Chinese AI Model Pricing Complete Comparison: Latest Cost-Performance Analysis June 2026

What Problem Does This Tutorial Solve?

You will get a panoramic view of Chinese AI API pricing:

• Complete pricing tables for 12 model providers
• Full-dimension text / image / video comparison
• Free quotas and sign-up benefits
• Best picks for different budgets
• Practical money-saving strategies

🎯 Pick the right model and ¥100 lasts you a month! Pick the wrong one and ¥100 burns in 3 days.

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Text Model Pricing Overview

Price unit: ¥ / million tokens (June 2026)

Provider	Model	Input Price	Output Price	Context	Notes
DeepSeek	V4-Pro	¥1.0	¥4.0	128K	Cost-performance king
DeepSeek	R1	¥2.0	¥8.0	128K	Reasoning-specific
Kimi	K2-Thinking	¥2.0	¥8.0	128K	2M chars free
Kimi	K2-Lite	¥0.5	¥2.0	32K	Lightweight & cheap
Qwen	Qwen-Plus	¥2.0	¥8.0	128K	Top Alibaba pick
Qwen	Qwen-Turbo	¥0.3	¥0.6	32K	Cheapest sub-128K
Doubao	Doubao-Pro	¥0.8	¥2.0	32K	ByteDance rising star
Doubao	Doubao-Lite	¥0.3	¥0.6	16K	Ultra-lightweight
Zhipu	GLM-4	¥2.0	¥8.0	128K	Veteran, stable
GLM	GLM-4-Flash	¥0.1	¥0.1	128K	🔥 Generous free quota
Xiaomi	MiMo V2.5	¥1.0	¥4.0	512K	Huge context window
Baichuan	Baichuan 4	¥2.0	¥8.0	128K	Strong in vertical domains
Baichuan	Baichuan 4-Turbo	¥0.5	¥2.0	32K	Fast
01.AI	Yi-Large	¥2.0	¥8.0	512K	Huge context window
Baidu	ERNIE 4.5	¥2.0	¥8.0	128K	Baidu ecosystem
Tencent	Hunyuan-Pro	¥1.0	¥4.0	32K	Tencent Cloud
iFlytek	Spark 4.0	¥2.5	¥10.0	32K	Voice advantage
MiniMax	abab6.5s	¥1.0	¥4.0	128K	Multimodal

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Image Understanding (Vision) Pricing

Provider	Model	Price	Notes
DeepSeek	V4-Pro (vision)	¥1.5/1K tokens	Per-token billing
Qwen	Qwen-VL-Plus	¥2.0/1K tokens	Video support
GLM	GLM-4V	¥5.0/1K tokens	High-res understanding
Kimi	K2-Vision	¥2.0/1K tokens	Document OCR
Doubao	Doubao-Vision	¥1.0/1K tokens	ByteDance

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Video Generation Pricing

Provider	Model	Per Generation	Duration	Notes
Kuaishou Kling	Kling V3.0	¥2.0/gen	5 sec	Great cost-performance
Kling	Kling V3.0 Pro	¥5.0/gen	10 sec	High-def mode
MiniMax	Video-01	¥1.0/gen	6 sec	Cheapest
Zhipu	CogVideoX	¥3.0/gen	4 sec	Open source, local deployable
Tencent	Hunyuan-Video	¥0.5/gen	3 sec	Beta pricing

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Free Quota Showdown

Provider	Free Quota	Validity	How to Get
DeepSeek	5M tokens	Upon signup	Phone number registration
Kimi	2M chars context	Permanent free	Upon signup
GLM	10M tokens	New users	Registration + verification
Qwen	1M tokens/month	Monthly	Alibaba Cloud account
Doubao	500K tokens/day	Daily	Volcano Engine
Baichuan	2M tokens	New users	Upon signup
01.AI	1M tokens	New users	Upon signup
Baidu	10M tokens	New users	Baidu Cloud account
iFlytek	2M tokens	New users	Registration + verification

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Recommendations by Budget

💰 Zero Budget (Free Tier)

Main model: DeepSeek Chat (free web version)
API testing: GLM-4-Flash (¥0.1/million)
Long text: Kimi (2M chars free)
Vision: Qwen-VL free quota

🪙 Monthly Budget ¥50

Daily chat: DeepSeek V4-Pro (¥5/million)
  → ~10M tokens ≈ 500K words of conversation ≈ 17K words/day
Code generation: DeepSeek V4-Pro
Long documents: Yi-Large-Turbo (512K context)

💵 Monthly Budget ¥200 — Small Projects

Main: DeepSeek V4-Pro + Kimi K2 dual models
Agent: GLM-4 + Function Calling
Vision: Qwen-VL-Plus
Video: Kling V3.0 (generate ~20 short videos)
Embedding: Self-hosted text2vec (free)

🏢 Monthly Budget ¥1000+ — Commercial Projects

High concurrency: Qwen-Turbo (¥0.3/million — extremely cheap)
Main model: DeepSeek V4-Pro + Baichuan 4 dual-channel
Reasoning: DeepSeek R1
Video: Kling V3.0 Pro
Multimodal: Qwen-VL-Plus
Fine-tuning: Yi-Large + LoRA

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Practical Money-Saving Strategies

Strategy 1: Cache Common Responses

from functools import lru_cache
import hashlib

class CachedAI:
    """Cache responses for identical / similar questions to reduce API calls"""

    def __init__(self):
        self.client = OpenAI(
            api_key=os.getenv("DEEPSEEK_API_KEY"),
            base_url="https://api.deepseek.com/v1",
        )
        self.cache = {}

    def ask(self, prompt: str, use_cache: bool = True) -> str:
        # Normalize key (strip punctuation and whitespace differences)
        cache_key = hashlib.md5(
            prompt.lower().strip().encode()
        ).hexdigest()

        if use_cache and cache_key in self.cache:
            print("✅ Cache hit, ¥0")
            return self.cache[cache_key]

        response = self.client.chat.completions.create(
            model="deepseek-v4-pro",
            messages=[{"role": "user", "content": prompt}],
            max_tokens=500,  # Limit output length
        )

        answer = response.choices[0].message.content
        self.cache[cache_key] = answer
        cost = response.usage.total_tokens * 5 / 1000000  # Approx ¥/million
        print(f"💰 API call, ~¥{cost:.4f}")
        return answer

Strategy 2: Smart Routing

def smart_route(prompt: str) -> str:
    """Select the cheapest model based on task type"""
    prompt_len = len(prompt)

    # Simple tasks → cheap model
    if prompt_len < 200 and any(kw in prompt for kw in ["hello", "thanks", "bye", "weather"]):
        model = "qwen-turbo"  # ¥0.3/million
    # Medium tasks → cost-performance model
    elif prompt_len < 2000:
        model = "deepseek-v4-pro"  # ¥1.0/million
    # Complex / long text → cheap model with long context
    else:
        model = "yi-large-turbo"  # 512K context

    return model

Strategy 3: Limit max_tokens

# ❌ Wastes money
response = client.chat.completions.create(
    model="deepseek-v4-pro",
    messages=[...],
    # No max_tokens set → could output 8000 tokens
)

# ✅ Saves money
response = client.chat.completions.create(
    model="deepseek-v4-pro",
    messages=[...],
    max_tokens=500,  # Most responses don't need more than 500 tokens
)

Strategy 4: Use System Prompt Compression

# Add this line to your system prompt — output can shrink 30-50%
compression_prompt = "Answer concisely. Avoid unnecessary pleasantries and repetition."

# Or request directly in the conversation
user_message = "Please answer in no more than 200 words..."

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International Payment Methods — How Overseas Users Pay

This is the #1 pain point for international developers. Here’s the reality:

Provider	Intl Credit Card	PayPal	Alipay	WeChat Pay	Crypto	Notes
DeepSeek	✅	❌	✅	✅	❌	Top-up model; $2 minimum
Kimi	✅ (Intl edition)	❌	✅	✅	❌	Separate China/intl platforms
Qwen	⚠️ (Intl edition)	❌	✅	❌	❌	Intl edition requires business verification
Doubao	❌	❌	✅	✅	❌	China-only payment for now
Zhipu GLM	✅	❌	✅	✅	❌	Supports international cards
MiniMax	✅	❌	✅	✅	❌	International-friendly
01.AI (Yi)	✅	❌	✅	✅	❌	Supports international cards
Baichuan	❌	❌	✅	✅	❌	China-only
iFlytek Spark	❌	❌	✅	✅	❌	China-only
Tencent Hunyuan	⚠️	❌	✅	✅	❌	Intl requires Tencent Cloud Intl account

Workarounds for China-Only Payment

1. Ask a Chinese friend to top up your account (most common solution)
2. Use a proxy service (WildCard, VPay — service fees apply, ~3-5%)
3. Stick to free quotas — DeepSeek (5M), GLM-4-Flash (¥0.1/M), Kimi (2M chars free)
4. Use international-friendly providers as your primary API

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Hidden Costs Most Tutorials Don’t Mention

1. Context Window ≠ What You Pay For

When you send a 100K-token document, you pay for ALL 100K input tokens — even if the model only needs the first 10K. This is the biggest hidden cost:

Example: Processing a 200-page PDF with Kimi K2.6
- If you send all 200 pages: 256K input tokens × ¥2/M = ¥0.51 per query
- If you pre-filter to relevant 10 pages: 13K input tokens × ¥2/M = ¥0.03 per query
→ 17x cost difference per query!

2. Output Token Waste

Models tend to over-explain unless constrained:

Without max_tokens:  8000 output tokens  →  ¥0.032 (DeepSeek)
With max_tokens=500:   500 output tokens  →  ¥0.002 (DeepSeek)
→ 16x cost difference per call!

3. System Prompt Costs

Your system prompt is counted as input tokens on EVERY call:

System prompt: 500 tokens
× 10,000 API calls/day
= 5M wasted tokens/day = ¥5/day (at ¥1/M)
= ¥1,825/year on just the system prompt!

4. Image Token Cost Multiplier

Vision models bill images differently — 1 image can cost 500-1500 tokens:

Model	512×512 image	1024×1024 image	2048×2048 image
Qwen-VL-Plus	~300 tokens	~600 tokens	~1200 tokens
GLM-4V	~500 tokens	~1000 tokens	~2000 tokens
Kimi K2.5	~400 tokens	~800 tokens	~1600 tokens

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Regional Availability and Latency

Provider	China Server	Singapore Server	US Server	Notes
DeepSeek	✅	❌	❌	All traffic routes through China
Kimi	✅ (moonshot.cn)	✅ (moonshot.ai)	❌	Intl users get SG routing
Qwen	✅ (dashscope)	✅ (dashscope-intl)	❌	Two separate endpoints
Zhipu GLM	✅	❌	❌	China-only infrastructure
Doubao	✅	❌	❌	Volcano Engine (China regions only)

Latency from outside China:

From	To DeepSeek (CN)	To Kimi Intl (SG)	To Qwen Intl (SG)
US West	~250ms	~180ms	~180ms
Europe	~350ms	~200ms	~200ms
Southeast Asia	~100ms	~30ms	~30ms
Japan/Korea	~80ms	~80ms	~80ms

Price Trend (2024-2026)

Early 2024: ~¥12/million tokens (GPT-3.5 comparable)
Mid 2024:   ~¥8/million tokens  (DeepSeek V2)
Late 2024:  ~¥4/million tokens  (DeepSeek V3)
2025:       ~¥2/million tokens  (GLM-4-Flash)
2026:       ~¥1/million tokens  (multiple providers)
Late 2026:  projected ~¥0.5/million tokens

Trend: 50% drop per year. By 2027, AI inference costs will approach zero.

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FAQ

Q: Why is DeepSeek the cheapest?

A: DeepSeek’s self-developed MoE (Mixture of Experts) architecture + efficient training methods result in inference costs far below peers. DeepSeek V4-Pro only activates a subset of parameters, making both training and inference cheaper.

Q: Are more expensive models always better?

A: No. DeepSeek V4-Pro (¥1.0) outperforms some ¥2.0 models on many tasks. The key is scenario fit, not price.

Q: How do I check the latest pricing?

A: Each provider’s official website → Open Platform → Product Pricing. Prices may adjust monthly; it is recommended to re-evaluate quarterly.

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Next Steps

• China AI Model Comprehensive Comparison
• DeepSeek API Beginner Guide

📝 Prices are based on public information as of June 2026. Refer to each platform’s latest pricing page for current rates.