="https://api.openai.com/v1"
)

After (Asiatek AI)

from openai import OpenAI

client = OpenAI(
    api_key="ak-...",  # Your Asiatek AI key
    base_url="https://api.asiatekai.com/v1"  # That's it
)

Same SDK. Same API shapes. Same streaming, function calling, JSON mode — everything works.

11 Models Available

Model	Input ($/1M)	Output ($/1M)	Best For
qwen-turbo	$0.08	$0.16	Fast & cheap tasks
qwen-coder-turbo	$0.16	$0.48	Code generation
qwen-plus	$0.84	$2.50	High-quality multilingual
qwen-coder-plus	$1.12	$3.34	Code + reasoning
qwen-max	$5.56	$16.66	GPT-4o equivalent
qwen-long	$1.38	$4.16	Ultra-long context
qwen-math-plus	$0.84	$2.50	Math reasoning
qwen-vl-plus	$1.38	$4.16	Vision understanding
deepseek-chat	$0.32	$1.32	128K context chat
deepseek-coder	$0.32	$1.32	Code + 128K context
deepseek-reasoner	$0.66	$2.63	Advanced reasoning

Full Feature Parity

✅ Streaming — Real-time token streaming
✅ Function calling — Tools / function calling support
✅ JSON mode — Structured output
✅ Vision — Image understanding (qwen-vl-plus)
✅ 128K+ context — Long documents (deepseek-chat, qwen-long)
✅ 201 languages — Native Thai, Vietnamese, Indonesian, Malay support

Quick Test with cURL

curl https://api.asiatekai.com/v1/chat/completions \\
  -H "Authorization: Bearer $ASIATEK_API_KEY" \\
  -H "Content-Type: application/json" \\
  -d '{
    "model": "qwen-plus",
    "messages": [{"role": "user", "content": "Hello from Southeast Asia!"}]
  }'

Node.js? Same Deal

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.ASIATEK_API_KEY,
  baseURL: 'https://api.asiatekai.com/v1'
});

const response = await client.chat.completions.create({
  model: 'qwen-plus',
  messages: [{ role: 'user', content: 'Hello!' }]
});

Why This Matters

If your users are in Southeast Asia:

200ms → 10ms latency means your chatbot feels instant
97% cheaper means you can scale 30x more for the same budget
Native language support means better results for Thai, Vietnamese, Indonesian, Malay queries

Stop paying US prices for US latency when your users are 10,000km away from Virginia.

Get Started

Sign up at asiatekai.com
Generate your API key from the dashboard
Change base_url to https://api.asiatekai.com/v1
Pick a model and go

Free tier available. No credit card required to start.

Built in Singapore, for Southeast Asia. asiatekai.com

Cut 70%+ LLM API Expense with Qwen-Turbo & DeepSeek: Real Pricing & Optimization Case

q409605362 — Sat, 06 Jun 2026 14:37:08 +0000

Most indie devs and small SaaS waste massive budget on expensive OpenAI/Claude APIs. After 2 months of production testing, I built a cost-saving solution combining Qwen-Turbo and DeepSeek series, cutting total token cost up to 72% without downgrading response quality. This guide includes official raw pricing, task allocation rules and real billing data.

Raw Official Token Price List (USD / 1M Tokens) Model Input Output Core Advantage Best Scenario Qwen-Turbo $0.05 $0.10 Ultra-low cost, multilingual Classification, short chat, translation DeepSeek-V3(Cache Hit) $0.028 $0.28 Cache discount Multi-turn customer chat DeepSeek-V3(Normal) $0.14 $0.28 Balance cost&quality General long document summary DeepSeek-R1 $0.55 $2.19 Top reasoning Math/code/logic calculation Core highlight：Qwen-Turbo input only $0.05 per million tokens, far cheaper than most mainstream open-source cloud APIs.
Core Optimization 3 Rules Task-based model routing（成本降幅 45%） Simple tasks(intention extraction, keyword pull): Qwen-Turbo; daily chat: DeepSeek-V3; complex reasoning: DeepSeek-R1 only. Most projects misuse high-end model for trivial requests, which causes overspending. Enable input cache（cost cut extra 25%） DeepSeek native cache auto-discount repeated context input; our platform adds global request cache to Qwen services, repeat prompts hit cached result directly with zero token cost. Prompt compression（save 5%-10% token） Trim redundant system prompt, remove useless description in fixed prompt template.
Real Case: Small AI Chatbot Monthly Cost Comparison Original: Full GPT-3.5 → $218/month After Qwen+DeepSeek optimization → $59/month (↓72%) Ending If you want ready-to-use low-price Qwen & DeepSeek API with built-in routing+cache system, check our pricing page: asiatekai.com. We provide pay-as-you-go token billing and monthly subscription plans for indie developers.

How to Migrate from OpenAI to Asiatek AI in 5 Minutes

q409605362 — Fri, 05 Jun 2026 05:30:17 +0000

The killer feature? Full OpenAI API compatibility. Just change two lines of code.

Why Migrate?

Southeast Asia Latency Advantage

If your users are in Southeast Asia, latency matters. Here's the difference:

Region	US Endpoint	Singapore Endpoint
Singapore	~200ms	<10ms
Jakarta	~220ms	<30ms
Bangkok	~210ms	<35ms
Manila	~190ms	<25ms

Every millisecond counts for real-time applications.

Pricing That Makes Sense

Compare the costs (USD per 1M tokens):

Model	Input	Output	Use Case
qwen-turbo	$0.08	$0.16	Fast, cheap tasks
qwen-coder-turbo	$0.16	$0.48	Code generation
qwen-plus	$0.84	$2.50	High-quality multilingual
qwen-coder-plus	$1.12	$3.34	Code + reasoning
qwen-max	$5.56	$16.66	GPT-4o equivalent
qwen-long	$1.38	$4.16	Ultra-long context
qwen-math-plus	$0.84	$2.50	Math reasoning
qwen-vl-plus	$1.38	$4.16	Vision understanding
deepseek-chat	$0.32	$1.32	128K context
deepseek-coder	$0.32	$1.32	Code + 128K context
deepseek-reasoner	$0.66	$2.63	Advanced reasoning

qwen-turbo is 97% cheaper than GPT-4o for basic tasks.

Multi-Language Native Support

Built for Southeast Asia? Models like qwen-plus handle Thai, Vietnamese, Indonesian, Malay, and more—with actual cultural context, not just translation.

Python Migration Guide

Before (OpenAI)


python
from openai import OpenAI

client = OpenAI(
    api_key="sk-...",
    base_url="https://api.openai.com/v1"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

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