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Discover how Google's Gemini 1.5 Pro and Meta's Llama 3.3 70B stack up against each other in this comprehensive comparison of two leading AI
language models.
Released in February 2024 and December 2024 respectively, these models represent significant advancements in artificial intelligence,
with Gemini 1.5 Pro offering a 1,000,000-token context
window and Llama 3.3 70B featuring a 128,000-token
capacity. Their distinct approaches to natural language processing are reflected in their
benchmark performances, with Gemini 1.5 Pro achieving 81.9% on MMLU and Llama 3.3 70B scoring 86%, making this comparison essential
for developers and organizations seeking the right AI solution for their specific needs.
Models Overview
 Gemini 1.5 Pro |  Llama 3.3 70B | |
|---|---|---|
Provider Company that developed the model | Meta | |
Context Length Maximum number of tokens the model can process | 1M | 128K |
Maximum Output Maximum number of tokens the model can generate in a single response | 8192 | Unknown |
Release Date Date when the model was released | 15-02-2024 | 06-12-2024 |
Knowledge Cutoff Training data cutoff date | November 2023 | December 2023 |
Open Source Whether the model's code is open-source | FALSE | TRUE |
API Providers API providers that offer access to the model | Vertex AI | Azure AI, AWS Bedrock, Vertex AI, NVIDIA NIM, IBM watsonx, Hugging Face |
Pricing Comparison
Compare the pricing of Google's Gemini 1.5 Pro and Meta's Llama 3.3 70B to determine the most cost-effective solution for your AI needs.
| Gemini 1.5 Pro | Llama 3.3 70B | |
|---|---|---|
Input Cost Cost per million input tokens | $7 / 1M tokens | $0.59 / 1M tokens |
Output Cost Cost per million tokens generated | $21 / 1M tokens | $0.77 / 1M tokens |
Comparing Benchmarks and Performance
Compare the performances of Google's Gemini 1.5 Pro and Meta's Llama 3.3 70B on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.
| Gemini 1.5 Pro | Llama 3.3 70B | |
|---|---|---|
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings. | 81.9% | 86% |
MMMU A wide ranging multi-discipline and multimodal benchmark. | 58.5% | Benchmark not available |
HellaSwag A challenging sentence completion benchmark. | 93.3% | Benchmark not available |
GSM8K Grade-school math problems benchmark. | 90.8% | Benchmark not available |
HumanEval A benchmark to measure functional correctness for synthesizing programs from docstrings. | 84.1% | 86% |
MATH Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines. | 67.7% | 76% |
