A new version of the Artificial Analysis Intelligence Index surfaced on Hacker News with 98 points and 35 comments. The update ranks leading language models across multiple capability dimensions using standardized tests.
What the Index Measures
The index evaluates models on reasoning, knowledge retrieval, coding, and instruction following. It aggregates results from public benchmarks into a single composite score while preserving individual category breakdowns. Scores reflect performance on held-out test sets rather than training data contamination checks.
Latest Scores and Changes
Version 4.2 shows GPT-4o at 87.4, Claude 3.5 Sonnet at 86.9, and Gemini 1.5 Pro at 84.2. Open-source models improved: Llama 3.1 405B reached 81.7 while Qwen2 72B scored 79.3. The gap between the top closed model and the best open model narrowed to 5.7 points.
| Model | Overall | Reasoning | Coding | Knowledge |
|---|---|---|---|---|
| GPT-4o | 87.4 | 89.1 | 86.8 | 85.2 |
| Claude 3.5 Sonnet | 86.9 | 88.4 | 85.9 | 86.1 |
| Gemini 1.5 Pro | 84.2 | 85.7 | 83.1 | 84.8 |
| Llama 3.1 405B | 81.7 | 82.3 | 80.9 | 81.4 |
How to Access and Use the Data
Visit the official page at artificialanalysis.ai for full tables and methodology notes. Download CSV exports for local analysis. Researchers can replicate core tests using the listed public datasets and evaluation scripts.
Comparison with Other Benchmarks
The index differs from LMSYS Chatbot Arena and Hugging Face Open LLM Leaderboard in weighting. It places heavier emphasis on multi-step reasoning than pure chat preference votes. It also reports variance across five runs per model, unlike single-run leaderboards.
| Benchmark | Focus | Update Frequency | Open Weights Coverage |
|---|---|---|---|
| AI Intelligence Index | Composite capability | Quarterly | High |
| LMSYS Arena | Human preference | Weekly | Medium |
| HF Open LLM Leaderboard | Academic tasks | Continuous | Very high |
Pros and Cons
- Provides transparent category scores instead of one opaque number.
- Includes both proprietary and open models in the same table.
- Lacks contamination audits for every new model release.
- Relies on static benchmarks that can saturate quickly.
Who Should Use This Index
Developers choosing between frontier APIs benefit from the side-by-side numbers. Researchers tracking open-model progress can filter for weights-available entries. Teams needing real-time chat preference data should still consult LMSYS Arena instead.
Verdict
The v4.2 release gives the clearest current snapshot of capability gaps between closed and open models while remaining easy to query programmatically.
The index will likely influence procurement decisions until the next major model wave resets the leaderboard.
Top comments (0)