AI Model Evaluation: How to Test and Compare Chinese LLM Performance (2026)

August 14, 2026 · 19 min read

Choosing the wrong AI model can cost you 10x more in latency and expenses while delivering inferior results. This guide shows you how to systematically evaluate Chinese AI models — DeepSeek, GLM-4, Qwen, Kimi, Doubao — using production-tested benchmarks and evaluation frameworks.

Why Model Evaluation Matters

Public benchmarks like MMLU and C-Eval tell only part of the story. Your application has specific requirements:

• Latency-sensitive apps need fast models, not just accurate ones
• Cost-constrained products need the best performance per dollar
• Domain-specific tasks may favor models that excel in narrow areas
• Multilingual apps need models strong in your target languages

Evaluation Dimensions

1. Building Your Evaluation Dataset

# Example: Customer support evaluation dataset
eval_dataset = [
    {
        "id": "cs_001",
        "category": "refund_request",
        "input": "I ordered a laptop last week but it arrived damaged. Can I get a refund?",
        "expected_output": {
            "action": "initiate_refund",
            "tone": "empathetic",
            "key_elements": ["acknowledge_damage", "refund_policy", "next_steps"]
        },
        "difficulty": "easy"
    },
    {
        "id": "cs_002",
        "category": "technical_issue",
        "input": "My app crashes when I click the profile tab. I'm on iOS 17.2.",
        "expected_output": {
            "action": "troubleshoot",
            "tone": "technical",
            "key_elements": ["acknowledge_issue", "diagnostic_steps", "escalation_path"]
        },
        "difficulty": "medium"
    },
    {
        "id": "cs_003",
        "category": "complex_policy",
        "input": "I bought a subscription with my old company email. I left the company and lost access. Can you transfer it?",
        "expected_output": {
            "action": "verify_identity",
            "tone": "professional",
            "key_elements": ["acknowledge_situation", "security_verification", "transfer_process"]
        },
        "difficulty": "hard"
    }
]

# Guidelines for good eval datasets:
# - Cover edge cases, not just happy paths
# - Include examples at different difficulty levels
# - Represent your actual user distribution
# - Update quarterly as user behavior changes

2. Automated Evaluation Framework

import requests
import json
import time
from typing import List, Dict
from dataclasses import dataclass

TOKEN = "your_tokenease_api_key"
BASE_URL = "https://tokenease.io/v1"

@dataclass
class EvalResult:
    model: str
    test_id: str
    latency_ms: float
    tokens_used: int
    cost_usd: float
    output: str
    scores: Dict[str, float]

class ModelEvaluator:
    def __init__(self, models: List[str]):
        self.models = models
        self.results = []
    
    def evaluate_all(self, test_cases: List[dict], system_prompt: str = ""):
        """Run all models against all test cases"""
        for model in self.models:
            print(f"Evaluating {model}...")
            for test in test_cases:
                result = self.run_single_test(model, test, system_prompt)
                self.results.append(result)
        
        return self.generate_report()
    
    def run_single_test(self, model: str, test: dict, system_prompt: str) -> EvalResult:
        """Run a single test case"""
        messages = []
        if system_prompt:
            messages.append({"role": "system", "content": system_prompt})
        messages.append({"role": "user", "content": test["input"]})
        
        start = time.time()
        
        try:
            response = requests.post(
                f"{BASE_URL}/chat/completions",
                headers={"Authorization": f"Bearer {TOKEN}"},
                json={
                    "model": model,
                    "messages": messages,
                    "temperature": 0.3,
                    "max_tokens": 1024
                }
            )
            
            latency = (time.time() - start) * 1000
            data = response.json()
            output = data["choices"][0]["message"]["content"]
            tokens = data.get("usage", {}).get("total_tokens", 0)
            
            # Calculate cost
            cost = self.calculate_cost(model, tokens)
            
            # Score the output
            scores = self.score_output(test, output)
            
            return EvalResult(
                model=model,
                test_id=test["id"],
                latency_ms=latency,
                tokens_used=tokens,
                cost_usd=cost,
                output=output,
                scores=scores
            )
            
        except Exception as e:
            return EvalResult(
                model=model,
                test_id=test["id"],
                latency_ms=(time.time() - start) * 1000,
                tokens_used=0,
                cost_usd=0,
                output="",
                scores={"error": 1.0, "overall": 0.0}
            )
    
    def score_output(self, test: dict, output: str) -> Dict[str, float]:
        """Score output against expected criteria"""
        scores = {}
        
        # Exact match for action (binary)
        if "expected_output" in test:
            expected = test["expected_output"]
            
            # Check key elements presence (using simple string matching)
            if "key_elements" in expected:
                element_scores = []
                for element in expected["key_elements"]:
                    # Use LLM-as-judge for semantic matching
                    element_scores.append(
                        self.semantic_match(element, output)
                    )
                scores["element_coverage"] = sum(element_scores) / len(element_scores)
            
            # Check tone (using another LLM call)
            if "tone" in expected:
                scores["tone_match"] = self.check_tone(expected["tone"], output)
        
        # Length appropriateness
        input_len = len(test["input"])
        output_len = len(output)
        scores["length_ratio"] = min(output_len / (input_len * 2), 1.0)
        
        # Overall score
        scores["overall"] = sum(scores.values()) / len(scores) if scores else 0.5
        
        return scores
    
    def semantic_match(self, concept: str, text: str) -> float:
        """Check if concept is semantically present in text"""
        prompt = f"Does the following text address the concept '{concept}'? Reply with a number 0-1.\n\nText: {text[:500]}\n\nScore:"
        
        try:
            response = requests.post(
                f"{BASE_URL}/chat/completions",
                headers={"Authorization": f"Bearer {TOKEN}"},
                json={
                    "model": "kimi-k2",
                    "messages": [{"role": "user", "content": prompt}],
                    "max_tokens": 10,
                    "temperature": 0.1
                }
            )
            return float(response.json()["choices"][0]["message"]["content"])
        except:
            return 0.5
    
    def check_tone(self, expected_tone: str, text: str) -> float:
        """Check if text matches expected tone"""
        prompt = f"Rate how well this text matches a '{expected_tone}' tone (0-1):\n\n{text[:300]}\n\nScore:"
        
        try:
            response = requests.post(
                f"{BASE_URL}/chat/completions",
                headers={"Authorization": f"Bearer {TOKEN}"},
                json={
                    "model": "kimi-k2",
                    "messages": [{"role": "user", "content": prompt}],
                    "max_tokens": 10,
                    "temperature": 0.1
                }
            )
            return float(response.json()["choices"][0]["message"]["content"])
        except:
            return 0.5
    
    def calculate_cost(self, model: str, tokens: int) -> float:
        pricing = {
            "deepseek-v4": 0.0005,
            "glm-4": 0.0007,
            "kimi-k2": 0.0003,
            "qwen-max": 0.0005,
            "doubao-pro": 0.0004
        }
        return tokens / 1000 * pricing.get(model, 0.0005)
    
    def generate_report(self) -> dict:
        """Generate evaluation report"""
        report = {"models": {}}
        
        for model in self.models:
            model_results = [r for r in self.results if r.model == model]
            
            report["models"][model] = {
                "avg_latency_ms": sum(r.latency_ms for r in model_results) / len(model_results),
                "avg_tokens": sum(r.tokens_used for r in model_results) / len(model_results),
                "total_cost": sum(r.cost_usd for r in model_results),
                "error_rate": sum(1 for r in model_results if "error" in r.scores) / len(model_results),
                "avg_score": sum(r.scores.get("overall", 0) for r in model_results) / len(model_results),
                "scores_by_dimension": self.aggregate_scores(model_results)
            }
        
        return report
    
    def aggregate_scores(self, results: List[EvalResult]) -> dict:
        """Aggregate scores by dimension"""
        dimensions = {}
        for r in results:
            for dim, score in r.scores.items():
                if dim not in dimensions:
                    dimensions[dim] = []
                dimensions[dim].append(score)
        
        return {dim: sum(scores) / len(scores) for dim, scores in dimensions.items()}

# Run evaluation
models = ["deepseek-v4", "glm-4", "kimi-k2", "qwen-max"]
evaluator = ModelEvaluator(models)
report = evaluator.evaluate_all(eval_dataset)

print(json.dumps(report, indent=2))

3. Latency Benchmarking

def benchmark_latency(models: List[str], prompt: str, iterations: int = 50):
    """Benchmark latency across models"""
    
    results = {}
    
    for model in models:
        latencies = []
        ttfts = []  # Time to first token
        
        for _ in range(iterations):
            start = time.time()
            
            response = requests.post(
                f"{BASE_URL}/chat/completions",
                headers={"Authorization": f"Bearer {TOKEN}"},
                json={
                    "model": model,
                    "messages": [{"role": "user", "content": prompt}],
                    "stream": True,
                    "max_tokens": 500
                },
                stream=True
            )
            
            first_token_time = None
            for line in response.iter_lines():
                if line and first_token_time is None:
                    first_token_time = time.time()
                    ttfts.append((first_token_time - start) * 1000)
            
            total_time = (time.time() - start) * 1000
            latencies.append(total_time)
        
        latencies.sort()
        ttfts.sort()
        n = len(latencies)
        
        results[model] = {
            "ttft_p50": ttfts[n // 2],
            "ttft_p95": ttfts[int(n * 0.95)],
            "total_p50": latencies[n // 2],
            "total_p95": latencies[int(n * 0.95)],
            "total_p99": latencies[int(n * 0.99)]
        }
    
    return results

# Example results:
# {
#   "deepseek-v4": {"ttft_p50": 245, "total_p50": 3200},
#   "glm-4": {"ttft_p50": 310, "total_p50": 3800},
#   "kimi-k2": {"ttft_p50": 180, "total_p50": 2100}
# }

4. Long-Context Evaluation (Needle-in-Haystack)

def needle_in_haystack_test(model: str, context_lengths: List[int]):
    """Test long-context retrieval accuracy"""
    
    results = []
    
    for length in context_lengths:
        # Generate context with hidden needle
        needle = "The secret code is BLUE-42."
        haystack = generate_random_text(length - len(needle))
        
        # Insert needle at random position
        insert_pos = random.randint(0, len(haystack))
        context = haystack[:insert_pos] + needle + haystack[insert_pos:]
        
        # Ask model to retrieve needle
        prompt = f"{context}\n\nWhat is the secret code mentioned above?"
        
        response = requests.post(
            f"{BASE_URL}/chat/completions",
            headers={"Authorization": f"Bearer {TOKEN}"},
            json={
                "model": model,
                "messages": [{"role": "user", "content": prompt}],
                "temperature": 0.1
            }
        )
        
        output = response.json()["choices"][0]["message"]["content"]
        correct = "BLUE-42" in output
        
        results.append({
            "context_length": length,
            "correct": correct,
            "output": output[:100]
        })
    
    return results

# Test at different context lengths
context_lengths = [1000, 4000, 8000, 16000, 32000, 64000, 128000]
for model in ["deepseek-v4", "glm-4"]:
    print(f"\nTesting {model}:")
    results = needle_in_haystack_test(model, context_lengths)
    for r in results:
        status = "PASS" if r["correct"] else "FAIL"
        print(f"  {r['context_length']:>6} tokens: {status}")

5. Cost-Performance Analysis

def cost_performance_analysis(report: dict):
    """Calculate best model per dollar"""
    
    analysis = {}
    
    for model, stats in report["models"].items():
        score = stats["avg_score"]
        cost = stats["total_cost"]
        latency = stats["avg_latency_ms"]
        
        # Performance per dollar
        if cost > 0:
            score_per_dollar = score / cost
        else:
            score_per_dollar = 0
        
        # Performance per second
        if latency > 0:
            score_per_second = score / (latency / 1000)
        else:
            score_per_second = 0
        
        analysis[model] = {
            "score": score,
            "cost": cost,
            "latency_ms": latency,
            "score_per_dollar": score_per_dollar,
            "score_per_second": score_per_second,
            "efficiency_rating": "high" if score_per_dollar > 100 else "medium" if score_per_dollar > 50 else "low"
        }
    
    return analysis

# Find best value model
analysis = cost_performance_analysis(report)
best_value = max(analysis.items(), key=lambda x: x[1]["score_per_dollar"])
print(f"Best value: {best_value[0]} (${best_value[1]['cost']:.4f} for score {best_value[1]['score']:.2f})")

Benchmark Results Summary (August 2026)

When to Choose Which Model

Continuous Evaluation Pipeline

# Run weekly evaluation and alert on regression
import schedule
import time

def weekly_evaluation():
    """Run full evaluation suite weekly"""
    
    # Load latest test cases
    test_cases = load_test_cases()
    
    # Run evaluation
    evaluator = ModelEvaluator(MODELS)
    new_report = evaluator.evaluate_all(test_cases)
    
    # Compare with baseline
    baseline = load_baseline_report()
    
    for model in MODELS:
        new_score = new_report["models"][model]["avg_score"]
        old_score = baseline["models"][model]["avg_score"]
        
        regression = (old_score - new_score) / old_score
        
        if regression > 0.05:  # 5% regression
            send_alert(f"Model {model} regressed by {regression:.1%}")
        elif regression < -0.05:  # 5% improvement
            send_alert(f"Model {model} improved by {abs(regression):.1%}")
    
    # Save new baseline
    save_baseline_report(new_report)

# Schedule weekly run
schedule.every().monday.at("02:00").do(weekly_evaluation)

while True:
    schedule.run_pending()
    time.sleep(60)

Next Steps

1. Build a dataset of 50-100 examples from your actual use case
2. Run the evaluation framework against all candidate models
3. Analyze cost-performance to find your optimal model
4. Set up continuous evaluation to catch model regressions
5. Get your TokenEase API key to test all models with one account

For deployment patterns, see our guides on A/B testing AI models and failover and load balancing.

Last updated: August 2026. Benchmark scores reflect current model versions.