gpt-4-turbo-2024-04-09 · RepoQA & LongCodeArena · RepoQA retrieval accuracy · similarity threshold 0.8 ↗
OpenAI / MODEL EVIDENCE
GPT-4-turbo-2024-04-09
8 benchmarks · 13 observations · 6 sources
Scores at a glance
8 benchmarks · large dot: GPT-4-turbo-2024-04-09; small dots: closest models where reported
GPT-4-Turbo / 2024-04-09 · BigCodeBench & BigCodeBench-Hard · BigCodeBench Complete · greedy accuracy ↗
1 more reported result
48% · GPT-4-Turbo / 2024-04-09 · BigCodeBench & BigCodeBench-Hard · BigCodeBench Instruct · greedy accuracy · BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions ↗
gpt-4-turbo-2024-04-09 · SimpleQA · Correct answers · OpenAI Simple Evals · archived July 2025 ↗
gpt-4-turbo-2024-04-09 · HumanEval · Pass@1 · OpenAI Simple Evals · archived July 2025 ↗
gpt-4-turbo-2024-04-09 · DROP (Discrete Reasoning Over Paragraphs) · F1 · three-shot · OpenAI Simple Evals · archived July 2025 ↗
GPT-4-Turbo-2024-04-09 · Chatbot Arena · Chatbot Arena Vision · English Elo · 2024-11-13 · November 13, 2024 snapshot ↗
GPT-4 Turbo (2024-04-09) · MMLU-Redux (2.0) · Virology · corrected-instance accuracy · MMLU-Redux 2.0 paper · audited subject comparison ↗
4 more reported results
100% · GPT-4 Turbo (2024-04-09) · MMLU-Redux (2.0) · Logical Fallacies · corrected-instance accuracy · MMLU-Redux 2.0 paper · audited subject comparison · Are We Done with MMLU? ↗
76% · GPT-4 Turbo (2024-04-09) · MMLU-Redux (2.0) · College Chemistry · corrected-instance accuracy · MMLU-Redux 2.0 paper · audited subject comparison · Are We Done with MMLU? ↗
63% · GPT-4 Turbo (2024-04-09) · MMLU-Redux (2.0) · Professional Law · corrected-instance accuracy · MMLU-Redux 2.0 paper · audited subject comparison · Are We Done with MMLU? ↗
93% · GPT-4 Turbo (2024-04-09) · MMLU-Redux (2.0) · Human Sexuality · corrected-instance accuracy · MMLU-Redux 2.0 paper · audited subject comparison · Are We Done with MMLU? ↗
gpt-4-turbo-2024-04-09† · Overall difficulty-adjusted Brier index ↗
Closest models by score
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
5 of 8 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.
- Are We Done with MMLU? — published result table ↗
All 16 authors
Gema, Aryo Pradipta, Leang, Joshua Ong Jun, Hong, Giwon, Devoto, Alessio, Mancino, Alberto Carlo Maria, Saxena, Rohit, He, Xuanli, Zhao, Yu, Du, Xiaotang, Madani, Mohammad Reza Ghasemi, Barale, Claire, McHardy, Robert, Harris, Joshua, Kaddour, Jean, van Krieken, Emile, Minervini, Pasquale
- RepoQA: Evaluating Long Context Code Understanding — published result table ↗
All 8 authors
Jiawei Liu, Jia Le Tian, Vijay Daita, Yuxiang Wei, Yifeng Ding, Yuhan Katherine Wang, Jun Yang, Lingming Zhang
- BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions — published result table ↗
All 33 authors
Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, Simon Brunner, Chen Gong, Thong Hoang, Armel Randy Zebaze, Xiaoheng Hong, Wen-Ding Li, Jean Kaddour, Ming Xu, Zhihan Zhang, Prateek Yadav, Naman Jain, Alex Gu, Zhoujun Cheng, Jiawei Liu, Qian Liu, Zijian Wang, Binyuan Hui, Niklas Muennighoff, David Lo, Daniel Fried, Xiaoning Du, Harm de Vries, Leandro Von Werra
- Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models — published result table ↗
All 50 authors
Deitke, Matt, Clark, Christopher, Lee, Sangho, Tripathi, Rohun, Yang, Yue, Park, Jae Sung, Salehi, Mohammadreza, Muennighoff, Niklas, Lo, Kyle, Soldaini, Luca, Lu, Jiasen, Anderson, Taira, Bransom, Erin, Ehsani, Kiana, Ngo, Huong, Chen, YenSung, Patel, Ajay, Yatskar, Mark, Callison-Burch, Chris, Head, Andrew, Hendrix, Rose, Bastani, Favyen, VanderBilt, Eli, Lambert, Nathan, Chou, Yvonne, Chheda, Arnavi, Sparks, Jenna, Skjonsberg, Sam, Schmitz, Michael, Sarnat, Aaron, Bischoff, Byron, Walsh, Pete, Newell, Chris, Wolters, Piper, Gupta, Tanmay, Zeng, Kuo-Hao, Borchardt, Jon, Groeneveld, Dirk, Nam, Crystal, Lebrecht, Sophie, Wittlif, Caitlin, Schoenick, Carissa, Michel, Oscar, Krishna, Ranjay, Weihs, Luca, Smith, Noah A., Hajishirzi, Hannaneh, Girshick, Ross, Farhadi, Ali, Kembhavi, Aniruddha
- Forecastbench — methodology and published results ↗
- Simple Evals — model benchmark results ↗
Names used in source tables
GPT-4 Turbo (2024-04-09) · GPT-4-Turbo / 2024-04-09 · GPT-4-Turbo-2024-04-09 · gpt-4-turbo-2024-04-09 · gpt-4-turbo-2024-04-09†