RLTasksby ulam.ai
Models / OpenAI

OpenAI / MODEL EVIDENCE

GPT-4.1

50 benchmarks · 72 observations · 51 sources · released 2025-04-14

Scores at a glance

50 benchmarks · large dot: GPT-4.1; small dots: closest models where reported

LMCA ↗25.61%
2.7868.26

GPT-4.1 · LMCA · Score · % ↗

Fortress ↗53.02%
5.7874.39

GPT-4.1 · Scale Labs · Risk elicitation score ↗

RM-Bench ↗77.4%
59.796

GPT-4.1 · RM-Bench · Overall accuracy · official leaderboard ↗

LiveCodeBench Pro ↗889 Elo
1999

GPT-4.1 · LiveCodeBench Pro · Elo rating · human contest calibration ↗

3 more reported results

0% · GPT-4.1 · LiveCodeBench Pro · Hard · pass@1 · difficulty breakdown · LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? ↗

0% · GPT-4.1 · LiveCodeBench Pro · Medium · pass@1 · difficulty breakdown · LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? ↗

23.9% · GPT-4.1 · LiveCodeBench Pro · Easy · pass@1 · difficulty breakdown · LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? ↗

NYU CTF Bench ↗11.5%
11.511.5

gpt-4.1 · NYU CTF Bench · pass@1 · 200-challenge full set ↗

11 more reported results

13.5% · gpt-4.1 · NYU CTF Bench · pass@1 · 200-challenge full set · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

7.6923% · gpt-4.1 · NYU CTF Bench · pass@1 · crypto subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

9.6154% · gpt-4.1 · NYU CTF Bench · pass@1 · crypto subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

20% · gpt-4.1 · NYU CTF Bench · pass@1 · forensics subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

6.6667% · gpt-4.1 · NYU CTF Bench · pass@1 · forensics subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

20.8333% · gpt-4.1 · NYU CTF Bench · pass@1 · misc subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

7.6923% · gpt-4.1 · NYU CTF Bench · pass@1 · pwn subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

12.8205% · gpt-4.1 · NYU CTF Bench · pass@1 · pwn subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

11.7647% · gpt-4.1 · NYU CTF Bench · pass@1 · rev subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

17.6471% · gpt-4.1 · NYU CTF Bench · pass@1 · rev subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

10.5263% · gpt-4.1 · NYU CTF Bench · pass@1 · web subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

ALE-Bench ↗558.1 Performance points
137.782951.32

gpt-4.1 · Average contest performance ↗

Closest models by score

Similar positions on shared benchmark leaderboards. Smaller gaps are closer.

o1 →OpenAI
16.3 percentile-point gap

16 of 26 shared benchmark comparisons

How similarity is measured

Mean absolute gap in percentile positions on shared benchmark settings; lower is closer. At least 60% overlap is required, with a minimum of three when available. Independent evaluations are used when there are at least three; otherwise published report comparisons are used. Repeated model observations within one setting are averaged before ranking. Writing-style traits and composite indices are excluded. This describes similarity, not an overall quality ranking.

Filter comparisons to this model →

Sources

Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.

  1. Aider code editing benchmark ↗

    Paul Gauthier, Aider contributors

  2. Aider LLM Leaderboards — Polyglot coding benchmark ↗

    Aider

  3. ARC Prize - Leaderboard ↗

    ARC Prize Foundation

  4. HealthBench: Evaluating Large Language Models Towards Improved Human Health — published result table ↗

    Arora, Rahul K., Wei, Jason, Hicks, Rebecca Soskin, Bowman, Preston, Quiñonero-Candela, Joaquin, Tsimpourlas, Foivos et al. · 2025

    All 12 authors

    Arora, Rahul K., Wei, Jason, Hicks, Rebecca Soskin, Bowman, Preston, Quiñonero-Candela, Joaquin, Tsimpourlas, Foivos, Sharman, Michael, Shah, Meghan, Vallone, Andrea, Beutel, Alex, Heidecke, Johannes, Singhal, Karan

  5. SWE-bench Goes Live! — published result table ↗

    Linghao Zhang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Chengxing Xie et al. · 2025

    All 15 authors

    Linghao Zhang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Chengxing Xie, Junhao Wang, Maoquan Wang, Yufan Huang, Shengyu Fu, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang

  6. SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models — published result table ↗

    Pham, Thinh, Nguyen, Nguyen, Zunjare, Pratibha, Chen, Weiyuan, Tseng, Yu-Min, Vu, Tu · 2025

  7. LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? — published result table ↗

    Zihan Zheng, Zerui Cheng, Zeyu Shen, Shang Zhou, Kaiyuan Liu, Hansen He et al. · 2025

    All 19 authors

    Zihan Zheng, Zerui Cheng, Zeyu Shen, Shang Zhou, Kaiyuan Liu, Hansen He, Dongruixuan Li, Stanley Wei, Hangyi Hao, Jianzhu Yao, Peiyao Sheng, Zixuan Wang, Wenhao Chai, Aleksandra Korolova, Peter Henderson, Sanjeev Arora, Pramod Viswanath, Jingbo Shang, Saining Xie

  8. TextQuests: How Good are LLMs at Text-Based Video Games? — published result table ↗

    Phan, Long, Mazeika, Mantas, Zou, Andy, Hendrycks, Dan · 2025

  9. CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers — published result table ↗

    Pan, Haining, Roggeveen, James V., Berg, Erez, Carrasquilla, Juan, Chowdhury, Debanjan, Ganguli, Surya et al. · 2025

    All 19 authors

    Pan, Haining, Roggeveen, James V., Berg, Erez, Carrasquilla, Juan, Chowdhury, Debanjan, Ganguli, Surya, Ghimenti, Federico, Hasik, Juraj, Hunt, Henry, Jiang, Hong-Chen, Kamb, Mason, Kao, Ying-Jer, Khatami, Ehsan, Lawler, Michael J., Luo, Di, Neupert, Titus, Qi, Xiaoliang, Brenner, Michael P., Kim, Eun-Ah

  10. DTBench — Conceptual Reasoning Index ↗

    Conceptual Reasoning Index

  11. LMCA — Conceptual Reasoning Index ↗

    Conceptual Reasoning Index

  12. Ale Bench — methodology and published results ↗

    Epoch AI · 2026

  13. ARC-AGI-2 ↗

    Epoch AI

  14. Chess Puzzles ↗

    Epoch AI

  15. FrontierMath Tier 4 (v1, old) ↗

    Epoch AI

  16. FrontierMath Tiers 1-3 (v1, old) ↗

    Epoch AI

  17. FrontierMath Tiers 1-3 (v2) ↗

    Epoch AI

  18. GPQA Diamond ↗

    Epoch AI

  19. Humanity's Last Exam ↗

    Epoch AI

  20. MATH Level 5 ↗

    Epoch AI

  21. OTIS Mock AIME 2024-2025 ↗

    Epoch AI

  22. SimpleQA Verified ↗

    Epoch AI

  23. SimpleBench ↗

    Epoch AI

  24. SWE-bench Verified ↗

    Epoch AI

  25. AI benchmarks & capabilities ↗

    Epoch AI

  26. Fiction.liveBench — March 14, 2025 benchmark release ↗

    Fiction.live · 2025

  27. GeoBench — GeoGuessr model benchmark ↗

    GeoBench

  28. WeirdML ↗

    Håvard Tveit Ihle

  29. EnigmaEval ↗

    Scale Labs / SEAL

  30. Scale Labs — evaluation methodology and results ↗

    Scale Labs / SEAL · 2026

  31. Scale Labs — evaluation methodology and results ↗

    Scale Labs / SEAL · 2026

  32. Scale Labs — evaluation methodology and results ↗

    Scale Labs / SEAL · 2026

  33. Scale Labs — evaluation methodology and results ↗

    Scale Labs / SEAL · 2026

  34. Scale Labs — evaluation methodology and results ↗

    Scale Labs / SEAL · 2026

  35. Scale Labs — evaluation methodology and results ↗

    Scale Labs / SEAL · 2026

  36. NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗

    Minghao Shao, Sofija Jancheska, Meet Udeshi, Brendan Dolan-Gavitt, Haoran Xi, Kimberly Milner et al. · 2024

    All 13 authors

    Minghao Shao, Sofija Jancheska, Meet Udeshi, Brendan Dolan-Gavitt, Haoran Xi, Kimberly Milner, Boyuan Chen, Max Yin, Siddharth Garg, Prashanth Krishnamurthy, Farshad Khorrami, Ramesh Karri, Muhammad Shafique

  37. RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style — published result table ↗

    Liu, Yantao, Yao, Zijun, Min, Rui, Cao, Yixin, Hou, Lei, Li, Juanzi · 2024

  38. CadEval Dashboard — run 20250422_095709 ↗

    willpatrick · 2025

  39. AIME — evaluation methodology and results ↗

    Vals AI · 2026

  40. CaseLaw — evaluation methodology and results ↗

    Vals AI · 2026

  41. CorpFin — evaluation methodology and results ↗

    Vals AI · 2026

  42. GPQA Diamond — evaluation methodology and results ↗

    Vals AI · 2026

  43. LiveCodeBench — evaluation methodology and results ↗

    Vals AI · 2026

  44. LegalBench — evaluation methodology and results ↗

    Vals AI · 2026

  45. MATH 500 — evaluation methodology and results ↗

    Vals AI · 2026

  46. MedQA — evaluation methodology and results ↗

    Vals AI · 2026

  47. MGSM — evaluation methodology and results ↗

    Vals AI · 2026

  48. MMLU Pro — evaluation methodology and results ↗

    Vals AI · 2026

  49. MMMU Pro — evaluation methodology and results ↗

    Vals AI · 2026

  50. MortgageTax — evaluation methodology and results ↗

    Vals AI · 2026

  51. TaxEval — evaluation methodology and results ↗

    Vals AI · 2026

Names used in source tables

GPT 4.1 · GPT-4.1 · gpt-4.1