FinED-Bench - Financial Domain Knowledge Errors: leaderboard
Metric: F1 (%; financial domain knowledge errors: terminology misuse, incorrect legal reference, ambiguous expression, numerical unit error, omitted financial element; a predicted error counts as correct when the extracted sentence matches or contains the annotated sentence and the error type is classified correctly; the paper's error-detection prompt over each document, reasoning models with thinking on unless marked no thinking). Source: arxiv.org. Saturation forecast: Estimated already saturated. 11 models tracked.
Top models
| # | Model | Score |
|---|---|---|
| 1 | GPT-4o | 47.36 |
| 2 | Qwen 3 14B | 41.1 |
| 3 | Qwen 3 8B | 38.6 |
| 4 | Qwen 3 14B (Non-reasoning) | 26.63 |
| 5 | DeepSeek R1 0528 Qwen3 8B | 22.82 |
| 6 | Qwen 3 8B (Non-reasoning) | 18.06 |
| 7 | Qwen 2.5 7B Instruct | 8.33 |
| 8 | GPT-4o Mini | 7.34 |
| 9 | GPT-3.5 Turbo | 3.65 |
Interactive version: theaggregate.ai/benchmark?slug=fined-bench-financial-domain-knowledge-errors · How It Works · Data refreshed daily, snapshot 2026-09-26.