{"id":4011,"date":"2024-07-24T04:48:42","date_gmt":"2024-07-23T23:48:42","guid":{"rendered":"https:\/\/www.edopedia.com\/blog\/?p=4011"},"modified":"2025-10-21T06:30:55","modified_gmt":"2025-10-21T01:30:55","slug":"llama-3-1-vs-gpt-4-benchmarks","status":"publish","type":"post","link":"https:\/\/www.edopedia.com\/blog\/llama-3-1-vs-gpt-4-benchmarks\/","title":{"rendered":"Llama 3.1 vs GPT-4 Benchmarks"},"content":{"rendered":"\n

We evaluated the performance of Llama 3.1 vs GPT-4<\/strong> models on over 150 benchmark datasets covering a wide range of languages. Additionally, we conducted extensive human evaluations comparing Llama 3.1 to GPT-4 in real-world scenarios. Our experimental results indicate that the Llama 3.1 405B model is competitive with GPT-4 across various tasks. Furthermore, the smaller Llama 3.1 models (8B and 70B) also perform well against both closed and open models with a similar number of parameters.<\/p>\n\n\n\n

Benchmark Performance: Llama 3.1 vs GPT-4<\/h2>\n\n\n\n

To objectively compare\u00a0Llama 3.1 vs GPT-4<\/strong>, let\u2019s examine some key benchmark results:<\/p>\n\n\n\n

General<\/h3>\n\n\n\n
Benchmark<\/td>Llama 3.1 8B<\/td>Llama 3.1 70B<\/td>Llama 3.1 405B<\/td>GPT-4<\/td><\/tr>
MMLU (0-shot, CoT)<\/td>73.0<\/td>86.0<\/td>88.6<\/td>85.4<\/td><\/tr>
MMLU PRO (5-shot, CoT)<\/td>48.3<\/td>66.4<\/td>73.3<\/td>64.8<\/td><\/tr>
IFEval<\/td>80.4<\/td>87.5<\/td>88.6<\/td>84.3<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Code Generation<\/h3>\n\n\n\n
Benchmark<\/td>Llama 3.1 8B<\/td>Llama 3.1 70B<\/td>Llama 3.1 405B<\/td>GPT-4<\/td><\/tr>
HumanEval (0-shot)<\/td>72.6<\/td>80.5<\/td>89.0<\/td>86.6<\/td><\/tr>
MBPP EvalPlus (base) (0-shot)<\/td>72.8<\/td>86.0<\/td>88.6<\/td>83.6<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Math<\/h3>\n\n\n\n
Benchmark<\/td>Llama 3.1 8B<\/td>Llama 3.1 70B<\/td>Llama 3.1 405B<\/td>GPT-4<\/td><\/tr>
GSM8K (8-shot, CoT)<\/td>84.5<\/td>95.1<\/td>96.8<\/td>94.2<\/td><\/tr>
MATH (0-shot, CoT)<\/td>51.9<\/td>68.0<\/td>73.8<\/td>64.5<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Reasoning<\/h3>\n\n\n\n
Benchmark<\/td>Llama 3.1 8B<\/td>Llama 3.1 70B<\/td>Llama 3.1 405B<\/td>GPT-4<\/td><\/tr>
ARC Challenge (0-shot)<\/td>83.4<\/td>94.8<\/td>96.9<\/td>96.4<\/td><\/tr>
GPQA (0-shot, CoT)<\/td>32.8<\/td>46.7<\/td>51.1<\/td>41.4<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Tool use<\/h3>\n\n\n\n
Benchmark<\/td>Llama 3.1 8B<\/td>Llama 3.1 70B<\/td>Llama 3.1 405B<\/td>GPT-4<\/td><\/tr>
BFCL<\/td>76.1<\/td>84.8<\/td>88.5<\/td>88.3<\/td><\/tr>
Nexus<\/td>38.5<\/td>56.7<\/td>58.7<\/td>50.3<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Long context<\/h3>\n\n\n\n
Benchmark<\/td>Llama 3.1 8B<\/td>Llama 3.1 70B<\/td>Llama 3.1 405B<\/td>GPT-4<\/td><\/tr>
ZeroSCROLLS\/QuALITY<\/td>81.0<\/td>90.5<\/td>95.2<\/td>95.2<\/td><\/tr>
InfiniteBench\/En.MC<\/td>65.1<\/td>78.2<\/td>83.4<\/td>72.1<\/td><\/tr>
NIH\/Multi-needle<\/td>98.8<\/td>97.5<\/td>98.1<\/td>100.0<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Multilingual<\/h3>\n\n\n\n
Benchmark<\/td>Llama 3.1 8B<\/td>Llama 3.1 70B<\/td>Llama 3.1 405B<\/td>GPT-4<\/td><\/tr>
Multilingual MGSM (0-shot)<\/td>68.9<\/td>86.9<\/td>91.6<\/td>85.9<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Overall Benchmark Analysis<\/h2>\n\n\n\n

The benchmark results reveal that Llama 3.1 models consistently perform at a competitive level with GPT-4. The Llama 3.1 405B model excels across various categories, often surpassing GPT-4, particularly in tasks like math and reasoning. Even the smaller Llama 3.1 models (8B and 70B) demonstrate impressive capabilities, showing strong performance in multilingual and code generation tasks.<\/p>\n\n\n\n

Is Llama 3.1 Better than GPT-4?<\/h2>\n\n\n\n

Based on the benchmark results, Llama 3.1 shows advantages over GPT-4 in specific areas, particularly in code generation and reasoning tasks. The 405B model of Llama 3.1 consistently outperforms or matches GPT-4 across a wide range of tasks. However, GPT-4 still holds its ground in certain areas, such as long-context understanding, where it matches the performance of Llama 3.1.<\/p>\n\n\n\n

Capabilities and Performance<\/h2>\n\n\n\n

Both Llama 3.1 and GPT-4 possess robust capabilities in natural language understanding, code generation, and multilingual processing. Llama 3.1 models are particularly strong in mathematical problem-solving and tool use, which are crucial for applications requiring logical reasoning and data analysis. GPT-4, with its well-rounded performance, remains a formidable model in language processing and context comprehension.<\/p>\n\n\n\n

Applications and Use Cases<\/h2>\n\n\n\n

Llama 3.1 and GPT-4 can be applied in diverse domains:<\/p>\n\n\n\n

    \n
  1. Code Generation<\/strong>: Both models assist developers in generating and refining code, with Llama 3.1 demonstrating exceptional capabilities in creating accurate and efficient code snippets.<\/li>\n\n\n\n
  2. Multilingual Translation<\/strong>: The multilingual capabilities of these models allow for seamless translation and localization of content, supporting global communication.<\/li>\n\n\n\n
  3. Education and Learning<\/strong>: Their reasoning and problem-solving abilities make these models suitable for educational tools that provide tutoring and support in subjects like mathematics and science.<\/li>\n\n\n\n
  4. Customer Support<\/strong>: These AI models can enhance customer service by providing quick and accurate responses to inquiries in multiple languages.<\/li>\n<\/ol>\n\n\n\n

    Implications for the Future of AI<\/h2>\n\n\n\n

    The advancements in models like Llama 3.1 and GPT-4 indicate a promising future for AI technology. Their ability to perform complex tasks with high accuracy suggests potential improvements in automation, decision-making, and personalized user experiences. As these models continue to evolve, they will likely drive innovations in AI applications across industries.<\/p>\n\n\n\n

    Conclusion<\/h2>\n\n\n\n

    In conclusion, the Llama 3.1 models, especially the 405B variant, are strong contenders in the AI landscape, rivaling GPT-4 in many key areas. Their robust performance across a variety of benchmarks highlights their versatility and potential for widespread application. As AI models continue to develop, their impact on technology and society is poised to grow significantly.<\/p>\n","protected":false},"excerpt":{"rendered":"

    We evaluated the performance of Llama 3.1 vs GPT-4 models on over 150 benchmark datasets covering a wide range of languages. Additionally, we conducted extensive human evaluations comparing Llama 3.1 to GPT-4 in real-world scenarios. Our experimental results indicate that the Llama 3.1 405B model is competitive with GPT-4 across various tasks. Furthermore, the smaller … Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":4019,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[124],"tags":[],"class_list":["post-4011","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-comparisons"],"yoast_head":"\nLlama 3.1 vs GPT-4 Benchmarks<\/title>\n<meta name=\"description\" content=\"We evaluated the performance of Llama 3.1 vs GPT-4 models on over 150 benchmark datasets covering a wide range of languages. 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