Chinese AI API Pricing Comparison 2026: Every Model, Every Number

PricingComparisonBudget

Published July 20, 2026 · Prices verified & updated

Chinese AI models offer the best price-to-performance ratio in the industry. This is the definitive pricing reference for developers comparing costs across DeepSeek, Kimi, GLM, ERNIE, and Qwen. All prices are in USD per 1M tokens, as charged on AIWave.

Flagship Models Compared

ModelProviderInput $/1MOutput $/1MCache Hit $/1MContext
DeepSeek V4 ProDeepSeek AI$0.0638$1.914$5.742128K
Kimi K3Moonshot AI$4.50$22.50$0.901M
GLM-5.1Zhipu AI$2.10$6.60$0.68128K
ERNIE 5.1Baidu$0.60$1.80$0.12128K
Qwen3 Coder 480BAlibaba$0.46$1.38$0.09128K

Budget-Friendly Options

If you’re optimizing for cost, these models deliver excellent quality at minimal expense:

ModelInput $/1MOutput $/1MUse Case
DeepSeek V4 Flash$0.638$1.914Fast tasks, autocomplete
ERNIE Tiny 8K$0.005$0.015Simple classification, embedding
GLM-4.5$0.70$2.17General chat, light tasks
Qwen3 8B$0.05$0.15Quick responses, routing

Western Model Comparison

How do Chinese models stack up against Western alternatives on price?

Categorymost cost-effective ChineseWestern EquivalentSavings
FlagshipDeepSeek V4 Pro ($1.914/$5.742)GPT-4o ($2.50/$10.00)43% on output
CodingKimi K2.7 Code ($1.89/$6.00)Claude 4 Sonnet ($3.00/$15.00)60% on output
BudgetERNIE Tiny ($0.005/$0.015)GPT-4o-mini ($0.15/$0.60)97%

Build Your Own Cost Calculator

Copy this Python script to estimate your monthly bill before you commit:

# pricing_config.py — AIWave pricing reference
MODELS = {
    "deepseek-v4-pro": {"input": 2.09, "output": 4.18, "cache": 1.04},
    "deepseek-v4-flash": {"input": 0.18, "output": 0.36, "cache": 0.0364},
    "kimi-k3": {"input": 4.50, "output": 22.50, "cache": 0.90},
    "glm-5.1": {"input": 2.10, "output": 6.60, "cache": 0.68},
    "glm-5": {"input": 1.55, "output": 4.96, "cache": 0.40},
    "ernie-5.1": {"input": 0.60, "output": 1.80, "cache": 0.12},
    "ernie-tiny-8k": {"input": 0.005, "output": 0.015, "cache": 0.001},
}

def calc_monthly(model, daily_input_m, daily_output_m, cache_pct=0.3):
    """Estimate monthly cost. Prices per 1M tokens."""
    p = MODELS[model]
    daily_input_cost = (daily_input_m * (1 - cache_pct) * p["input"] + daily_input_m * cache_pct * p["cache"])
    daily_output_cost = daily_output_m * p["output"]
    monthly = (daily_input_cost + daily_output_cost) * 30
    return f"{model}: ${monthly:,.2f}/month (${daily_input_cost + daily_output_cost:.4f}/day)"

# Examples
print(calc_monthly("deepseek-v4-pro", 5, 15))   # 5M in + 15M out per day
print(calc_monthly("kimi-k3", 2, 5))                # Long context, less volume
print(calc_monthly("ernie-tiny-8k", 50, 10))         # High-volume classification
# deepseek-v4-pro: $2,350.10/month ($78.34/day)
# kimi-k3: $861.00/month ($28.70/day)
# ernie-tiny-8k: $1.56/month ($0.05/day)

This shows the dramatic cost differences between models. A classification-heavy workload on ERNIE Tiny costs $1.56/month vs $2,350 for DeepSeek V4 Pro. See our full cost reduction guide.

Cost Optimization Strategies

  1. Use prompt caching — System prompts are identical across requests. Cache hits reduce input costs by 50-80%. DeepSeek V4 Pro cache hits at $1.914 vs $5.742 standard input.
  2. Model routing — Use intelligent routing to send simple tasks to Flash models and complex tasks to Pro models.
  3. Token budgeting — Set max_tokens limits appropriate to your task. Over-allocating wastes money. See our cost reduction guide.
  4. Batch processing — Group similar requests together to maximize cache hit rates.
  5. Monitor usageAIWave dashboard shows per-model spending in real time.

What Affects Your Bill Most

The biggest cost driver is output tokens, not input. A typical application with a 500-token input and 2000-token output pays 4x more for output. This is why choosing models with low output pricing (like DeepSeek V4 Pro at $1.914 vs GPT-4o at $5.742) has an outsized impact on your bill.

Try It Yourself

All models listed here are available with prepaid credits on signup through a single OpenAI-compatible API. See full pricing page for every model.

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What the numbers actually are today

Pricing pages age badly, so these figures are read from the live endpoint rather than typed by hand. The most cost-effective models on this platform are priced in fractions of a cent per million tokens — a range where the arithmetic of what you can afford to build changes qualitatively.

The ten most cost-effective models currently served, by input price (2026-07-26):

Model IDInput / 1M tokensOutput / 1M tokens
glm-4.7-flash$0.00$0.00
ernie-char-8k$0.0006$0.0006
ernie-char-fiction-8k$0.0006$0.0006
ernie-lite-8k$0.0006$0.0006
ernie-novel-8k$0.0006$0.0006
ernie-speed-8k$0.0006$0.0006
ernie-4.0-turbo-8k$0.0012$0.0012
ernie-4.0-turbo-8k-latest$0.0012$0.0012
ernie-4.0-turbo-8k-preview$0.0012$0.0012
ernie-3.5-8k$0.0018$0.0018

Rates read from the AIWave pricing endpoint on 2026-07-26. Check live pricing before budgeting — providers revise rates.

How to compare providers honestly

Most published comparisons are wrong within a month, and many were wrong on publication because they compared a headline rate against a different provider’s blended rate. Three rules make your own comparison reliable:

The formula

monthly_cost = (input_tokens  / 1_000_000) * input_price
             + (output_tokens / 1_000_000) * output_price

Pull your real token totals from your current provider’s usage dashboard and substitute. That single multiplication is worth more than any benchmark table, because it uses your actual traffic rather than someone else’s assumptions.

Where the savings actually come from

In practice, four levers dominate, roughly in order of impact:

  1. Routing by difficulty — most requests do not need your best model.
  2. Trimming context — conversation history and retrieved chunks are resent on every call.
  3. Batching — one request handling twenty items sends the instructions once.
  4. Caching — exact repeats should never reach the API twice.

Model choice matters, but it is frequently the smallest of the four. A team that switches models without addressing context bloat usually finds the saving disappointing. See AI API cost optimisation for the implementation of each.