Meta / MODEL EVIDENCE

Llama 3.3 70B Instruct

7 benchmarks · 20 observations · 7 sources

Scores at a glance

7 benchmarks · large dot: Llama 3.3 70B Instruct; small dots: closest models where reported

5.740.3

Llama-3.3-70B-Instruct · PolyMath · PolyMath-Top · Mean accuracy across the published languages ↗

3 more reported results

7.3% · Llama-3.3-70B-Instruct · PolyMath · PolyMath-High · Mean accuracy across the published languages · PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts ↗

16.8% · Llama-3.3-70B-Instruct · PolyMath · PolyMath-Medium · Mean accuracy across the published languages · PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts ↗

64.2% · Llama-3.3-70B-Instruct · PolyMath · PolyMath-Low · Mean accuracy across the published languages · PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts ↗

8.7352.59

Llama-3.3-70B-Instruct · HalluLens · PreciseWikiQA correct answers · three-trial mean ↗

1 more reported result

66.86% · Llama-3.3-70B-Instruct · HalluLens · MixedEntities false acceptance · fabricated-entity acceptance · HalluLens: LLM Hallucination Benchmark ↗

ProcessBench ↗58 F1 points
5.187.9

Llama-3.3-70B-Instruct · ProcessBench · Average F1 · original comparison ↗

5.3657.16

LLaMA3.3-70B-Instruct · UGPhysics · English · overall accuracy · original evaluation ↗

2 more reported results

26.07% · LLaMA3.3-70B-Instruct · UGPhysics · Chinese · overall accuracy · original evaluation · UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models ↗

30.97% · LLaMA3.3-70B-Instruct · UGPhysics · Language average · overall accuracy · original evaluation · UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models ↗

7.169.2

Llama 3.3 70B Instruct · GAIA-2 · Gaia2 full · Execution pass@1 · baseline ReAct · three runs ↗

7 more reported results

11.5% · Llama 3.3 70B Instruct · GAIA-2 · Gaia2 full · Search pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗

1.7% · Llama 3.3 70B Instruct · GAIA-2 · Gaia2 full · Ambiguity pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗

1.9% · Llama 3.3 70B Instruct · GAIA-2 · Gaia2 full · Adaptability pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗

0.4% · Llama 3.3 70B Instruct · GAIA-2 · Gaia2 full · Time pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗

3.8% · Llama 3.3 70B Instruct · GAIA-2 · Gaia2 full · Noise pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗

4.6% · Llama 3.3 70B Instruct · GAIA-2 · Gaia2 full · Agent2Agent pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗

4.4% · Llama 3.3 70B Instruct · GAIA-2 · Gaia2 full · Overall pass@1 · baseline ReAct · three runs · Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments ↗

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.

  1. Aider refactoring benchmark ↗

    Paul Gauthier, Aider contributors

  2. ProcessBench: Identifying Process Errors in Mathematical Reasoning — published result table ↗

    Zheng, Chujie, Zhang, Zhenru, Zhang, Beichen, Lin, Runji, Lu, Keming, Yu, Bowen et al. · 2024

    All 9 authors

    Zheng, Chujie, Zhang, Zhenru, Zhang, Beichen, Lin, Runji, Lu, Keming, Yu, Bowen, Liu, Dayiheng, Zhou, Jingren, Lin, Junyang

  3. UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models — published result table ↗

    Xin Xu, Qiyun Xu, Tong Xiao, Tianhao Chen, Yuchen Yan, Jiaxin Zhang et al. · 2025

    All 9 authors

    Xin Xu, Qiyun Xu, Tong Xiao, Tianhao Chen, Yuchen Yan, Jiaxin Zhang, Shizhe Diao, Can Yang, Yang Wang

  4. HalluLens: LLM Hallucination Benchmark — published result table ↗

    Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang et al. · 2025

    All 8 authors

    Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang, Nicola Cancedda, Pascale Fung

  5. PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts — published result table ↗

    Yiming Wang, Pei Zhang, Jialong Tang, Haoran Wei, Baosong Yang, Rui Wang et al. · 2025

    All 16 authors

    Yiming Wang, Pei Zhang, Jialong Tang, Haoran Wei, Baosong Yang, Rui Wang, Chenshu Sun, Feitong Sun, Jiran Zhang, Junxuan Wu, Qiqian Cang, Yichang Zhang, Fei Huang, Junyang Lin, Fei Huang, Jingren Zhou

  6. Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments — published result table ↗

    Froger, Romain, Andrews, Pierre, Bettini, Matteo, Budhiraja, Amar, Cabral, Ricardo Silveira, Do, Virginie et al. · 2026

    All 24 authors

    Froger, Romain, Andrews, Pierre, Bettini, Matteo, Budhiraja, Amar, Cabral, Ricardo Silveira, Do, Virginie, Garreau, Emilien, Gaya, Jean-Baptiste, Laurençon, Hugo, Lecanu, Maxime, Malkan, Kunal, Mekala, Dheeraj, Ménard, Pierre, Bertran, Gerard Moreno-Torres, Piterbarg, Ulyana, Plekhanov, Mikhail, Rita, Mathieu, Rusakov, Andrey, Vorotilov, Vladislav, Wang, Mengjue, Yu, Ian, Benhalloum, Amine, Mialon, Grégoire, Scialom, Thomas

  7. Scale Labs — evaluation methodology and results ↗

    Scale Labs / SEAL · 2026

Names used in source tables

LLaMA3.3-70B-Instruct · Llama 3.3 70B Instruct · Llama-3.3-70B-Instruct · llama-3.3-70b-instruct