Scores at a glance
6 benchmarks · large dot: Llama 3.1-70B-Inst; small dots: closest models where reported
Closest models by score
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
16.1 percentile-point gap5 of 6 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.
- HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly — published result table ↗
Howard Yen, Tianyu Gao, Minmin Hou, Ke Ding, Daniel Fleischer, Peter Izsak et al. · 2024
All 8 authors
Howard Yen, Tianyu Gao, Minmin Hou, Ke Ding, Daniel Fleischer, Peter Izsak, Moshe Wasserblat, Danqi Chen
- LongProc: Benchmarking Long-Context Language Models on Long Procedural Generation — published result table ↗
Xi Ye, Fangcong Yin, Yinghui He, Joie Zhang, Howard Yen, Tianyu Gao et al. · 2025
All 8 authors
Xi Ye, Fangcong Yin, Yinghui He, Joie Zhang, Howard Yen, Tianyu Gao, Greg Durrett, Danqi Chen
- KernelBench: Can LLMs Write Efficient GPU Kernels? — published result table ↗
Ouyang, Anne, Guo, Simon, Arora, Simran, Zhang, Alex L., Hu, William, Ré, Christopher et al. · 2025
All 7 authors
Ouyang, Anne, Guo, Simon, Arora, Simran, Zhang, Alex L., Hu, William, Ré, Christopher, Mirhoseini, Azalia
Names used in source tables
Llama 3.1-70B Inst. · Llama-3.1-70B-Inst