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Discover how Open AI's GPT-4 and Meta's Llama 3.1 405B Instruct stack up against each other in this comprehensive comparison of two leading AI
language models.
Released in March 2023 and July 2024 respectively, these models represent significant advancements in artificial intelligence,
with GPT-4 offering a 8,192-token context
window and Llama 3.1 405B Instruct featuring a 128,000-token
capacity. Their distinct approaches to natural language processing are reflected in their
benchmark performances, with GPT-4 achieving 86.4% on MMLU and Llama 3.1 405B Instruct scoring 88.6%, making this comparison essential
for developers and organizations seeking the right AI solution for their specific needs.
Models Overview
 GPT-4 |  Llama 3.1 405B Instruct | |
|---|---|---|
Provider Company that developed the model | Open AI | Meta |
Context Length Maximum number of tokens the model can process | undefined | 128K |
Maximum Output Maximum number of tokens the model can generate in a single response | 8192 | 2048 |
Release Date Date when the model was released | 14-03-2023 | 23-07-2024 |
Knowledge Cutoff Training data cutoff date | September 2021 | December 2023 |
Open Source Whether the model's code is open-source | FALSE | TRUE |
API Providers API providers that offer access to the model | OpenAI API | Azure AI, AWS Bedrock, Vertex AI, NVIDIA NIM, IBM watsonx, Hugging Face |
Pricing Comparison
Compare the pricing of Open AI's GPT-4 and Meta's Llama 3.1 405B Instruct to determine the most cost-effective solution for your AI needs.
| GPT-4 | Llama 3.1 405B Instruct | |
|---|---|---|
Input Cost Cost per million input tokens | $30 / 1M tokens | Pricing not available |
Output Cost Cost per million tokens generated | $60 / 1M tokens | Pricing not available |
Comparing Benchmarks and Performance
Compare the performances of Open AI's GPT-4 and Meta's Llama 3.1 405B Instruct on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.
| GPT-4 | Llama 3.1 405B Instruct | |
|---|---|---|
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings. | 86.4% | 88.6% |
MMMU A wide ranging multi-discipline and multimodal benchmark. | 34.9% | Benchmark not available |
HellaSwag A challenging sentence completion benchmark. | 95.3% | Benchmark not available |
GSM8K Grade-school math problems benchmark. | 92% | 96.8% |
HumanEval A benchmark to measure functional correctness for synthesizing programs from docstrings. | 67% | 89% |
MATH Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines. | Benchmark not available | 73.8% |
