LLM Benchmarks Explained
Understanding how language models are evaluated and compared
GSM8K • HellaSwag • MMLU • HumanEval • TruthfulQA • More
📊 Benchmark Quick Reference
GSM8K - Math Reasoning Benchmark
Grade School Math 8K • 8,500 grade school math word problems
What is GSM8K?
GSM8K (Grade School Math 8K) is a dataset of 8,500 high-quality grade school math word problems created by human problem writers. Each problem requires between 2 to 8 steps to solve and involves basic arithmetic operations. The benchmark tests a model's ability to understand natural language math problems, break them down into steps, and perform accurate calculations.
📝 Example GSM8K Problem
Question: "Janet's ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?"
Answer: $18 (16 - 3 - 4 = 9 eggs × $2 = $18)
Top GSM8K Performance (2025)
| Model | GSM8K Score | Organization | Size |
|---|---|---|---|
| GPT-4 Turbo | OpenAI | Unknown | |
| Claude 3 Opus | Anthropic | Unknown | |
| Gemini 1.5 Pro | Unknown | ||
| Llama 3.1 70B | Meta | 70B | |
| Mistral Large | Mistral AI | Unknown | |
| Llama 3.1 8B | Meta | 8B |
Why GSM8K Matters
- Real-world reasoning: Tests multi-step problem solving similar to real tasks
- Chain-of-thought: Reveals how well models can break down complex problems
- Math applications: Critical for finance, science, engineering use cases
- Deployment indicator: High GSM8K scores correlate with better instruction-following
HellaSwag - Commonsense Reasoning
70,000 multiple-choice questions testing natural reasoning
What is HellaSwag?
HellaSwag (Harder Endings, Longer contexts, and Low-shot Activities for Situations With Adversarial Generations) is a benchmark designed to test commonsense natural language inference. It contains 70,000 multiple-choice questions where models must predict the most likely continuation of a given scenario. The dataset is specifically designed to be easy for humans (95%+ accuracy) but challenging for models.
📝 Example HellaSwag Problem
Context: "A woman is outside with a bucket and a dog. The dog is running around trying to avoid a bath. She..."
Options:
- A) rinses the bucket off with soap and blow dries the dog's head.
- B) uses a hose to keep it from getting soapy.
- C) grabs the dog and holds it still. ✓ Correct
- D) gets into the bath tub with the dog.
Top HellaSwag Performance (2025)
| Model | HellaSwag Score | Organization | Human Baseline |
|---|---|---|---|
| GPT-4 Turbo | OpenAI | ~95.6% | |
| Claude 3.5 Sonnet | Anthropic | ||
| Llama 3.1 70B | Meta | ||
| Gemini 1.5 Pro | |||
| Mistral 7B | Mistral AI | ||
| Llama 3.1 8B | Meta |
Why HellaSwag Matters
- Real-world understanding: Tests how well models understand everyday situations
- Chatbot quality: High scores indicate better conversational abilities
- Context awareness: Measures ability to understand implicit information
- Deployment readiness: Critical for customer-facing applications
MMLU - Massive Multitask Language Understanding
57 subjects spanning STEM, humanities, social sciences, and more
What is MMLU?
MMLU (Massive Multitask Language Understanding) is a comprehensive benchmark that tests a model across 57 different subjects including elementary mathematics, US history, computer science, law, and more. It contains ~16,000 multiple-choice questions spanning knowledge from elementary level to professional level. MMLU is considered one of the most thorough benchmarks for measuring general knowledge and reasoning capabilities.
MMLU Subject Categories
🔬 STEM
Physics, Chemistry, Biology, Mathematics, Computer Science, Engineering
📖 Humanities
Philosophy, History, Literature, Religion, Ethics, Art
🏛️ Social Sciences
Psychology, Economics, Law, Politics, Geography, Sociology
📊 Other
Business, Health, Misc. Professional Topics
Top MMLU Performance (2025)
| Model | MMLU Score (57 subjects avg) | Organization |
|---|---|---|
| GPT-4 Turbo | OpenAI | |
| Claude 3 Opus | Anthropic | |
| Gemini 1.5 Pro | ||
| Llama 3.1 70B | Meta | |
| Mistral Large | Mistral AI | |
| Llama 3.1 8B | Meta |
Why MMLU Matters
- Broad knowledge assessment: Tests across diverse domains like a comprehensive exam
- Professional applications: Indicates readiness for specialized use cases (legal, medical, etc.)
- General intelligence proxy: High MMLU scores suggest strong overall capabilities
- Educational applications: Critical for tutoring, research assistance, content creation
Other Important Benchmarks
💻 HumanEval
CodingTests code generation abilities with 164 programming problems. Models must write Python functions that pass unit tests.
Top Score: GPT-4 Turbo (90%)
Best Open Source: CodeLlama 70B (67%)
✅ TruthfulQA
TruthfulnessMeasures whether models generate truthful answers, specifically testing resistance to common misconceptions and false beliefs.
Top Score: Claude 3 Opus (87%)
Baseline: Human experts (~94%)
🎓 ARC
ReasoningAI2 Reasoning Challenge - 7,787 science exam questions requiring complex reasoning beyond simple fact recall.
Top Score: GPT-4 (96%)
Best Open Source: Llama 3.1 70B (85%)
🧩 BIG-Bench Hard
Multi-stepCollection of 23 challenging tasks from BIG-Bench where previous models performed below human level.
Top Score: GPT-4 Turbo (89%)
Focus: Multi-step reasoning
How to Choose Models Based on Benchmarks
For Math & Finance Applications
Prioritize: GSM8K (math reasoning), MMLU Math (advanced math)
Recommended: GPT-4 Turbo (95% GSM8K), Claude 3 Opus (93%), Llama 3.1 70B (89%)
For Chatbots & Customer Service
Prioritize: HellaSwag (common sense), TruthfulQA (accuracy)
Recommended: Claude 3.5 Sonnet, GPT-4 Turbo, Gemini 1.5 Pro
For Code Generation
Prioritize: HumanEval (Python coding), MBPP (code reasoning)
Recommended: GPT-4 Turbo (90%), CodeLlama 70B (67%), Claude 3.5 Sonnet
For General Knowledge & Research
Prioritize: MMLU (broad knowledge), ARC (science reasoning)
Recommended: Claude 3 Opus (86.8% MMLU), GPT-4 Turbo (86.4%), Gemini 1.5 Pro
Find Models by Benchmark Performance
Compare 2,800+ models with detailed benchmark scores and deployment guides
Frequently Asked Questions
Are benchmark scores the only factor in choosing a model?
No. Also consider: latency, cost, context window, API availability, deployment complexity, and specific use case requirements. Benchmarks measure capabilities but not necessarily real-world performance for your specific task.
Why do some models score lower on benchmarks but perform better in practice?
Benchmarks test specific, narrow tasks. A model might excel at your use case (e.g., creative writing, specific domain knowledge) even with lower benchmark scores. Always test with your actual data.
How often are these benchmarks updated?
Model scores are updated as new models are released (monthly). Some benchmarks themselves evolve to avoid "teaching to the test" and maintain validity as models improve.
What's the best overall benchmark for deployment decisions?
MMLU is often considered the best single indicator of general capability, but combine it with task-specific benchmarks (GSM8K for math, HumanEval for coding, etc.) for the most informed decision.