gpt-3.5-turbo-1106 · Aider coding benchmarks · Refactoring · percent correct ↗
OpenAI / MODEL EVIDENCE
GPT-3.5-turbo-1106
6 benchmarks · 13 observations · 6 sources
Scores at a glance
6 benchmarks · large dot: GPT-3.5-turbo-1106; small dots: closest models where reported
GPT-3.5-Turbo-1106 · ComplexBench · DRFR · all constraints · RAL evaluation ↗
GPT-3.5-turbo-1106 · HarmBench · HarmBench harmful compliance · SORRY-Bench paper · cross-benchmark comparison ↗
GPT-3.5-Turbo-1106 · FollowBench · Hard success rate · five-level average · English evaluation ↗
1 more reported result
72.5% · GPT-3.5-Turbo-1106 · FollowBench · Soft success rate · five-level average · English evaluation · FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models ↗
gpt-3.5-turbo-1106 · InFoBench · Decomposed requirement following rate · GPT-4-1106 evaluator ↗
gpt-3.5-turbo-1106 · NYU CTF Bench · pass@5 · 200-challenge full set ↗
6 more reported results
1.9231% · gpt-3.5-turbo-1106 · NYU CTF Bench · pass@5 · crypto subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗
0% · gpt-3.5-turbo-1106 · NYU CTF Bench · pass@5 · forensics subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗
12.5% · gpt-3.5-turbo-1106 · NYU CTF Bench · pass@5 · misc subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗
2.5641% · gpt-3.5-turbo-1106 · NYU CTF Bench · pass@5 · pwn subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗
5.8824% · gpt-3.5-turbo-1106 · NYU CTF Bench · pass@5 · rev subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗
0% · gpt-3.5-turbo-1106 · NYU CTF Bench · pass@5 · web subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗
Closest models by score
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
There are not enough shared scored settings to identify close comparisons reliably.
Sources
Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.
- Aider refactoring benchmark ↗
- FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models — published result table ↗
All 10 authors
Jiang, Yuxin, Wang, Yufei, Zeng, Xingshan, Zhong, Wanjun, Li, Liangyou, Mi, Fei, Shang, Lifeng, Jiang, Xin, Liu, Qun, Wang, Wei
- InFoBench: Evaluating Instruction Following Ability in Large Language Models — published result table ↗
All 10 authors
Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, Pengfei Liu, Dong Yu
- SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal — published result table ↗
All 16 authors
Tinghao Xie, Xiangyu Qi, Yi Zeng, Yangsibo Huang, Udari Madhushani Sehwag, Kaixuan Huang, Luxi He, Boyi Wei, Dacheng Li, Ying Sheng, Ruoxi Jia, Bo Li, Kai Li, Danqi Chen, Peter Henderson, Prateek Mittal
- Benchmarking Complex Instruction-Following with Multiple Constraints Composition — published result table ↗
All 14 authors
Bosi Wen, Pei Ke, Xiaotao Gu, Lindong Wu, Hao Huang, Jinfeng Zhou, Wenchuang Li, Binxin Hu, Wendy Gao, Jiaxin Xu, Yiming Liu, Jie Tang, Hongning Wang, Minlie Huang
- NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗
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
Names used in source tables
GPT-3.5-Turbo-1106 · GPT-3.5-turbo-1106 · gpt-3.5-turbo-1106