MMLU, HumanEval, MATH, GSM8K - AI benchmarks explained in plain English. What they measure, why they matter, and how to interpret them.
The AI Model Benchmark Guide: What the Numbers Actually Mean
Every AI announcement comes with benchmarks. Claude scores 88.7% on MMLU. GPT-4 achieves 92% on HumanEval. Gemini reaches 90% on MATH.
What do these numbers actually mean? Should you care?
This guide explains the major benchmarks in plain English.
Why Benchmarks Exist
AI models are hard to evaluate. Unlike software that either works or crashes, AI outputs exist on a spectrum of quality.
Benchmarks attempt to standardize evaluation: give every model the same questions, measure performance, compare.
The problem: Models can be optimized for specific benchmarks without improving general capability. A high score doesn't always mean a better model for your use case. When evaluating models like those in our Claude vs GPT-4 vs Gemini comparison, real-world testing often matters more than benchmark scores.
The value: Benchmarks provide a rough comparison point. They're imperfect but better than nothing.
The Major Benchmarks Explained
MMLU (Massive Multitask Language Understanding)
What it measures: General knowledge across 57 subjects.
How it works: Multiple-choice questions covering everything from elementary math to professional law, medicine, history, science, and more.
Example question:
The longest river in Africa is:
A) Nile
B) Congo
C) Niger
D) Zambezi
Score interpretation:
- 90%+: Exceptional broad knowledge
- 80-90%: Very strong
- 70-80%: Competent
- <70%: Significant gaps
Why it matters: Indicates how much "stuff" the model knows. Useful if you need an AI that can discuss diverse topics.
Why it might not matter: Knowledge doesn't equal reasoning. A model might know facts but fail at applying them.
Current scores (2024-2025):
- Claude 3.5 Sonnet: ~88%
- GPT-4o: ~88%
- Gemini 2.0: ~90%
HumanEval
What it measures: Code generation ability.
How it works: 164 programming problems. The model writes Python code, which is then run against test cases.
Example task:
Write a function that takes a list of numbers and returns only the even ones.
Model generates code. Tests check if it works.
Score interpretation:
- 90%+: Excellent coder
- 80-90%: Strong
- 70-80%: Capable with limitations
- <70%: Unreliable for coding
Why it matters: Direct measure of coding capability. If you use AI for programming, this benchmark matters.
Why it might not matter: HumanEval problems are self-contained. Real coding involves context, existing codebases, and ambiguous requirements—none of which HumanEval tests.
Current scores:
- Claude 3.5 Sonnet: ~92%
- GPT-4o: ~90%
- Gemini 2.0: ~89%
These scores help explain why Claude dominates AI coding assistant rankings—it genuinely writes better code.
MATH
What it measures: Mathematical problem-solving.
How it works: 12,500 competition math problems across difficulty levels (algebra, geometry, calculus, etc.).
Example problem:
Find all real numbers x such that x² - 5x + 6 = 0
Score interpretation:
- 80%+: Strong mathematical reasoning
- 60-80%: Decent math ability
- 40-60%: Limited to basic problems
- <40%: Unreliable for math
Why it matters: Tests actual reasoning, not just pattern matching. Math problems require step-by-step logic.
Why it might not matter: Competition math is very different from practical math. High MATH scores don't guarantee good financial modeling or data analysis.
Current scores:
- Claude 3.5 Sonnet: ~71%
- GPT-4o: ~76%
- Gemini 2.0: ~78%
GSM8K (Grade School Math 8K)
What it measures: Basic mathematical reasoning with word problems.
How it works: 8,500 grade school word problems requiring multi-step arithmetic.
Example problem:
Sarah has 5 apples. She gives 2 to John and buys 3 more. How many apples does Sarah have now?
Score interpretation:
- 95%+: Excellent basic reasoning
- 85-95%: Strong
- <85%: Concerning gaps in basic logic
Why it matters: Unlike MATH, these are practical problems. If a model fails GSM8K, it struggles with basic reasoning.
Why it might not matter: The problems are formulaic. High scores might reflect pattern recognition rather than genuine understanding.
Current scores:
- Claude 3.5 Sonnet: ~96%
- GPT-4o: ~96%
- Gemini 2.0: ~95%
GPQA (Graduate-Level Google-Proof Q&A)
What it measures: Expert-level reasoning that can't be Googled.
How it works: Questions written by PhD experts that require genuine understanding, not retrieval.
Example area: Organic chemistry mechanisms, quantum physics concepts, advanced biology.
Why it matters: Tests if the model actually "understands" or is just pattern matching against its training data.
Why it might not matter: Most users don't need PhD-level expertise. This benchmark matters for research applications, less for business use.
HellaSwag
What it measures: Common sense reasoning.
How it works: Complete-the-sentence tasks requiring understanding of everyday situations.
Example:
A woman is juggling three balls. She drops one. She then...
A) picks it up and continues
B) starts singing
C) orders a pizza
D) calls her lawyer
Why it matters: Tests whether the model has intuitive understanding of how the world works.
Why it might not matter: Models have nearly saturated this benchmark (95%+). It no longer differentiates between top models.
MT-Bench
What it measures: Multi-turn conversation quality.
How it works: Judges evaluate model responses across two-turn conversations on various tasks.
Why it matters: More realistic than single-question benchmarks. Tests how models handle follow-ups and context. This becomes especially important for AI agents that need to maintain context across multiple interactions.
Why it might not matter: Scoring is subjective. Different judges rate differently.
Benchmarks That Don't Exist (But Should)
Real-World Task Completion
Does the model actually help users accomplish goals? No standardized benchmark exists.
Instruction Following Precision
How well does the model stick to specific formatting/constraint requirements? Partially measured but not standardized.
Long-Document Reliability
Performance on 50K+ token contexts with detail retention? Underexplored.
Production Code Quality
Not just "does it work" but "is it maintainable, secure, idiomatic"? Not systematically measured.
How to Actually Interpret Benchmarks
1. Ignore Small Differences
88% vs 90% on MMLU? Meaningless in practice. The margin of error and day-to-day variance exceeds this.
Only care about differences of 5%+ on major benchmarks.
2. Match Benchmarks to Your Use Case
Writing code? HumanEval matters.
Analyzing documents? MMLU is relevant.
Math-heavy work? MATH and GSM8K matter.
General chat? Most benchmarks are less relevant than actual conversation feel.
3. Recognize Gaming
Companies optimize for benchmarks. A model might score high on MMLU while having gaps in practical knowledge not covered by the test.
4. Test Yourself
The best benchmark is your own evaluation. Try each model on YOUR actual tasks. What works best for YOU is what matters.
5. Check Multiple Benchmarks
No single benchmark tells the full story. A model weak in one area might excel in another.
The Benchmark Arms Race Problem
Every time a new benchmark emerges, companies optimize for it. Benchmarks become less meaningful as models specifically train to pass them.
This is why:
- New benchmarks constantly appear
- Old benchmarks get "saturated" (all models score 95%+)
- Companies report only benchmarks where they excel
What Actually Matters for Choosing a Model
Beyond benchmarks, consider:
- Speed: How fast does it respond?
- Cost: What's the API pricing?
- Context length: How much text can it handle?
- Reliability: How often does it fail or refuse?
- Feel: Does the conversation feel natural?
- Ecosystem: What integrations exist?
These factors often matter more than benchmark scores.
The Bottom Line
Benchmarks are useful for:
- Rough comparisons between models
- Tracking progress over time
- Identifying specific strengths/weaknesses
Benchmarks are not useful for:
- Determining the "best" model (depends on use case)
- Small percentage differences
- Predicting real-world performance precisely
Use benchmarks as one input among many. Your own testing on your actual tasks beats any published number.
The best model is the one that works best for what you're doing. Benchmarks are a starting point, not the answer.
Frequently Asked Questions
What is MMLU and what does it measure?
MMLU (Massive Multitask Language Understanding) measures an AI model's general knowledge across 57 subjects through multiple-choice questions. It covers everything from elementary math to professional law, medicine, history, and science. Scores above 90% indicate exceptional broad knowledge, while scores below 70% suggest significant knowledge gaps.
What is HumanEval benchmark for AI coding?
HumanEval is a benchmark that measures AI code generation ability using 164 programming problems. The AI writes Python code that's tested against automated test cases. Scores above 90% indicate excellent coding capability, but HumanEval only tests self-contained problems—it doesn't measure performance on real codebases with complex context.
How do AI benchmarks work?
AI benchmarks standardize evaluation by giving every model the same questions or tasks, measuring performance, and comparing results. They attempt to quantify capabilities like knowledge (MMLU), coding (HumanEval), math reasoning (MATH, GSM8K), and common sense (HellaSwag). However, benchmarks can be gamed and don't always reflect real-world performance.
Are AI benchmark scores reliable for choosing a model?
Benchmark scores are useful for rough comparisons but shouldn't be your only criterion. They're imperfect—companies optimize specifically for benchmarks, small differences (2-3%) are meaningless, and benchmarks don't test real-world factors like speed, cost, reliability, or conversation feel. Test models on your actual tasks for the best evaluation.
What's the difference between MATH and GSM8K benchmarks?
MATH tests competition-level mathematical problem-solving with 12,500 problems across algebra, geometry, and calculus—it's challenging even for strong models. GSM8K tests basic mathematical reasoning using 8,500 grade school word problems requiring multi-step arithmetic. GSM8K is more practical but formulaic, while MATH tests deeper reasoning ability.
Why do AI companies report different benchmarks?
Companies selectively report benchmarks where they perform best. This is why you'll see one company emphasizing MMLU while another highlights HumanEval—each showcases their strengths. This benchmark cherry-picking makes it crucial to look at multiple benchmarks and test models yourself rather than relying on marketing claims.
The Bottom Line
Benchmarks are useful for rough comparisons but deeply flawed as absolute measures. They're gamed, narrow, and don't capture what matters most for real-world use.
Use benchmarks to narrow your choices, then test models on your actual tasks. That's the only evaluation that counts.
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