Understand and compare
Gemini 1.5 Pro
vs.
Gemini Pro
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Podial
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Overview
Gemini 1.5 Pro was released
2 months after
Gemini Pro.
Gemini 1.5 Pro
|
Gemini Pro
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
Google
|
Google
|
|
Input Context Window
The number of tokens supported by the input context window.
|
1M
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.
|
2024-02-15
|
2023-12-13
|
|
Knowledge Cutoff
Limit on the knowledge base used by the model.
|
November 2023
|
Unknown
|
|
Open Source
|
|
|
|
API Providers
The providers that offer this model. (This is not an exhaustive list.)
|
|
|
Pricing
|
Gemini 1.5 Pro
|
Gemini Pro
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
$7.00
per million tokens
|
Pricing not available.
|
|
Output
Cost of output tokens generated by the model.
|
$21.00
per million tokens
|
Pricing not available.
|
Benchmarks
Compare relevant benchmarks between Gemini 1.5 Pro
and Gemini Pro.
|
Gemini 1.5 Pro
|
Gemini Pro
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
81.9
(5-shot)
|
71.8
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
58.5
(0-shot)
|
47.9
(pass@1)
|
|
HellaSwag
A challenging sentence completion benchmark.
|
93.3
(10-shot)
|
84.7
(10-shot)
|
|
GSM8K
Grade-school math problems benchmark.
|
90.8
(11-shot)
|
77.9
(11-shot)
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
84.1
(0-shot)
|
67.7
(0-shot)
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
67.7
(4-shot Minerva Prompt)
|
32.6
(4-shot Minerva Prompt)
|
Gemini 1.5 Pro by Google features a vast context window of 1,000,000 tokens. The model is priced at 0.7 cents per thousand tokens for input and 2.1 cents per thousand tokens for output. It was launched on February 15, 2024. In benchmark tests, it achieved a score of 58.5 in MMMU with a 0-shot scenario and 81.9 in MMLU with a 5-shot scenario.
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.
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