LLM benchmark scores provide a standardized way to compare language models across reasoning, coding, math, and knowledge tasks. This reference covers 20+ models including GPT-4o, Claude 3.5, Gemini 1.5, and open-source models like Llama 3 and Mistral.
| Model | Provider | MMLU | HumanEval | GSM8K | HellaSwag | TruthfulQA | GPQA | MT-Bench | Context | Open Source |
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Score color coding (per benchmark column):
Click column headers to sort. All scores are percentages (0-100). N/A = not publicly available.
How to Use LLM Benchmark Scores
LLM benchmark scores provide a standardized way to compare models before paying for API access or downloading model weights. Understanding what each benchmark measures helps you choose the right model for your specific use case.
Understanding Each Benchmark
MMLU (Massive Multitask Language Understanding, 57 subjects) tests general knowledge breadth. High MMLU indicates the model handles diverse knowledge domains. HumanEval tests code generation — essential for coding assistants. GSM8K tests grade school math reasoning, a proxy for multi-step logical reasoning. HellaSwag tests commonsense reasoning about everyday situations. TruthfulQA tests whether the model avoids stating common misconceptions. GPQA tests graduate-level science reasoning that can't be looked up.
Choosing a Model for Your Use Case
For coding tasks: prioritize HumanEval scores. For research and analysis: prioritize MMLU and GPQA. For math and logical reasoning: prioritize GSM8K. For general chat assistants: MT-Bench is the most predictive benchmark. For factual accuracy: TruthfulQA matters.
Limitations of Benchmark Comparisons
Benchmark scores have important limitations. Contamination: models trained on benchmark test sets score artificially high. Methodology variance: the same model can score 5-10% differently based on prompting style, shot count, and evaluation harness. Task mismatch: a model scoring 90% on MMLU may still struggle on your specific domain. Always test candidate models on your actual use case data before committing.
Open Source vs. Proprietary Models
Use the open-source filter to compare models you can run locally or self-host. Open-source models (Llama 3, Mistral, Qwen 2) have closed the gap with proprietary models significantly — Llama 3 405B is competitive with GPT-4 on many benchmarks while being freely deployable. The tradeoffs: proprietary models get continuous updates; open-source models offer privacy and cost control at scale.
FAQ
What is MMLU and why does it matter?
MMLU (Massive Multitask Language Understanding) tests knowledge across 57 subjects at high school and college level. A score of 90%+ indicates near-human expert knowledge breadth. It's one of the most trusted benchmarks because it's difficult to train on accidentally and covers diverse domains.
What is HumanEval?
HumanEval is a coding benchmark of 164 Python programming problems with test cases. The pass@1 score measures what percentage of problems the model solves correctly in its first attempt. GPT-4 and Claude 3.5 Sonnet score around 87-90%, making it a strong test of practical coding capability.
Are these benchmark scores accurate?
Scores are compiled from published papers and official provider disclosures as of April 2026. Benchmark scores can vary based on evaluation methodology, prompt formatting, and temperature settings. Always verify with official sources before making high-stakes model selection decisions.
What is benchmark contamination?
Benchmark contamination occurs when training data includes examples from the test set, artificially inflating scores. This is a significant concern as benchmarks become public — models may score high not because they reason well, but because they've seen the answers. New benchmarks like GPQA are designed to resist contamination.
Which benchmark best predicts real-world performance?
No single benchmark perfectly predicts real-world performance. MMLU correlates with general knowledge tasks, HumanEval with coding, GSM8K with reasoning, and MT-Bench with conversational quality. For most applications, MT-Bench and GPQA are strongest predictors of practical usefulness as they test reasoning rather than memorization.
What is GPQA?
GPQA (Graduate-Level Google-Proof Q&A) tests expert-level knowledge in biology, physics, and chemistry with questions that can't be answered by web search alone. Expert humans score around 65%. It's considered one of the most reliable benchmarks because it requires genuine understanding, not recall.
Is this reference free?
Yes, completely free. Browse and compare all benchmark scores without signing up. Data is updated when major model releases provide new benchmark scores.