Understand and compare
GPT-4
vs.
Gemini Ultra
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Podial
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Overview
GPT-4
|
Gemini Ultra
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
OpenAI
|
Google
|
|
Input Context Window
The number of tokens supported by the input context window.
|
8,192
tokens
|
32.8K
characters
|
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
8,192
tokens
|
8,192
characters
|
|
Release Date
When the model was first released.
|
2023-03-14
|
Unknown
|
|
Knowledge Cutoff
Limit on the knowledge base used by the model.
|
September 2021
|
Unknown
|
|
Open Source
|
|
|
|
API Providers
The providers that offer this model. (This is not an exhaustive list.)
|
|
|
Pricing
|
GPT-4
|
Gemini Ultra
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
$30.00
per million tokens
|
Pricing not available.
|
|
Output
Cost of output tokens generated by the model.
|
$60.00
per million tokens
|
Pricing not available.
|
Benchmarks
Compare relevant benchmarks between GPT-4
and Gemini Ultra.
|
GPT-4
|
Gemini Ultra
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
86.4
(5-shot)
|
83.7
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
34.9
|
59.4
(0-shot pass@1)
|
|
HellaSwag
A challenging sentence completion benchmark.
|
95.3
(10-shot)
|
Benchmark not available.
|
|
GSM8K
Grade-school math problems benchmark.
|
92.0
(5-shot)
|
88.9
(11-shot)
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
67.0
(0-shot)
|
74.4
(0-shot)
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
Benchmark not available.
|
53.2
(4-shot Minerva Prompt)
|
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.
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.
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