Understand and compare
Gemini Pro
vs.
GPT-4 32K 0613
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Podial
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Overview
Gemini Pro was released
6 months after
GPT-4 32K 0613.
Gemini Pro
|
GPT-4 32K 0613
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
Google
|
OpenAI
|
|
Input Context Window
The number of tokens supported by the input context window.
|
32.8K
characters
|
32.8K
tokens
|
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
8,192
characters
|
Not specified.
|
|
Release Date
When the model was first released.
|
2023-12-13
|
2023-06-13
|
|
Knowledge Cutoff
Limit on the knowledge base used by the model.
|
Unknown
|
Unknown
|
|
Open Source
|
|
|
|
API Providers
The providers that offer this model. (This is not an exhaustive list.)
|
|
|
Pricing
|
Gemini Pro
|
GPT-4 32K 0613
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
Pricing not available.
|
$60.00
per million tokens
|
|
Output
Cost of output tokens generated by the model.
|
Pricing not available.
|
$120.00
per million tokens
|
Benchmarks
Compare relevant benchmarks between Gemini Pro
and GPT-4 32K 0613.
|
Gemini Pro
|
GPT-4 32K 0613
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
71.8
(5-shot)
|
Benchmark not available.
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
47.9
(pass@1)
|
Benchmark not available.
|
|
HellaSwag
A challenging sentence completion benchmark.
|
84.7
(10-shot)
|
Benchmark not available.
|
|
GSM8K
Grade-school math problems benchmark.
|
77.9
(11-shot)
|
Benchmark not available.
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
67.7
(0-shot)
|
Benchmark not available.
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
32.6
(4-shot Minerva Prompt)
|
Benchmark not available.
|
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.
GPT-4 32K 0613, developed by OpenAI, features a context window of 32768 tokens. The model costs 6.0 cents per thousand tokens for input and 12.0 cents per thousand tokens for output. It was released on June 13, 2023.
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