Understand and compare
GPT-4o 2024-05-13
vs.
Llama 3.1 8B Instruct
Try
Podial
Turn your documents into engaging podcast discussions.
Overview
GPT-4o 2024-05-13 was released
2 months before
Llama 3.1 8B Instruct.
GPT-4o 2024-05-13
|
Llama 3.1 8B Instruct
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
OpenAI
|
Meta
|
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Input Context Window
The number of tokens supported by the input context window.
|
128K
tokens
|
128K
tokens
|
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
2,048
tokens
|
2,048
tokens
|
|
Release Date
When the model was first released.
|
2024-05-13
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2024-07-23
|
|
Knowledge Cutoff
Limit on the knowledge base used by the model.
|
October 2023
|
December 2023
|
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Open Source
|
|
|
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API Providers
The providers that offer this model. (This is not an exhaustive list.)
|
|
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Pricing
|
GPT-4o 2024-05-13
|
Llama 3.1 8B Instruct
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
$5.00
per million tokens
|
Pricing not available.
|
|
Output
Cost of output tokens generated by the model.
|
$15.00
per million tokens
|
Pricing not available.
|
Benchmarks
Compare relevant benchmarks between GPT-4o 2024-05-13
and Llama 3.1 8B Instruct.
|
GPT-4o 2024-05-13
|
Llama 3.1 8B Instruct
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
88.7
(5-shot)
|
66.7
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
69.1
|
Benchmark not available.
|
|
HellaSwag
A challenging sentence completion benchmark.
|
Benchmark not available.
|
Benchmark not available.
|
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GSM8K
Grade-school math problems benchmark.
|
Benchmark not available.
|
84.5
(8-shot)
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
91.0
(0-shot)
|
Benchmark not available.
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
Benchmark not available.
|
51.9
(0-shot)
|
Claude 2
Mistral Large 2
Measure & Improve LLM
Product Performance.
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