verifiable-reasoning on VCBench
verifiable-reasoning scores F0.5 27.7 on VCBench, the venture capital benchmark from the University of Oxford and Vela Research, with 30.6% precision and 21.0% recall on the 4,500-founder private test set. That is rank 11 of 32, 2.6× the F0.5 of tier-1 VCs and 3.2× Y Combinator, at $0 per 1,000 founders scored.
- Rank
- #11 of 32
- F0.5
- 27.7
- Precision
- 30.6%
- Recall
- 21.0%
- Cost / 1k founders
- $0
Scored on the 4,500-founder private test set, mean over three folds. That is 2.6× the F0.5 of tier-1 VCs. See the full leaderboard.
Compared with the reference rows
| Entry | Precision | Recall | F0.5 | Cost / 1k |
|---|---|---|---|---|
| verifiable-reasoning | 30.6% | 21.0% | 27.7 | $0 |
| Think-Reason-Learn Ensemble | 40.6% | 30.1% | 37.9 | $0.87 |
| Tier-1 VCs | 23.0% | 5.2% | 10.7 | n/a |
| Y Combinator | 14.0% | 6.9% | 8.6 | n/a |
| Random Classifier | 9.0% | 9.0% | 9.0 | $0 |
Human rows are normalized to the dataset's 9% success rate, so every entry is compared on the same base rate. Costs use list prices on 2026-09-24.
How it was scored
No LLM runs at scoring time. A trained model reads structured features from the anonymized profile (education, career history, prior companies) and scores the founder directly, so the API cost at scoring is $0. Training cost is excluded, the same rule applied to every entry.
Frequently asked questions
- What does verifiable-reasoning score on VCBench?
- verifiable-reasoning scores F0.5 27.7 on VCBench, with 30.6% precision and 21.0% recall, rank 11 of 32 as of 2026-10-07.
- Does verifiable-reasoning beat human investors at predicting founder success?
- Yes. Its F0.5 is 2.6 times that of tier-1 VCs (10.7) and 3.2 times Y Combinator (8.6), after both are normalized to the dataset's 9% base rate.
- How much does verifiable-reasoning cost per 1,000 founders?
- $0 at scoring time: no LLM runs when verifiable-reasoning scores a founder. Training cost is excluded for every entry.