GoldBean

Posted on Jul 31

2026 Chinese LLM API Benchmark: 7 Models Ranked by Real Data (July Update)

#llm #api #benchmark

2026 Chinese LLM API Benchmark: 7 Models Tested with Real Data

DeepSeek-v4-pro leads with 81.1 overall score. Kimi K2.6 Thinking dominates hallucination control at 90.0. Doubao Seed2.0-pro tops code generation at 85.7. But no single model covers all scenarios.

Why Benchmark Now?

The Chinese LLM market has shifted from "hundred flowers blooming" to "scenario differentiation". By early 2026, hundreds of models completed regulatory filing, but only 7-8 providers offer truly production-grade API services.

The real pain: Same algorithm problem, 8 models give 8 different answers. Some respond in 0.5s, others timeout at 60s. Cost per million tokens ranges from ¥15 to ¥150.

This benchmark is based on Bonree Data's May 2026 report covering 1900+ real-world API calls.

Overall Scores (Bonree Data 2026.05)

Model	Overall	Code	Math	Planning	Hallucination	Price (¥/M tokens)
DeepSeek-v4-pro	81.1	85	82	80	78	15
Kimi K2.6 Thinking	78.5	80	75	72	90.0	60
Doubao Seed2.0-pro	76.2	85.7	70	75	74	50
Qwen3.5	74.8	78	76	73	72	20
Hunyuan-large	72.5	75	70	71	73	30
ERNIE 4.5	70.1	72	68	70	71	40
GLM-4	68.9	70	65	69	70	25

Overall score = average of 4 core scenarios, max 100

Key Findings

Finding 1: No Universal Champion

Every model has clear strengths and weaknesses. DeepSeek is balanced but not best-in-class; Kimi dominates hallucination control but scores only 72 in task planning; Doubao leads code generation but drops to 70 in math reasoning.

Finding 2: Token Efficiency Varies 3-5x

Same prompt, different models consume 3-5x different token counts. Total cost = unit price × token volume. DeepSeek's efficiency means its real cost is often lower than cheaper alternatives.

Finding 3: Response Speed Tiers

Tier 1 (< 0.5s TTFT): DeepSeek, Doubao
Tier 2 (0.5-2s): Qwen, GLM
Tier 3 (> 3s or timeout): Some edge cases

For real-time chat, >2s is a hard UX red line.

Model Profiles

DeepSeek-v4-pro (81.1 overall, Best Value)

Architecture: ~1T params MoE, ~370B active
Context: 256K
Strengths: Code (85), Math (82), Long text
Weaknesses: Hallucination control (78) behind Kimi
Price: ¥15/M tokens — open source MIT
Best for: Budget-sensitive projects needing full-scenario coverage

Kimi K2.6 Thinking (90.0 hallucination control)

Context: 256K
Strengths: Hallucination control (90), Long documents, Fact checking
Weaknesses: Task planning (72), Slower response
Price: ¥60/M tokens
Best for: Finance, medical, legal — scenarios demanding high factual accuracy

Doubao Seed2.0-pro (85.7 code generation)

Context: 262K
Strengths: Code completion, Code review, Bug fixing
Weaknesses: Math reasoning (70), General chat
Price: ¥50/M tokens
Best for: IDE plugins, Code review tools, Technical Q&A

Unified Gateway: Why Not Just Pick One?

Managing 7 API keys, 7 SDKs, and 7 billing dashboards is painful. A unified gateway lets you:

// Call DeepSeek
fetch('https://goldbean-api.xyz/ai/ds/chat', {
  headers: { 'Authorization': 'Bearer KEY' },
  body: JSON.stringify({
    model: 'deepseek-v4-pro',
    messages: [{ role: 'user', content: 'Hello' }]
  })
});

// Switch to Kimi — only change path and model
fetch('https://goldbean-api.xyz/ai/kimi/chat', {
  ...sameConfig,
  model: 'kimi-k2.6'
});

GoldBean provides:

One API key for 7 providers
OpenAI-compatible endpoints
25-40% cheaper than official direct pricing
Built-in playground for side-by-side comparison
Free tier: 20 calls/day

Try the Playground

Compare models live without writing code:
https://goldbean-api.xyz/playground

Data Source

Bonree Data, China Mainstream Large Model API Service Performance and Comprehensive Evaluation Report, May 2026. 1900+ real environment calls across code generation, math reasoning, task planning, and hallucination control.

What's your experience with Chinese LLM APIs? Any model surprised you?

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