Understand and compare
Gemini Ultra
vs.
GPT-4 32K 0613
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Podial
Turn your documents into engaging podcast discussions.
Overview
Gemini Ultra
|
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.
|
Unknown
|
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.)
|
|
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Pricing
|
Gemini Ultra
|
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 Ultra
and GPT-4 32K 0613.
|
Gemini Ultra
|
GPT-4 32K 0613
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
83.7
(5-shot)
|
Benchmark not available.
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
59.4
(0-shot pass@1)
|
Benchmark not available.
|
|
HellaSwag
A challenging sentence completion benchmark.
|
Benchmark not available.
|
Benchmark not available.
|
|
GSM8K
Grade-school math problems benchmark.
|
88.9
(11-shot)
|
Benchmark not available.
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
74.4
(0-shot)
|
Benchmark not available.
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
53.2
(4-shot Minerva Prompt)
|
Benchmark not available.
|
Gemini Ultra, developed by Google, features a large context window of 32768 tokens. The model has excelled in benchmarks like MMMU with a score of 59.4 in a 0-shot pass@1 scenario and MMLU with a score of 83.7 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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