Chinese AI Model Pricing Comparison: A Cost-Effectiveness Guide (June 2026)

📌 All prices below are official list rates as of June 2026, verified on each provider’s pricing page. Prices in this category move frequently — re-verify before budgeting. USD figures use the listed USD price where the provider offers one; CNY figures are converted at ~¥7.2/$1 where the provider prices in CNY.

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Pricing is the single biggest reason developers switch to Chinese AI models — and the single biggest source of confusion when actually doing it. Provider pricing pages are inconsistent (some price in USD, some in CNY; some per-million-tokens, some per-thousand; some bundle context tiers, some don’t), and most “pricing comparison” articles online are months out of date the day they’re published.

This guide gives you the current picture as of June 2026: verified list prices across the major Chinese providers, organized by modality (text, image, video), plus the budget-based picks and the hidden costs that determine what you actually pay in practice. Where a provider’s pricing couldn’t be independently verified at the time of writing, that’s noted rather than guessed.

Text Model Pricing (Verified June 2026)

Price unit: USD per million tokens (CNY equivalent in notes where the provider prices in CNY).

Provider	Model	Input ($/M)	Output ($/M)	Context	Notes
DeepSeek	V4-flash	$0.14	$0.28	1M	Value king for text work
DeepSeek	V4-pro	$0.435	$0.87	1M	Higher-capability variant
Kimi	K2.6	$0.95	$4.00	262K	Long-context retrieval specialist
Qwen	qwen3.6-flash	$0.25	$1.50	—	Volume tier for high-throughput
Qwen	qwen3.7-plus	$0.40	$1.60	—	Value tier of the 3.7 series
Qwen	qwen3.7-max	$2.50	$7.50	Up to 1M	Flagship; Thinking mode available
GLM	GLM-4.7	$0.40	$1.75	256K	Competitive mid-tier
Doubao	Seed 2.1	~$0.83 (¥6)	~$4.17 (¥30)	256K	Priced in CNY; multimodal included
ERNIE	Bot 5.1	~$1.00	~$1.00	128K	Verify on Baidu Qianfan
Hunyuan	Turbo	~$0.55 (¥4)	~$2.20 (¥16)	256K	Tencent Cloud; verify current tier
MiniMax	MiniMax-2	~$0.30	~$1.20	256K	Multimodal; verify on provider page
Step-2	Step-2	verify on provider	verify on provider	256K	Math/reasoning specialist

For reference: GPT-5 is $1.25/$10.00, Claude Opus 4.8 is $5.00/$25.00, Claude Sonnet 4.6 is $3.00/$15.00. On output tokens — the part you actually pay for when generating — Chinese models are typically 10–50× cheaper than Western frontier models.

⚠️ The DeepSeek deepseek-chat and deepseek-reasoner API names are being phased out (announced deprecation July 2026). If you’re writing new code, use the deepseek-v4-flash / deepseek-v4-pro naming to avoid a forced migration later. Existing code using the old names will keep working until the deprecation date, then must be updated.

The headline economics

For a typical coding request (≈1K input tokens, ≈500 output tokens):

• DeepSeek V4-flash: ~$0.000028 per call
• GPT-5: ~$0.0009 per call
• Ratio: ~32× cheaper on output for equivalent work

Over 10,000 API calls/day, that’s roughly $8/month versus $270/month before accounting for the fact that cheaper pricing encourages more experimentation. The gap is large enough to change what’s economically viable — workloads that don’t pencil out on GPT-5 become routine on DeepSeek.

Image and Vision Pricing

Vision-capable models bill images as tokens (typically 300–2000 tokens per image depending on resolution). Per-token rates follow the text pricing above; the variable is how many tokens an image costs.

Provider	Model	Notes
Qwen	Qwen-VL (3.7 series)	Image understanding + limited generation; video support
GLM	GLM-4.7 vision	High-res understanding
Doubao	Seed 2.1 vision	ByteDance; multimodal suite
Kimi	K2.6 vision	Document OCR

DeepSeek V4 is text-only. If you need vision, DeepSeek is not an option — use Qwen-VL, GLM-4.7 vision, or Doubao Seed 2.1.

The image-token multiplier gotcha

Vision models bill images as tokens, and the token count scales with resolution:

Model	512×512 image	1024×1024 image	2048×2048 image
Qwen-VL	~300 tokens	~600 tokens	~1200 tokens
GLM-4V	~500 tokens	~1000 tokens	~2000 tokens

Cost tip: resize images to 512×512 before sending to vision APIs. You save 60–75% on the image-token cost without losing meaningful detail for most use cases. A 2048×2048 product photo billed at 2000 tokens is wasteful if the model only needs to identify the product category.

Video Generation Pricing

Provider	Model	Pricing model	Notes
Kuaishou	Kling 3.0	~$0.084/sec standard	1080p included; 4K available at premium
MiniMax	Hailuo AI video	per-generation	Top user reputation for video quality
Tencent	Hunyuan video	per-generation	Tencent ecosystem integration
Zhipu	CogVideoX	per-generation	Open source, locally deployable

Video generation is priced per-second or per-generation rather than per-token. Kling’s ~$0.084/sec standard rate means a 5-second 1080p clip costs ~$0.42; 4K costs more. Budget a 10–15% buffer for failed generations and silent content-moderation rejections — most providers bill per generation attempt, not per successful generation.

Free Tiers and Sign-Up Credits

Provider	Free offer	How to get it
DeepSeek	Free web version (chat.deepseek.com); new accounts get credits	Phone registration
Kimi	Free web version; 2M chars context on free tier	Signup
Qwen	Free at qwen.chat; 1M tokens/month on API	Alibaba Cloud account
GLM	Free web version; generous API free tier	Registration + verification
Doubao	Free basic features; 500K tokens/day on API	Volcano Engine account
ERNIE	10M tokens for new users	Baidu Cloud account
iFlytek	2M tokens for new users	Registration + verification

Free tiers are the right way to evaluate a model before committing API spend. Limits change — check the provider’s current offer rather than relying on the figures above.

Recommendations by Budget

Zero budget (free tier only)

Main model:      DeepSeek (free web version)
API testing:     GLM (generous free tier)
Long documents:  Kimi (2M chars free)
Vision:          Qwen-VL free quota

This covers evaluation and light personal use without spending anything. The limitation: free tiers aren’t suitable for production traffic.

~$10/month — Light personal use

Daily chat:      DeepSeek V4-flash (~$0.14/$0.28 per M tokens)
Code generation: DeepSeek V4-flash
Long documents:  Qwen 3.7-plus (value tier)

At DeepSeek V4-flash’s pricing, $10/month buys roughly 25–35M tokens depending on your input/output mix — enough for heavy personal use. Route long-document work to Qwen 3.7-plus when you need the larger context.

~$50/month — Small projects

Main:            DeepSeek V4-flash (default) + Kimi K2.6 (long context)
Reasoning:       DeepSeek V4-pro or Qwen 3.7-max with Thinking mode
Vision:          Qwen-VL
Video:           Kling 3.0 (~$0.42 per 5-sec clip, ~100 clips/month)

This is the multi-model sweet spot. DeepSeek handles the bulk of text work cheaply; Kimi handles long-context retrieval; Qwen-VL handles vision; Kling handles occasional video. Total spend stays well under $50 for moderate traffic.

$200+/month — Commercial projects

High volume:     Qwen 3.6-flash (cheapest input) for throughput
Main:            DeepSeek V4-flash + Qwen 3.7-plus (dual-channel)
Hard reasoning:  Qwen 3.7-max with Thinking, or Step-2
Video:           Kling 3.0
Multimodal:      Qwen-VL or Doubao Seed 2.1

At commercial scale, route by task: cheapest model for high-volume simple work, value model for most production traffic, flagship model for hard reasoning. This multi-tier routing keeps total spend a fraction of what a single Western frontier model would cost for equivalent volume.

Hidden Costs Most Pricing Guides Skip

The list price is not what you pay. Four hidden costs dominate real-world spend:

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 to answer.

Processing a 200-page PDF with Kimi K2.6:
- Send all 200 pages:  256K input × $0.95/M  = ~$0.24 per query
- Pre-filter to 10 relevant pages: 13K input × $0.95/M = ~$0.01 per query
→ ~24× cost difference per query

Fix: pre-filter documents before sending. Chunking, retrieval, or even simple keyword-based filtering can cut input costs by an order of magnitude with no quality loss.

2. Output token waste

Models tend to over-explain unless constrained:

Without max_tokens:   8000 output tokens × $0.28/M = ~$0.0022 (DeepSeek)
With max_tokens=500:    500 output tokens × $0.28/M = ~$0.00014 (DeepSeek)
→ ~16× cost difference per call

Fix: set max_tokens on every call. Most responses don’t need more than 500–1000 tokens; letting the model run to its default ceiling wastes money on output nobody reads.

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 tokens/day of system-prompt overhead
= ~$0.70/day at DeepSeek V4-flash rates
= ~$255/year on just the system prompt

At GPT-5 rates, that same system-prompt overhead is ~$6,250/year. The system-prompt tax scales with your call volume and your per-token price.

Fix: keep system prompts under 100 tokens. Move detailed instructions to the first user message (which can be cached between calls if your client supports prompt caching). Provider-side prompt caching, where available, also helps.

4. Image token cost multiplier

Covered above — resize images to 512×512 before sending. The savings are large and the quality loss is usually negligible.

Practical Money-Saving Strategies

Strategy 1: Cache common responses

import hashlib
import os
from openai import OpenAI

class CachedAI:
    """Cache responses for identical prompts to avoid duplicate 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:
        cache_key = hashlib.md5(
            prompt.lower().strip().encode()
        ).hexdigest()

        if use_cache and cache_key in self.cache:
            return self.cache[cache_key]  # Cache hit, $0

        response = self.client.chat.completions.create(
            model="deepseek-v4-flash",
            messages=[{"role": "user", "content": prompt}],
            max_tokens=500,
        )
        answer = response.choices[0].message.content
        self.cache[cache_key] = answer
        return answer

For workloads with repeated identical questions (support bots, FAQ systems), caching eliminates the API call entirely. Even a 20% cache hit rate cuts costs meaningfully.

Strategy 2: Smart routing by task

def smart_route(prompt: str) -> str:
    """Pick the cheapest model that handles the task."""
    prompt_len = len(prompt)

    # Simple/short → cheapest
    if prompt_len < 200 and any(kw in prompt.lower() for kw in
                                ["hello", "thanks", "bye", "weather"]):
        return "qwen3.6-flash"        # cheapest input
    # Medium → value tier
    elif prompt_len < 2000:
        return "deepseek-v4-flash"    # best value for most work
    # Long context → larger window
    else:
        return "qwen3.7-plus"         # value tier with larger context

Route by task complexity and length. Don’t send “hello” to your flagship model, and don’t send a 100K-token document to a model with a 32K window.

Strategy 3: Limit max_tokens

# Wasteful — model may output 8000 tokens
response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[...],
    # no max_tokens set
)

# Economical — caps output at what you actually need
response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[...],
    max_tokens=500,
)

Strategy 4: Compress with system prompt

# Add to system prompt — output shrinks 30-50%
compression_prompt = "Answer concisely. No pleasantries, no repetition."

# Or request directly
user_message = "Answer in no more than 200 words."

International Payment: How Overseas Users Pay

This is the top pain point for developers outside China. The reality as of June 2026:

Provider	Intl credit card	Alipay	WeChat Pay	Notes
DeepSeek	✅	✅	✅	Top-up model; low minimum
Kimi	✅ (intl edition)	✅	✅	Separate China/intl platforms
Qwen	⚠️ (intl edition)	✅	❌	Intl edition requires business verification
Zhipu GLM	✅	✅	✅	Supports international cards
MiniMax	✅	✅	✅	International-friendly
Doubao	❌	✅	✅	China-only payment for now
Tencent Hunyuan	⚠️	✅	✅	Intl requires Tencent Cloud Intl account
iFlytek Spark	❌	✅	✅	China-only
Baidu ERNIE	❌	✅	✅	China-only (Baidu Cloud)

Key takeaway: DeepSeek, Kimi (intl), Zhipu GLM, and MiniMax are the international-credit-card-friendly options. Most other providers require Alipay or WeChat Pay, which need a Chinese bank account.

Workarounds for China-only payment

1. Stick to international-friendly providers (DeepSeek, Kimi intl, GLM, MiniMax) as your primary API — this covers most use cases.
2. Use free tiers for evaluation — DeepSeek, GLM, Kimi, and Qwen all have usable free entry points.
3. Ask a contact in China to top up your account if you need a China-only provider — this is the most common solution in practice.

Regional Availability and Latency

Provider	China server	Singapore server	US server	Notes
DeepSeek	✅	❌	❌	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)

Approximate latency from outside China (varies by network path and load):

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

Streaming is essential for non-China users. Without streaming, a 250ms RTT means users wait ~2 seconds before seeing any response. Always set stream=True for user-facing applications.

Price Trend (2024–2026)

The directional trend in Chinese AI API pricing has been steadily downward:

Early 2024: ~¥12/M tokens (GPT-3.5-comparable era)
Mid 2024:   ~¥8/M tokens  (DeepSeek V2)
Late 2024:  ~¥4/M tokens  (DeepSeek V3)
2025:       ~¥2/M tokens  (GLM-4-Flash and competitors)
2026:       ~¥1/M tokens  (multiple providers at flash tiers)

The trend has been roughly a 50% drop per year on the cheapest tiers. Whether this continues depends on inference-cost curves and provider economics — don’t budget on prices halving again, but do expect them to keep moving.

FAQ

Why is DeepSeek the cheapest?

DeepSeek’s MoE (Mixture of Experts) architecture activates only a subset of parameters per token, which lowers inference cost. Combined with efficient training and a price-leader market positioning, this produces the lowest per-token rates in the ecosystem. Whether that pricing is sustainable at scale is a separate question — enjoy it while it lasts.

Are more expensive models always better?

No. DeepSeek V4-flash ($0.14/$0.28) outperforms some $2+ models on coding and Chinese-language tasks. The key is scenario fit, not price. Use the per-task recommendations in our model comparison rather than defaulting to the most expensive option.

How do I check the latest pricing?

Each provider’s open-platform pricing page. Prices adjust on a monthly-to-quarterly cadence — re-evaluate your model selection quarterly, and whenever a provider announces a new model generation.

Should I lock in a provider long-term?

Given the pace of change, no. Build your architecture around OpenAI-compatible APIs (which most Chinese providers support) so you can switch providers with a base-URL change. Avoid provider-specific features that would lock you in unless the feature is genuinely worth the lock-in.

Methodology and Limitations

1. Prices are a June 2026 snapshot. All figures were verified on provider pricing pages at the time of writing. They will be different by the time you read this — re-verify before budgeting.
2. USD vs CNY. Providers that price in CNY are converted at ~¥7.2/$1. The actual exchange rate and any FX fees from your payment method will affect your real cost.
3. List prices, not negotiated. Enterprise contracts often get volume discounts. If you’re spending enough to negotiate, the list prices above are a ceiling, not your actual rate.
4. Tier mapping varies. “Flash,” “plus,” “max,” “pro,” “turbo” mean different things at different providers. Always check which tier a price refers to on the provider’s page.
5. Your workload determines real cost. The hidden-cost section above matters more than the list price. A cheap model with wasteful prompting costs more than an expensive model used efficiently.

The Bottom Line

Chinese AI model pricing in 2026 is dramatically lower than Western frontier pricing — roughly 10–50× cheaper on output tokens for comparable work. DeepSeek V4-flash is the value king for text; Qwen’s flash and plus tiers compete on volume; Kimi K2.6 commands a long-context premium; Doubao Seed 2.1 bundles multimodal at a CNY price point. The multi-model approach (DeepSeek for text, Kimi for long context, Qwen-VL for vision, Kling for video) gives you the best model per task at a total spend that’s a fraction of any single Western frontier model.

But list price is only the start. The hidden costs — context waste, output bloat, system-prompt overhead, image-token multipliers — often dominate real-world spend. The developers who save the most aren’t the ones who pick the cheapest model; they’re the ones who pick a cheap model and use it efficiently. Cache, route, cap max_tokens, resize images, and keep system prompts short. Those practices matter more than which provider you choose.

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🔗 Related reading:

• Chinese AI APIs vs OpenAI: Cost Modeling — a 30-day cost model built from official pricing
• China AI Models Comparison 2026 — per-task model selection
• Kling API Developer Guide — video generation pricing in practice
• DeepSeek API Beginner Guide — get started with the value leader