Understand and compare
Gemini 1.5 Pro
vs.
Llama 3 70B Instruct
Try
Podial
Turn your documents into engaging podcast discussions.
Overview
Gemini 1.5 Pro was released
2 months before
Llama 3 70B Instruct.
Gemini 1.5 Pro
|
Llama 3 70B Instruct
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
Google
|
Meta
|
|
Input Context Window
The number of tokens supported by the input context window.
|
1M
tokens
|
8,000
tokens
|
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
8,192
tokens
|
2,048
tokens
|
|
Release Date
When the model was first released.
|
2024-02-15
|
2024-04-18
|
|
Knowledge Cutoff
Limit on the knowledge base used by the model.
|
November 2023
|
December 2023
|
|
Open Source
|
|
|
|
API Providers
The providers that offer this model. (This is not an exhaustive list.)
|
|
|
Pricing
|
Gemini 1.5 Pro
|
Llama 3 70B Instruct
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
$7.00
per million tokens
|
Pricing not available.
|
|
Output
Cost of output tokens generated by the model.
|
$21.00
per million tokens
|
Pricing not available.
|
Benchmarks
Compare relevant benchmarks between Gemini 1.5 Pro
and Llama 3 70B Instruct.
|
Gemini 1.5 Pro
|
Llama 3 70B Instruct
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
81.9
(5-shot)
|
82.0
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
58.5
(0-shot)
|
Benchmark not available.
|
|
HellaSwag
A challenging sentence completion benchmark.
|
93.3
(10-shot)
|
Benchmark not available.
|
|
GSM8K
Grade-school math problems benchmark.
|
90.8
(11-shot)
|
93.0
(8-shot)
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
84.1
(0-shot)
|
Benchmark not available.
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
67.7
(4-shot Minerva Prompt)
|
51.0
(0-shot)
|
Gemini 1.5 Pro by Google features a vast context window of 1,000,000 tokens. The model is priced at 0.7 cents per thousand tokens for input and 2.1 cents per thousand tokens for output. It was launched on February 15, 2024. In benchmark tests, it achieved a score of 58.5 in MMMU with a 0-shot scenario and 81.9 in MMLU with a 5-shot scenario.
Llama 3 70B Instruct, developed by Meta, features a context window of 8000 tokens. The model was released on April 18, 2024, and achieved a score of 82.0 in the MMLU benchmark under a 5-shot scenario.
GPT-4o
Mistral 8x7B Instruct
Claude 2
Measure & Improve LLM
Product Performance.
Get Started
© 2023-2024 Context