Understand and compare
Llama 3 70B Instruct
vs.
Gemini Pro
Try
Podial
Turn your documents into engaging podcast discussions.
Overview
Llama 3 70B Instruct was released
4 months after
Gemini Pro.
Llama 3 70B Instruct
|
Gemini Pro
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
Meta
|
Google
|
|
Input Context Window
The number of tokens supported by the input context window.
|
8,000
tokens
|
32.8K
characters
|
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
2,048
tokens
|
8,192
characters
|
|
Release Date
When the model was first released.
|
2024-04-18
|
2023-12-13
|
|
Knowledge Cutoff
Limit on the knowledge base used by the model.
|
December 2023
|
Unknown
|
|
Open Source
|
|
|
|
API Providers
The providers that offer this model. (This is not an exhaustive list.)
|
|
|
Pricing
|
Llama 3 70B Instruct
|
Gemini Pro
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
Pricing not available.
|
Pricing not available.
|
|
Output
Cost of output tokens generated by the model.
|
Pricing not available.
|
Pricing not available.
|
Benchmarks
Compare relevant benchmarks between Llama 3 70B Instruct
and Gemini Pro.
|
Llama 3 70B Instruct
|
Gemini Pro
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
82.0
(5-shot)
|
71.8
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
Benchmark not available.
|
47.9
(pass@1)
|
|
HellaSwag
A challenging sentence completion benchmark.
|
Benchmark not available.
|
84.7
(10-shot)
|
|
GSM8K
Grade-school math problems benchmark.
|
93.0
(8-shot)
|
77.9
(11-shot)
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
Benchmark not available.
|
67.7
(0-shot)
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
51.0
(0-shot)
|
32.6
(4-shot Minerva Prompt)
|
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.
Gemini Pro, developed by Google, features a context window of 32768 tokens. The model costs 0.0125 cents per thousand tokens for input and 0.0375 cents per thousand tokens for output. It was released on December 13, 2023, and has achieved a score of 47.9 in the MMMU benchmark with a "pass@1" caveat and a score of 71.8 in the MMLU benchmark in a 5-shot scenario.
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