LLM Benchmark Deep Dive: Let the Data Speak
When choosing an LLM, understanding its performance across standard benchmarks is crucial. This article analyzes mainstream evaluations to help you make informed choices.
Overview of Evaluation Suites
Authoritative Benchmarks
📚 MMLU
Massive Multitask Language Understanding
- • 57 academic subjects
- • 14,042 multiple-choice questions
- • Tests knowledge breadth and depth
- • Industry-standard benchmark
💻 HumanEval
Programming ability evaluation
- • 164 coding problems
- • Python function implementations
- • Tests code generation ability
- • Practical engineering metric
🎯 MT-Bench
Multi-turn dialogue evaluation
- • 80 multi-turn conversations
- • 8 categories
- • GPT-4 automated scoring
- • Utility-focused
🧮 GSM8K
Math reasoning evaluation
- • 8,792 math problems
- • Grade-school level word problems
- • Tests logical reasoning
- • Calculation accuracy
Overall Performance
Results for Main Models (2024)
| Model | MMLU | HumanEval | MT-Bench | GSM8K | Composite |
|---|---|---|---|---|---|
| GPT-4 | 86.4% | 67.0% | 9.18 | 92.0% | 95.2 |
| Claude 3 Opus | 84.9% | 64.7% | 9.05 | 88.7% | 92.8 |
| Gemini Ultra | 83.7% | 61.4% | 8.92 | 87.5% | 90.5 |
| Llama 3 70B | 79.5% | 55.3% | 8.45 | 82.1% | 85.3 |
| Wenxin Yiyan 4.0 | 78.2% | 52.8% | 8.31 | 79.6% | 83.7 |
| Tongyi Qianwen 2.0 | 76.8% | 50.2% | 8.15 | 77.3% | 81.2 |
* Composite scores are weighted across multiple metrics (0–100).
Focused Capability Evaluations
Performance by Dimension
🌐 Multilingual Ability
💡 Creative Writing
GPT-4
9.2/10
Imaginative
Claude 3
9.5/10
Elegant prose
Gemini
8.8/10
Clear logic
Real-world Scenario Tests
Performance on Practical Tasks
📝 Content Creation
💻 Software Development
Cost-effectiveness Analysis
Cost vs. Benefit
| Model | Price ($/1M tokens) | Performance | Value Index | Recommended Use |
|---|---|---|---|---|
| GPT-3.5-Turbo | $1.5 | 75 | 50.0 | Everyday chat, simple tasks |
| Claude 3 Haiku | $0.25 | 68 | 272.0 | High-volume processing |
| Llama 3 70B | $0.8 | 85 | 106.3 | Self-hosted deployments |
| GPT-4 | $30 | 95 | 3.2 | Complex reasoning, professional tasks |
* Value Index = Performance / Price (higher is better)
Methodology
How to Evaluate Models Properly
📏 Principles
- ✓
Standardized testing
Use well-known benchmark datasets
- ✓
Multi-dimensional evaluation
Test different capabilities separately
- ✓
Real-world validation
Evaluate against your business scenarios
⚠️ Caveats
- !
Overfitting risk
Models may be tuned for specific test sets
- !
Version variance
Different versions of the same model can vary
- !
Scenario fit
Good benchmark scores ≠ best in your use case
Model Selection Guidance
Scenario-based Recommendations
🏢 Enterprise applications
Recommended: GPT-4 / Claude 3 Opus
Reason: High accuracy, long-context support, stable APIs, strong compliance
💡 Innovative projects
Recommended: Open-source models (Llama 3, Mixtral)
Reason: Customizable, cost control, supports private deployments
🚀 Rapid prototyping
Recommended: GPT-3.5-Turbo / Claude 3 Haiku
Reason: Low cost, fast, easy integration
🌏 Chinese-language scenarios
Recommended: Wenxin Yiyan 4.0 / Tongyi Qianwen 2.0
Reason: Chinese optimization, localized understanding, strong compliance
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