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Discover how Google's Gemini Pro and Meta's Llama 2 Chat 70B stack up against each other in this comprehensive comparison of two leading AI
language models.
Released in December 2023 and July 2023 respectively, these models represent significant advancements in artificial intelligence,
with Gemini Pro offering a 32,800-token context
window and Llama 2 Chat 70B featuring a 4,096-token
capacity. Their distinct approaches to natural language processing are reflected in their
benchmark performances, with Gemini Pro achieving 71.8% on MMLU and Llama 2 Chat 70B scoring 68.9%, making this comparison essential
for developers and organizations seeking the right AI solution for their specific needs.
Models Overview
 Gemini Pro |  Llama 2 Chat 70B | |
|---|---|---|
Provider Company that developed the model | Meta | |
Context Length Maximum number of tokens the model can process | 32.8K | undefined |
Maximum Output Maximum number of tokens the model can generate in a single response | 8192 | 2048 |
Release Date Date when the model was released | 13-12-2023 | 18-07-2023 |
Knowledge Cutoff Training data cutoff date | Unknown | September 2022 |
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 Pro and Meta's Llama 2 Chat 70B to determine the most cost-effective solution for your AI needs.
| Gemini Pro | Llama 2 Chat 70B | |
|---|---|---|
Input Cost Cost per million input tokens | Pricing not available | Pricing not available |
Output Cost Cost per million tokens generated | Pricing not available | Pricing not available |
Comparing Benchmarks and Performance
Compare the performances of Google's Gemini Pro and Meta's Llama 2 Chat 70B on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.
| Gemini Pro | Llama 2 Chat 70B | |
|---|---|---|
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings. | 71.8% | 68.9% |
MMMU A wide ranging multi-discipline and multimodal benchmark. | 47.9% | 30.1% |
HellaSwag A challenging sentence completion benchmark. | 84.7% | 85.3% |
GSM8K Grade-school math problems benchmark. | 77.9% | 56.8% |
HumanEval A benchmark to measure functional correctness for synthesizing programs from docstrings. | 67.7% | 29.9% |
MATH Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines. | 32.6% | Benchmark not available |
