Llama-3-70B-Instruct · OlympiadBench (Math) · Mathematics · Text-only problems ↗
Meta / MODEL EVIDENCE
Llama 3-70B-Instruct
15 benchmarks · 50 observations · 12 sources
Scores at a glance
15 benchmarks · large dot: Llama 3-70B-Instruct; small dots: closest models where reported
Llama-3-70B-Instruct · OlympiadBench · physics · Text-only problems ↗
Meta-Llama-3-70B-Instruct · RepoQA & LongCodeArena · RepoQA retrieval accuracy · similarity threshold 0.8 ↗
Llama-3-70B-Instruct · ComplexBench · DRFR · all constraints · RAL evaluation ↗
Llama-3-70b-instruct · SORRY-Bench · Base harmful compliance · original prompts ↗
Llama3-70B-instruct · HumanEval+ · Pass@1 · EvalPlus · official result data ↗
Llama3-70B-instruct · MBPP+ · Pass@1 · EvalPlus · official result data ↗
Llama3-70B-instruct · HumanEval · Pass@1 · EvalPlus · official result data ↗
Llama3-instruct / 70B · BigCodeBench & BigCodeBench-Hard · BigCodeBench Complete · greedy accuracy ↗
1 more reported result
43.2% · Llama3-instruct / 70B · BigCodeBench & BigCodeBench-Hard · BigCodeBench Instruct · greedy accuracy · BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions ↗
Llama-3-70B-Instruct · VisualWebArena · Overall task success · updated model comparison ↗
Llama-3-70B-Instruct · ChemBench · Overall accuracy · published model comparison ↗
1 more reported result
60% · Llama-3-70B-Instruct · ChemBench · Human-evaluation subset · accuracy · published model comparison · Are large language models superhuman chemists? ↗
Meta-Llama-3-70B-Instruct · ProcessBench · Average F1 · original comparison ↗
Llama-3-70B-Instruct∗ · CFBench (Chinese Complex Following) · Full set · Constraint satisfaction · full-set evaluation ↗
2 more reported results
30% · Llama-3-70B-Instruct∗ · CFBench (Chinese Complex Following) · Full set · Instruction satisfaction · full-set evaluation · CFBench: A Comprehensive Constraints-Following Benchmark for LLMs ↗
41.4% · Llama-3-70B-Instruct∗ · CFBench (Chinese Complex Following) · Full set · Priority satisfaction · full-set evaluation · CFBench: A Comprehensive Constraints-Following Benchmark for LLMs ↗
LLaMA-3-70B-Instruct · MixEval & MixEval-Hard · accuracy · original comparison ↗
1 more reported result
84% · LLaMA-3-70B-Instruct · MixEval & MixEval-Hard · accuracy · original comparison · MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures ↗
Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · LitQA2 ↗
32 more reported results
20% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SuppQA · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
0% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · FigQA · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
0% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · TableQA · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
25.8% · Meta-Llama-3-70B-Instruct · LAB-Bench · DbQA · subtask macro average · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
44% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · ProtocolQA · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
36.6667% · Meta-Llama-3-70B-Instruct · LAB-Bench · SeqQA · subtask macro average · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
24% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · CloningScenarios · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
25% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · DbQA_dga_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
20% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · DbQA_gene_location_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
29% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · DbQA_mirna_targets_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
53% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · DbQA_mouse_tumor_gene_sets-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
20% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · DbQA_oncogenic_signatures_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
19% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · DbQA_tfbs_GTRD_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
30% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · DbQA_variant_from_sequence_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
7% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · DbQA_variant_multi_sequence_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
15% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · DbQA_vax_response_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
40% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · DbQA_viral_ppi_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
19% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SeqQA_ORF-seq-AAid-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
49% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SeqQA_ORF-seq-AAseq-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
23% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SeqQA_ORF-seq-numlen-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
45% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SeqQA_ORF-transeff-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
58% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SeqQA_PCR-gene-enzprimers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
45% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SeqQA_PCR-gene-gibshindprimers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
58% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SeqQA_PCR-gene-gibssmaprimers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
37% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SeqQA_PCR-geneprimers-enz-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
6% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SeqQA_PCR-len-primers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
21% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SeqQA_PCR-primers-len-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
73% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SeqQA_PCR-seq-enzprimers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
47% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SeqQA_PCR-seq-primers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
35% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SeqQA_Prop-seq-gcpercent-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
19% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SeqQA_RE-seq-lenfrags-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
15% · Meta-Llama-3-70B-Instruct · LAB-Bench (v1) · Accuracy · SeqQA_RE-seq-numfrags-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
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.
- VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks — published result table ↗
All 10 authors
Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Chong Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Ruslan Salakhutdinov, Daniel Fried
- 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
- MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures — published result table ↗
All 8 authors
Jinjie Ni, Fuzhao Xue, Xiang Yue, Yuntian Deng, Mahir Shah, Kabir Jain, Graham Neubig, Yang You
- 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
- 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
- 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
- LAB-Bench: Measuring Capabilities of Language Models for Biology Research — published result table ↗
All 9 authors
Jon M. Laurent, Joseph D. Janizek, Michael Ruzo, Michaela M. Hinks, Michael J. Hammerling, Siddharth Narayanan, Manvitha Ponnapati, Andrew D. White, Samuel G. Rodriques
- CFBench: A Comprehensive Constraints-Following Benchmark for LLMs — published result table ↗
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
- ProcessBench: Identifying Process Errors in Mathematical Reasoning — published result table ↗
All 9 authors
Zheng, Chujie, Zhang, Zhenru, Zhang, Beichen, Lin, Runji, Lu, Keming, Yu, Bowen, Liu, Dayiheng, Zhou, Jingren, Lin, Junyang
- Are large language models superhuman chemists? — published result table ↗
All 35 authors
Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu, Martiño Ríos-García, Benedict Emoekabu, Aswanth Krishnan, Tanya Gupta, Mara Schilling-Wilhelmi, Macjonathan Okereke, Anagha Aneesh, Amir Mohammad Elahi, Mehrdad Asgari, Juliane Eberhardt, Hani M. Elbeheiry, María Victoria Gil, Maximilian Greiner, Caroline T. Holick, Christina Glaubitz, Tim Hoffmann, Abdelrahman Ibrahim, Lea C. Klepsch, Yannik Köster, Fabian Alexander Kreth, Jakob Meyer, Santiago Miret, Jan Matthias Peschel, Michael Ringleb, Nicole Roesner, Johanna Schreiber, Ulrich S. Schubert, Leanne M. Stafast, Dinga Wonanke, Michael Pieler, Philippe Schwaller, Kevin Maik Jablonka
- Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation — published result table ↗
- OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems — published result table ↗
All 14 authors
Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Leng Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, Maosong Sun
Names used in source tables
LLaMA-3-70B-Instruct · Llama-3-70B-Instruct · Llama-3-70B-Instruct∗ · Llama-3-70b-instruct · Llama3-70B-instruct · Llama3-instruct / 70B · Meta-Llama-3-70B-Instruct