LLM Benchmarks Timeline
- See also: LLM Comparisons and LLM Rankings
Timeline of benchmarks surpassed by large language models (LLMs).
2024
| Benchmark | Category | Date Created | Date Defeated | Killed By | Defeated By Model | Original Score | Final Score | Details | Links |
|---|---|---|---|---|---|---|---|---|---|
| ARC-AGI | Reasoning | 2019-11 | 2024-12 | Saturation | O3 | Human Baseline: ~80% | O3: 87.5% | Abstract reasoning challenge with visual pattern completion tasks created by François Chollet. | Paper, Website |
| MATH | Mathematics | 2021-03 | 2024-09 | Saturation | O1 | Average CS PhD: ~40% | O1: 94.8% | 12K challenging competition math problems from AMC/AIME, requiring complex multi-step reasoning. | Paper, GitHub |
| BIG-Bench-Hard | Multi-task | 2022-10 | 2024-06 | Saturation | Sonnet 3.5 | Average Human: 67.7% | Sonnet 3.5: 93.1% | A curated suite of 23 challenging tasks from BIG-Bench. | Paper, GitHub, Evidence |
| HumanEval | Coding | 2021-07 | 2024-05 | Saturation | GPT-4o | Unspecified | GPT-4o: 90.2% | 164 Python programming problems testing coding abilities. | Paper, GitHub, Evidence |
| IFEval | Instruction Following | 2023-11 | 2024-03 | Saturation | LLama 3.3 70B | Unspecified | LLama 3.3 70B: 92.1% | Evaluation suite testing multi-step instruction-following capabilities. | Paper, GitHub, Evidence |
2023
| Benchmark | Category | Date Created | Date Defeated | Killed By | Defeated By Model | Original Score | Final Score | Details | Links |
|---|---|---|---|---|---|---|---|---|---|
| GSM8K | Mathematics | 2021-10 | 2023-11 | Saturation | GPT-4 | Unspecified | GPT-4: 92.0% | 8.5K grade school math word problems requiring step-by-step solutions. | Paper, GitHub, Evidence |
| Turing Test | Conversation | 1950-10 | 2023-03 | Saturation | GPT-4 | Interrogator > 50% | Interrogator 46% | The original AI benchmark proposed by Alan Turing in 1950 (the "imitation game"). | Paper, Evidence |
| ARC (AI2) | Reasoning | 2018-03 | 2023-03 | Saturation | GPT-4 | Unspecified | GPT-4: 96.3% | Grade-school multiple-choice reasoning tasks testing logical, spatial, temporal reasoning. | Paper, Website, Evidence |
| HellaSwag | Common Sense | 2019-05 | 2023-03 | Saturation | GPT-4 | Human: 95.6% | GPT-4: 95.3% | Multiple-choice questions about everyday scenarios with adversarial filtering. | Paper, Website, Evidence |
| MMLU | Knowledge | 2020-09 | 2023-03 | Saturation | GPT-4 | 95th pct Human: 87.0% | GPT-4: 87.3% | 57 subjects from real-world sources (professional exams) testing breadth and depth of knowledge. | Paper, GitHub, Evidence |
| WinoGrande | Common Sense | 2019-07 | 2023-03 | Saturation | GPT-4 | Human: 94% | GPT-4: 87.5% | Enhanced WSC with 44K problems testing common-sense pronoun resolution. | Paper, Website, Evidence |
Pre-2023
2022
| Benchmark | Category | Date Created | Date Defeated | Killed By Model | Defeated By | Original Score | Final Score | Details | Links |
|---|---|---|---|---|---|---|---|---|---|
| BIG-Bench | Multi-task | 2021-06 | 2022-04 | Saturation | Palm 540B | Human: 49.8% | Palm 540B: 61.4% | 204 tasks spanning linguistics, math, common-sense reasoning, and more. | Paper, GitHub, Evidence |
2019
| Benchmark | Category | Date Created | Date Defeated | Killed By | Defeated By Model | Original Score | Final Score | Details | Links |
|---|---|---|---|---|---|---|---|---|---|
| SuperGLUE | Language | 2019-05 | 2019-10 | Saturation | T5 | Human: 89.8% | T5: 89.3% | More challenging language understanding tasks (word sense, causal reasoning, RC). | Paper, Website |
| WSC | Common Sense | 2012-05 | 2019-07 | Saturation | ROBERTA (w SFT) | Human: 96.5% | ROBERTA (w SFT): 90.1% | Carefully crafted sentence pairs with ambiguous pronoun references. | Paper, Website |
| GLUE | Language | 2018-05 | 2019-06 | Saturation | XLNet | Human: 87.1% | XLNet: 88.4% | Nine tasks for evaluating NLU (inference, paraphrase, similarity, etc.). | Paper, Website |
| TriviaQA | Knowledge | 2017-05 | 2019-06 | Saturation | SpanBERT | Human: 79.7% | SpanBERT: 83.6% | 650K QA-evidence triples requiring cross-sentence reasoning. | Paper, Website |
| SQuAD v2.0 | Language | 2018-05 | 2019-04 | Saturation | BERT | Human: 89.5% | BERT: 89.5% | Extension of SQuAD adding unanswerable questions. | Paper, Website |
| SQuAD | Language | 2016-05 | 2019-03 | Saturation | BERT | Human: 91.2% | BERT: 93.2% | 100,000+ QA tasks on Wikipedia articles. | Paper, Website |
2018
| Benchmark | Category | Date Created | Date Defeated | Killed By | Defeated By Model | Original Score | Final Score | Details | Links |
|---|---|---|---|---|---|---|---|---|---|
| SWAG | Common Sense | 2018-05 | 2018-10 | Saturation | BERT | Human: 88% | BERT: 86% | 113K multiple-choice questions about grounded situations (common sense “next step”). | Paper, Website |