OpenAI / MODEL EVIDENCE

o1-preview

21 benchmarks · 29 observations · 19 sources · released 2024-09-12

Scores at a glance

21 benchmarks · large dot: o1-preview; small dots: closest models where reported

9.2896.4

o1-preview · GPQA diamond · Best score (across scorers) · % ↗

3.2998.13

o1-preview · MATH level 5 · Best score (across scorers) · % ↗

6.481

o1-preview · ZebraLogic · Grid-level accuracy · complete puzzles ↗

23.899.1

o1-preview · PlanBench · Blocksworld ↗

3 more reported results

52.8% · o1-preview · PlanBench · Mystery Blocksworld · PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change ↗

37.3% · o1-preview · PlanBench · Randomized Mystery Blocksworld · PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change ↗

23.65% · o1-preview · PlanBench · Blocksworld Hard · PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change ↗

14.962.2

o1-preview · Omni-MATH · Rule-based accuracy · rule-based evaluation ↗

33.1480.86

o1-preview · JudgeBench · Overall pairwise accuracy · Arena-Hard judge scaffold ↗

0.9541.94

o1-preview · Putnam-AXIOM · Original · problem accuracy · original questions ↗

2 more reported results

43% · o1-preview · Putnam-AXIOM · Functional variation · accuracy · five-trial robustness comparison · Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs ↗

51% · o1-preview · Putnam-AXIOM · Matched original subset · accuracy · five-trial robustness comparison · Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs ↗

60.990.8

o1-preview† · CFBench (Chinese Complex Following) · Full set · Constraint satisfaction · full-set evaluation ↗

2 more reported results

63.4% · o1-preview† · CFBench (Chinese Complex Following) · Full set · Instruction satisfaction · full-set evaluation · CFBench: A Comprehensive Constraints-Following Benchmark for LLMs ↗

71.8% · o1-preview† · CFBench (Chinese Complex Following) · Full set · Priority satisfaction · full-set evaluation · CFBench: A Comprehensive Constraints-Following Benchmark for LLMs ↗

7.662.5

o1-preview · SimpleQA · Correct answers · OpenAI Simple Evals · archived July 2025 ↗

71.599.3

o1-preview · HumanEval · Pass@1 · OpenAI Simple Evals · archived July 2025 ↗

1636

o1-preview · BioLP-Bench · rounded reported accuracy · Figure 1 caption · three-run means ↗

Closest models by score

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

3.8 percentile-point gap

3 of 3 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.

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 refactoring benchmark ↗

    Paul Gauthier, Aider contributors

  2. ARC Prize - Leaderboard ↗

    ARC Prize Foundation

  3. CFBench: A Comprehensive Constraints-Following Benchmark for LLMs — published result table ↗

    Tao Zhang, Chenglin Zhu, Yanjun Shen, Wenjing Luo, Yan Zhang, Hao Liang et al. · 2024

    All 14 authors

    Tao Zhang, Chenglin Zhu, Yanjun Shen, Wenjing Luo, Yan Zhang, Hao Liang, Tao Zhang, Fan Yang, Mingan Lin, Yujing Qiao, Weipeng Chen, Bin Cui, Wentao Zhang, Zenan Zhou

  4. JudgeBench: A Benchmark for Evaluating LLM-based Judges — published result table ↗

    Sijun Tan, Siyuan Zhuang, Kyle Montgomery, William Y. Tang, Alejandro Cuadron, Chenguang Wang et al. · 2024

    All 8 authors

    Sijun Tan, Siyuan Zhuang, Kyle Montgomery, William Y. Tang, Alejandro Cuadron, Chenguang Wang, Raluca Ada Popa, Ion Stoica

  5. HARP: A challenging human-annotated math reasoning benchmark — published result table ↗

    Yue, Albert S., Madaan, Lovish, Moskovitz, Ted, Strouse, DJ, Singh, Aaditya K. · 2024

  6. ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning — published result table ↗

    Bill Yuchen Lin, Ronan Le Bras, Kyle Richardson, Ashish Sabharwal, Radha Poovendran, Peter Clark et al. · 2025

    All 7 authors

    Bill Yuchen Lin, Ronan Le Bras, Kyle Richardson, Ashish Sabharwal, Radha Poovendran, Peter Clark, Yejin Choi

  7. Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs — published result table ↗

    Gulati, Aryan, Miranda, Brando, Chen, Eric, Xia, Emily, Fronsdal, Kai, Dumont, Bruno et al. · 2025

    All 8 authors

    Gulati, Aryan, Miranda, Brando, Chen, Eric, Xia, Emily, Fronsdal, Kai, Dumont, Bruno, Obbad, Elyas, Koyejo, Sanmi

  8. Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models ↗

    Zhang, Andy K., Perry, Neil, Dulepet, Riya, Ji, Joey, Menders, Celeste, Lin, Justin W. et al. · 2024

    All 27 authors

    Zhang, Andy K., Perry, Neil, Dulepet, Riya, Ji, Joey, Menders, Celeste, Lin, Justin W., Jones, Eliot, Hussein, Gashon, Liu, Samantha, Jasper, Donovan, Peetathawatchai, Pura, Glenn, Ari, Sivashankar, Vikram, Zamoshchin, Daniel, Glikbarg, Leo, Askaryar, Derek, Yang, Mike, Zhang, Teddy, Alluri, Rishi, Tran, Nathan, Sangpisit, Rinnara, Yiorkadjis, Polycarpos, Osele, Kenny, Raghupathi, Gautham, Boneh, Dan, Ho, Daniel E., Liang, Percy

  9. GPQA Diamond ↗

    Epoch AI

  10. MATH Level 5 ↗

    Epoch AI

  11. OTIS Mock AIME 2024-2025 ↗

    Epoch AI

  12. SimpleBench ↗

    Epoch AI

  13. AI benchmarks & capabilities ↗

    Epoch AI

  14. WeirdML ↗

    Håvard Tveit Ihle

  15. Measuring AI Ability to Complete Long Software Tasks ↗

    METR

  16. Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models — published result table ↗

    Gao, Bofei, Song, Feifan, Yang, Zhe, Cai, Zefan, Miao, Yibo, Dong, Qingxiu et al. · 2024

    All 20 authors

    Gao, Bofei, Song, Feifan, Yang, Zhe, Cai, Zefan, Miao, Yibo, Dong, Qingxiu, Li, Lei, Ma, Chenghao, Chen, Liang, Xu, Runxin, Tang, Zhengyang, Wang, Benyou, Zan, Daoguang, Quan, Shanghaoran, Zhang, Ge, Sha, Lei, Zhang, Yichang, Ren, Xuancheng, Liu, Tianyu, Chang, Baobao

  17. PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change — published result table ↗

    Karthik Valmeekam, Matthew Marquez, Alberto Olmo, Sarath Sreedharan, Subbarao Kambhampati · 2022

  18. Simple Evals — model benchmark results ↗

    OpenAI · 2025

  19. BioLP-bench: Measuring understanding of biological lab protocols by large language models ↗

    Igor Ivanov · 2024

Names used in source tables

o1 preview · o1-preview · o1-preview†