Llama 3.1-8B · GPQA diamond · Best score (across scorers) · % ↗
Meta / MODEL EVIDENCE
Llama 3.1-8B
22 benchmarks · 30 observations · 19 sources · released 2024-07-23
Scores at a glance
22 benchmarks · large dot: Llama 3.1-8B; small dots: closest models where reported
Llama 3.1-8B · MATH level 5 · Best score (across scorers) · % ↗
Llama 3.1-8B · OTIS Mock AIME 2024-2025 · Best score (across scorers) · % ↗
Llama 3.1-8B · Balrog · Average progress · % ↗
Llama 3.1-8B · GSM8K · EM · % ↗
Llama 3.1-8B · MMLU · EM · % ↗
Llama 3.1-8B · PIQA · Score · % ↗
Llama 3.1-8B · WeirdML · Accuracy · % ↗
Llama 3.1-8B · CritPt · Accuracy · % ↗
Llama 3.1-8B · LMCA · Score · % ↗
Llama 3.1-8B · DTBench · Accuracy · % ↗
Llama 3.1-8B · SciCode · Score · % ↗
Llama 3.1-8B · General ECI · 26 October 2026 fit ↗
Llama 3.1 8B · Scale Labs · small ↗
Llama-3.1-8B · INCLUDE · Original-language instructions · source dataset-card comparison ↗
Llama-3.1-8B · ZebraLogic · Grid-level accuracy · complete puzzles ↗
Llama-3.1-8B · HELMET (How to Evaluate Long-Context Models Effectively and Thoroughly) · HELMET source aggregate · source model comparison ↗
Llama-3.1-8B · InfiniteBench · InfiniteBench aggregate · HELMET paper · original-author harness ↗
Llama-3.1-8B · Putnam-AXIOM · Functional variation · accuracy · five-trial robustness comparison ↗
1 more reported result
6% · Llama-3.1-8B · 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 ↗
Llama 3.1 8b · HumanEval · Pass@1 · OpenAI Simple Evals · archived July 2025 ↗
Llama 3.1 8b · DROP (Discrete Reasoning Over Paragraphs) · F1 · three-shot · OpenAI Simple Evals · archived July 2025 ↗
LLaMA-3.1-8b · CRAFT-MD · Free response · Vignette accuracy · combined dataset ↗
7 more reported results
18.7% · LLaMA-3.1-8b · CRAFT-MD · Free response · Multi-turn conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
11.6% · LLaMA-3.1-8b · CRAFT-MD · Free response · Single-turn conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
16.3% · LLaMA-3.1-8b · CRAFT-MD · Free response · Summarized conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
72.3% · LLaMA-3.1-8b · CRAFT-MD · Four-choice MCQ · Vignette accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
51% · LLaMA-3.1-8b · CRAFT-MD · Four-choice MCQ · Multi-turn conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
42.1% · LLaMA-3.1-8b · CRAFT-MD · Four-choice MCQ · Single-turn conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
56% · LLaMA-3.1-8b · CRAFT-MD · Four-choice MCQ · Summarized conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
Closest models by score
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
3 of 4 shared benchmark comparisons
3 of 4 shared benchmark comparisons
3 of 4 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.
- SciCode Benchmark | Artificial Analysis ↗
- Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone ↗
All 129 authors
Marah Abdin, Jyoti Aneja, Hany Awadalla, Ahmed Awadallah, Ammar Ahmad Awan, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat Behl, Alon Benhaim, Misha Bilenko, Johan Bjorck, Sébastien Bubeck, Martin Cai, Qin Cai, Vishrav Chaudhary, Dong Chen, Dongdong Chen, Weizhu Chen, Yen-Chun Chen, Yi-Ling Chen, Hao Cheng, Parul Chopra, Xiyang Dai, Matthew Dixon, Ronen Eldan, Victor Fragoso, Jianfeng Gao, Mei Gao, Min Gao, Amit Garg, Allie Del Giorno, Abhishek Goswami, Suriya Gunasekar, Emman Haider, Junheng Hao, Russell J. Hewett, Wenxiang Hu, Jamie Huynh, Dan Iter, Sam Ade Jacobs, Mojan Javaheripi, Xin Jin, Nikos Karampatziakis, Piero Kauffmann, Mahoud Khademi, Dongwoo Kim, Young Jin Kim, Lev Kurilenko, James R. Lee, Yin Tat Lee, Yuanzhi Li, Yunsheng Li, Chen Liang, Lars Liden, Xihui Lin, Zeqi Lin, Ce Liu, Liyuan Liu, Mengchen Liu, Weishung Liu, Xiaodong Liu, Chong Luo, Piyush Madan, Ali Mahmoudzadeh, David Majercak, Matt Mazzola, Caio César Teodoro Mendes, Arindam Mitra, Hardik Modi, Anh Nguyen, Brandon Norick, Barun Patra, Daniel Perez-Becker, Thomas Portet, Reid Pryzant, Heyang Qin, Marko Radmilac, Liliang Ren, Gustavo de Rosa, Corby Rosset, Sambudha Roy, Olatunji Ruwase, Olli Saarikivi, Amin Saied, Adil Salim, Michael Santacroce, Shital Shah, Ning Shang, Hiteshi Sharma, Yelong Shen, Swadheen Shukla, Xia Song, Masahiro Tanaka, Andrea Tupini, Praneetha Vaddamanu, Chunyu Wang, Guanhua Wang, Lijuan Wang, Shuohang Wang, Xin Wang, Yu Wang, Rachel Ward, Wen Wen, Philipp Witte, Haiping Wu, Xiaoxia Wu, Michael Wyatt, Bin Xiao, Can Xu, Jiahang Xu, Weijian Xu, Jilong Xue, Sonali Yadav, Fan Yang, Jianwei Yang, Yifan Yang, Ziyi Yang, Donghan Yu, Lu Yuan, Chenruidong Zhang, Cyril Zhang, Jianwen Zhang, Li Lyna Zhang, Yi Zhang, Yue Zhang, Yunan Zhang, Xiren Zhou
- HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly — published result table ↗
All 8 authors
Howard Yen, Tianyu Gao, Minmin Hou, Ke Ding, Daniel Fleischer, Peter Izsak, Moshe Wasserblat, Danqi Chen
- ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning — published result table ↗
All 7 authors
Bill Yuchen Lin, Ronan Le Bras, Kyle Richardson, Ashish Sabharwal, Radha Poovendran, Peter Clark, Yejin Choi
- Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs — published result table ↗
All 8 authors
Gulati, Aryan, Miranda, Brando, Chen, Eric, Xia, Emily, Fronsdal, Kai, Dumont, Bruno, Obbad, Elyas, Koyejo, Sanmi
- BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games ↗
All 13 authors
Paglieri, Davide, Cupiał, Bartłomiej, Coward, Samuel, Piterbarg, Ulyana, Wolczyk, Maciej, Khan, Akbir, Pignatelli, Eduardo, Kuciński, Łukasz, Pinto, Lerrel, Fergus, Rob, Foerster, Jakob Nicolaus, Parker-Holder, Jack, Rocktäschel, Tim
- DTBench — Conceptual Reasoning Index ↗
- LMCA — Conceptual Reasoning Index ↗
- Holistic Evaluation of Language Models (HELM) ↗
- CritPt v1 ↗
- GPQA Diamond ↗
- MATH Level 5 ↗
- OTIS Mock AIME 2024-2025 ↗
- AI benchmarks & capabilities ↗
- WeirdML ↗
- INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge — published result table ↗
All 59 authors
Angelika Romanou, Negar Foroutan, Anna Sotnikova, Zeming Chen, Sree Harsha Nelaturu, Shivalika Singh, Rishabh Maheshwary, Micol Altomare, Mohamed A. Haggag, Snegha A, Alfonso Amayuelas, Azril Hafizi Amirudin, Viraat Aryabumi, Danylo Boiko, Michael Chang, Jenny Chim, Gal Cohen, Aditya Kumar Dalmia, Abraham Diress, Sharad Duwal, Daniil Dzenhaliou, Daniel Fernando Erazo Florez, Fabian Farestam, Joseph Marvin Imperial, Shayekh Bin Islam, Perttu Isotalo, Maral Jabbarishiviari, Börje F. Karlsson, Eldar Khalilov, Christopher Klamm, Fajri Koto, Dominik Krzemiński, Gabriel Adriano de Melo, Syrielle Montariol, Yiyang Nan, Joel Niklaus, Jekaterina Novikova, Johan Samir Obando Ceron, Debjit Paul, Esther Ploeger, Jebish Purbey, Swati Rajwal, Selvan Sunitha Ravi, Sara Rydell, Roshan Santhosh, Drishti Sharma, Marjana Prifti Skenduli, Arshia Soltani Moakhar, Bardia Soltani Moakhar, Ran Tamir, Ayush Kumar Tarun, Azmine Toushik Wasi, Thenuka Ovin Weerasinghe, Serhan Yilmaz, Mike Zhang, Imanol Schlag, Marzieh Fadaee, Sara Hooker, Antoine Bosselut
- Scale Labs — evaluation methodology and results ↗
- An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
All 12 authors
Shreya Johri, Jaehwan Jeong, Benjamin A. Tran, Daniel I. Schlessinger, Shannon Wongvibulsin, Leandra A. Barnes, Hong-Yu Zhou, Zhuo Ran Cai, Eliezer M. Van Allen, David Kim, Roxana Daneshjou, Pranav Rajpurkar
- Simple Evals — model benchmark results ↗
Names used in source tables
LLaMA-3.1-8b · Llama 3.1 8B · Llama 3.1 8b · Llama 3.1-8B · Llama-3.1-8B