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Discover how Meta's Llama 3.1 405B Instruct and Anthropic's Claude 3.5 Sonnet stack up against each other in this comprehensive comparison of two leading AI
language models.
Released in July 2024 and June 2024 respectively, these models represent significant advancements in artificial intelligence,
with Llama 3.1 405B Instruct offering a 128,000-token context
window and Claude 3.5 Sonnet featuring a 200,000-token
capacity. Their distinct approaches to natural language processing are reflected in their
benchmark performances, with Llama 3.1 405B Instruct achieving 88.6% on MMLU and Claude 3.5 Sonnet scoring 90.4%, making this comparison essential
for developers and organizations seeking the right AI solution for their specific needs.
Models Overview
 Llama 3.1 405B Instruct |  Claude 3.5 Sonnet | |
|---|---|---|
Provider Company that developed the model | Meta | Anthropic |
Context Length Maximum number of tokens the model can process | 128K | 200K |
Maximum Output Maximum number of tokens the model can generate in a single response | 2048 | 4096 |
Release Date Date when the model was released | 23-07-2024 | 20-06-2024 |
Knowledge Cutoff Training data cutoff date | December 2023 | April 2024 |
Open Source Whether the model's code is open-source | TRUE | FALSE |
API Providers API providers that offer access to the model | Azure AI, AWS Bedrock, Vertex AI, NVIDIA NIM, IBM watsonx, Hugging Face | Anthropic API, Vertex AI, AWS Bedrock |
Pricing Comparison
Compare the pricing of Meta's Llama 3.1 405B Instruct and Anthropic's Claude 3.5 Sonnet to determine the most cost-effective solution for your AI needs.
| Llama 3.1 405B Instruct | Claude 3.5 Sonnet | |
|---|---|---|
Input Cost Cost per million input tokens | Pricing not available | $3 / 1M tokens |
Output Cost Cost per million tokens generated | Pricing not available | $15 / 1M tokens |
Comparing Benchmarks and Performance
Compare the performances of Meta's Llama 3.1 405B Instruct and Anthropic's Claude 3.5 Sonnet on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.
| Llama 3.1 405B Instruct | Claude 3.5 Sonnet | |
|---|---|---|
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings. | 88.6% | 90.4% |
MMMU A wide ranging multi-discipline and multimodal benchmark. | Benchmark not available | 70.4% |
HellaSwag A challenging sentence completion benchmark. | Benchmark not available | Benchmark not available |
GSM8K Grade-school math problems benchmark. | 96.8% | 96.4% |
HumanEval A benchmark to measure functional correctness for synthesizing programs from docstrings. | 89% | 93.7% |
MATH Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines. | 73.8% | 78.3% |
