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Discover how Meta's Llama 3.2 1B and Google's Gemini 1.5 Pro stack up against each other in this comprehensive comparison of two leading AI
language models.
Released in September 2024 and February 2024 respectively, these models represent significant advancements in artificial intelligence,
with Llama 3.2 1B offering a 128,000-token context
window and Gemini 1.5 Pro featuring a 1,000,000-token
capacity. Their distinct approaches to natural language processing are reflected in their
benchmark performances, with Llama 3.2 1B achieving 49.3% on MMLU and Gemini 1.5 Pro scoring 81.9%, making this comparison essential
for developers and organizations seeking the right AI solution for their specific needs.
Models Overview
 Llama 3.2 1B |  Gemini 1.5 Pro | |
|---|---|---|
Provider Company that developed the model | Meta | |
Context Length Maximum number of tokens the model can process | 128K | 1M |
Maximum Output Maximum number of tokens the model can generate in a single response | Unknown | 8192 |
Release Date Date when the model was released | 25-09-2024 | 15-02-2024 |
Knowledge Cutoff Training data cutoff date | December 2023 | November 2023 |
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 | Vertex AI |
Pricing Comparison
Compare the pricing of Meta's Llama 3.2 1B and Google's Gemini 1.5 Pro to determine the most cost-effective solution for your AI needs.
| Llama 3.2 1B | Gemini 1.5 Pro | |
|---|---|---|
Input Cost Cost per million input tokens | Pricing not available | $7 / 1M tokens |
Output Cost Cost per million tokens generated | Pricing not available | $21 / 1M tokens |
Comparing Benchmarks and Performance
Compare the performances of Meta's Llama 3.2 1B and Google's Gemini 1.5 Pro on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.
| Llama 3.2 1B | Gemini 1.5 Pro | |
|---|---|---|
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings. | 49.3% | 81.9% |
MMMU A wide ranging multi-discipline and multimodal benchmark. | Benchmark not available | 58.5% |
HellaSwag A challenging sentence completion benchmark. | 41.2% | 93.3% |
GSM8K Grade-school math problems benchmark. | 44.4% | 90.8% |
HumanEval A benchmark to measure functional correctness for synthesizing programs from docstrings. | Benchmark not available | 84.1% |
MATH Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines. | 30.6% | 67.7% |
