LLM Benchmarks
MMLU, HumanEval & more
The Problem: Every AI company claims their model is the best. Marketing highlights cherry-picked benchmarks. How can you objectively compare models and know which actually performs better for your use case?
The Solution: Evaluate Models Systematically
Benchmarks are standardized tests that measure model performance across various tasks. They're like crash tests for cars — independent evaluations that let you compare apples to apples. They inform model selection and can reveal where fine-tuning is needed. But beware: benchmark contamination (models trained on test data) can inflate scores, and no single benchmark tells the whole story.
Think of it like a test drive evaluation:
- 1. Define task categories from YOUR use case: What does the model need to do? Summarize reports? Answer medical questions? Write code? Debug existing code?
- 2. Check public benchmarks (beware contamination): Use GPQA, AIME, SWE-bench as filters — but remember models may have trained on test data. AIME/SWE-bench are harder to game because they use real-world problems
- 3. Collect 50-100 gold-standard pairs: Build input/output pairs with expert-verified correct answers — your ground truth dataset. This is the only evaluation that cannot be contaminated
- 4. Score models on YOUR data: Run each candidate on your dataset. Use LLM-as-judge (a stronger model grades weaker ones) for scalable evaluation. Measure accuracy, latency, and cost
- 5. Compare cost per quality point: A cheaper model at 90% accuracy may beat an expensive one at 95% — calculate the ROI. Reasoning models cost 3-5x more but may be worth it for math/logic tasks
- 6. Re-evaluate quarterly: Models improve fast — the best choice today may not be best in 3 months. Keep your evaluation pipeline automated
Key Benchmarks to Know
- MMLU: Multiple-choice knowledge across 57 subjects. Saturated — most frontier models score >88%, making it less useful for differentiation
- GPQA Diamond: PhD-level science questions. Harder than MMLU and still differentiates frontier models well. GPT-5 and Claude Opus 4.5 lead at ~87%
- AIME 2024: Real math competition problems. Reasoning models (o3: 91.6%, DeepSeek R1: 86.7%) dominate. Regular models score 50-74%. The biggest gap between reasoning and non-reasoning models
- SWE-bench Verified: Resolve real GitHub issues in real codebases. The most practical coding benchmark. Claude Opus 4.5 leads at 80.9% — far ahead of open-source models (~40-50%)
- Chatbot Arena (Elo): Human preference ranking: users compare anonymous model responses and vote. 6M+ votes compute Elo ratings. The most reliable "vibes" benchmark — measures what people actually prefer
- Custom Benchmarks: Public benchmarks show general capability. Custom benchmarks show if the model works for YOUR task. The model topping MMLU may not be best for your medical chatbot. Always build domain-specific evaluation
Fun Fact: On AIME 2024, reasoning model o3 scores 91.6% — outperforming 99% of human competitors. But on SWE-bench (real code), it scores only 61.2% while Claude Opus 4.5 reaches 80.9%. There is no universal "best" model — only the best model for YOUR task.
Try It Yourself!
Explore the interactive benchmark comparison below to see how different models perform across tasks.
Massive Multitask Language Understanding
Tests knowledge across 57 subjects from STEM to humanities. Saturated — most frontier models score >88%
How to Read Benchmarks
- Higher is better: But differences < 2% are usually not significant in practice
- Context matters: MMLU tests knowledge, SWE-bench tests real coding. Choose based on your task
- No model wins all: o3 leads math, Claude leads coding, GPT-5 leads knowledge. Pick for YOUR task
Benchmark Contamination
Models may have trained on benchmark data, inflating scores. AIME and SWE-bench are harder to contaminate because they use real-world problems. Always test on YOUR data — it is the only benchmark that cannot be gamed.
Try it yourself
Interactive demo of this technique
Evaluate model quality systematically instead of subjective assessment
Try a few articles and see if you like the results. If summaries are good — the model fits.
1. Test dataset: 100 articles
- 5 categories × 20 articles: politics, tech, sports, economics, science
- Golden summaries: written by experts (ground truth)
2. Metrics: Auto: ROUGE-L ≥ 0.35, BERTScore ≥ 0.85, length 40-80 tokens Manual (sample 20%): factual accuracy (1-5), completeness (1-5), readability (1-5)
3. Pass/fail criteria:
- ROUGE-L < 0.30 → FAIL
- Factual errors > 10% → FAIL
- BERTScore < 0.80 → WARNING
4. Process:
- CI: auto-metrics on every PR changing prompts
- Weekly: manual evaluation of 20 random production requests
- Monthly: full run of 100 articles when switching models
Systematic benchmark: dataset with golden answers + auto-metrics (ROUGE, BERTScore) + manual evaluation + pass/fail thresholds. Without this, changing model or prompt is a gamble.
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