What is a "Good" MMLU Score in 2026? The New Standard for AI Models
⚡ Quick Answer: The 2026 Benchmark Cheat Sheet
- The Baseline: In 2026, a "good" MMLU score starts at 85%. Anything lower is considered previous-generation technology.
- The Meaning: MMLU measures General Knowledge (like a trivia contest), not intelligence.
- The Trap: A model can score 90% on MMLU but still fail at writing code or solving logic puzzles.
- The Comparison: Think of MMLU as "High School GPA" and the new Humanity's Last Exam (HLE) as a "PhD Defense."
If you are reading the latest AI headlines, you are probably drowning in acronyms.
MMLU. HumanEval. HLE. CoT.
Marketing teams love to throw these numbers around to prove their AI is "smarter" than the competition. But what do they actually mean?
This guide is part of our extensive Live Leaderboard 2026: Gemini 3 Pro vs. DeepSeek vs. GPT-5. We built this to help you cut through the hype and understand the real numbers.
What Exactly is MMLU?
MMLU stands for Massive Multitask Language Understanding.
Launched in 2020, it was designed to test how much "world knowledge" an AI has.
It consists of over 16,000 multiple-choice questions across 57 subjects, including:
- Elementary Mathematics
- US History
- Computer Science
- Law
- Medicine
Think of it as the ultimate "Pub Trivia" for robots.
If an AI knows that Mitochondria is the powerhouse of the cell and can calculate a basic derivative, it gets points.
The "Good" Score Threshold in 2026
Three years ago, a score of 60% was considered revolutionary.
Today, that would be laughable.
In 2026, the bar has moved drastically. Here is the new grading scale for AI models:
- 90% - 100% (Elite): Gemini 3 Pro, GPT-5, Claude 3.7 Opus. These models have effectively "memorized" the internet.
- 80% - 89% (Standard): Llama 4 (Small), DeepSeek R1. Good for chatbots and basic assistants.
- < 80% (Legacy): GPT-3.5, Llama 2. These models hallucinate frequently and lack nuance.
If a new model launches today with an MMLU score under 85%, it is not "State of the Art."
To see real examples of high MMLU scores in action, check our Live Leaderboard to see who is currently sitting at the top.
MMLU vs. HumanEval: Know the Difference
Here is where developers get tricked.
A high MMLU score does not mean the AI can code.
We recently analyzed the DeepSeek R1 vs. Gemini 3 Pro benchmark, and the results were shocking.
- Gemini 3 Pro had a higher MMLU (General Knowledge).
- DeepSeek R1 had a higher HumanEval (Coding Ability).
The takeaway?
If you want an AI to write a poem or answer history questions, look at MMLU.
If you want an AI to build a Python script, look at HumanEval.
Compare DeepSeek's MMLU against its coding score to see this disparity yourself.
Why MMLU is Losing Relevance
There is a problem with the MMLU in 2026.
It has become too easy.
When every top model scores between 88% and 92%, the test loses its ability to differentiate between "Smart" and "Genius."
This is why the industry is shifting toward Humanity's Last Exam (HLE).
Unlike MMLU's static trivia, HLE requires reasoning. It asks the AI to solve problems it has never seen before, rather than just reciting facts it learned during training.
Frequently Asked Questions (FAQ)
1. What does MMLU stand for in AI?
MMLU stands for Massive Multitask Language Understanding. It is a benchmark designed to measure a Large Language Model's knowledge across 57 distinct subjects, ranging from STEM to Humanities.
2. Is a score of 85% on MMLU good?
In 2026, a score of 85% is considered "Good but not Great." It is the standard baseline for a competent AI assistant. Top-tier "Frontier Models" like Gemini 3 Pro are now pushing past 90%.
3. What is the difference between MMLU and HumanEval?
MMLU tests knowledge (facts, history, law), while HumanEval tests skill (writing functional code). A model can be great at trivia (High MMLU) but terrible at programming (Low HumanEval).
4. Why do coding models have lower MMLU scores?
Specialized coding models are often trained on GitHub repositories and StackOverflow rather than Wikipedia. This makes them brilliant engineers but poor historians, leading to lower general knowledge scores.
Conclusion
Don't let the marketing numbers fool you.
An MMLU score is just a starting point. It tells you if the model is well-read, not if it is smart.
In 2026, look for the MMLU score to ensure basic competence (85%+), but look at Humanity's Last Exam to find true intelligence.
Sources & References
- [Internal] Live Leaderboard 2026: Gemini 3 Pro vs. DeepSeek vs. GPT-5 - The definitive list of current model scores.
- [Internal] DeepSeek R1 vs. Gemini 3 Pro - A case study in Knowledge vs. Coding skills.
- [External] Papers With Code - MMLU Leaderboard (Historical Data).
- [External] arXiv.org - Measuring Massive Multitask Language Understanding (Original Paper).