Understand and compare
GPT-3.5 Turbo
vs.
Gemini Pro
Try
Podial
Turn your documents into engaging podcast discussions.
Overview
GPT-3.5 Turbo was released
about 1 year before
Gemini Pro.
GPT-3.5 Turbo
|
Gemini Pro
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
OpenAI
|
Google
|
|
Input Context Window
The number of tokens supported by the input context window.
|
4,096
tokens
|
32.8K
characters
|
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
4,096
tokens
|
8,192
characters
|
|
Release Date
When the model was first released.
|
2022-11-28
|
2023-12-13
|
|
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-3.5 Turbo
|
Gemini Pro
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
$0.50
per million tokens
|
Pricing not available.
|
|
Output
Cost of output tokens generated by the model.
|
$1.50
per million tokens
|
Pricing not available.
|
Benchmarks
Compare relevant benchmarks between GPT-3.5 Turbo
and Gemini Pro.
|
GPT-3.5 Turbo
|
Gemini Pro
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
70.0
(5-shot)
|
71.8
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
Benchmark not available.
|
47.9
(pass@1)
|
|
HellaSwag
A challenging sentence completion benchmark.
|
85.5
(10-shot)
|
84.7
(10-shot)
|
|
GSM8K
Grade-school math problems benchmark.
|
Benchmark not available.
|
77.9
(11-shot)
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
Benchmark not available.
|
67.7
(0-shot)
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
43.1
(0-shot)
|
32.6
(4-shot Minerva Prompt)
|
GPT-3.5 Turbo, developed by OpenAI, features a context window of 4096 tokens. It is priced at 0.05 cents per thousand tokens for input and 0.15 cents per thousand tokens for output. The model was released on November 28, 2022, and has achieved high scores in benchmarks like HellaSwag (85.5 in a 10-shot scenario) and MMLU (70.0 in 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.
Llama 3.1 70B Instruct
Mistral Large 2
Claude 3 Haiku
Measure & Improve LLM
Product Performance.
Get Started
© 2023-2024 Context