Understand and compare
Llama 3 8B Instruct
vs.
GPT-4
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Podial
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Overview
Llama 3 8B Instruct was released
about 1 year after
GPT-4.
Llama 3 8B Instruct
|
GPT-4
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
Meta
|
OpenAI
|
|
Input Context Window
The number of tokens supported by the input context window.
|
8,000
tokens
|
8,192
tokens
|
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
2,048
tokens
|
8,192
tokens
|
|
Release Date
When the model was first released.
|
2024-04-18
|
2023-03-14
|
|
Knowledge Cutoff
Limit on the knowledge base used by the model.
|
March 2023
|
September 2021
|
|
Open Source
|
|
|
|
API Providers
The providers that offer this model. (This is not an exhaustive list.)
|
|
|
Pricing
|
Llama 3 8B Instruct
|
GPT-4
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
Pricing not available.
|
$30.00
per million tokens
|
|
Output
Cost of output tokens generated by the model.
|
Pricing not available.
|
$60.00
per million tokens
|
Benchmarks
Compare relevant benchmarks between Llama 3 8B Instruct
and GPT-4.
|
Llama 3 8B Instruct
|
GPT-4
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
68.4
(5-shot)
|
86.4
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
Benchmark not available.
|
34.9
|
|
HellaSwag
A challenging sentence completion benchmark.
|
Benchmark not available.
|
95.3
(10-shot)
|
|
GSM8K
Grade-school math problems benchmark.
|
80.6
(8-shot)
|
92.0
(5-shot)
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
Benchmark not available.
|
67.0
(0-shot)
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
29.1
(0-shot)
|
Benchmark not available.
|
Llama 3 8B Instruct, developed by Meta, features a context window of 8000 tokens. The model was released on April 18, 2024, and achieved a score of 68.4 in the MMLU benchmark.
GPT-4, developed by OpenAI, features a context window of 8192 tokens. The model costs 3.0 cents per thousand tokens for input and 6.0 cents per thousand tokens for output. It was released on March 14, 2023, and has achieved impressive scores in benchmarks like HellaSwag with a score of 95.3 in a 10-shot scenario and MMLU with a score of 86.4 in a 5-shot scenario.
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