Understand and compare
Mistral 7B Instruct
vs.
Gemini Pro
Try
Podial
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Overview
Mistral 7B Instruct was released
3 months before
Gemini Pro.
Mistral 7B Instruct
|
Gemini Pro
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
Mistral
|
Google
|
|
Input Context Window
The number of tokens supported by the input context window.
|
32K
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.
|
2023-09-27
|
2023-12-13
|
|
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
|
Mistral 7B Instruct
|
Gemini Pro
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
$0.25
per million tokens
|
Pricing not available.
|
|
Output
Cost of output tokens generated by the model.
|
$0.25
per million tokens
|
Pricing not available.
|
Benchmarks
Compare relevant benchmarks between Mistral 7B Instruct
and Gemini Pro.
|
Mistral 7B Instruct
|
Gemini Pro
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
60.1
(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.
|
Benchmark not available.
|
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.
|
Benchmark not available.
|
32.6
(4-shot Minerva Prompt)
|
Mistral 7B Instruct, developed by Mistral, features a large context window of 32000 tokens. The model is priced at 0.025 cents per thousand tokens for both input and output. It was released on September 27, 2023, and achieved a score of 60.1 in the MMLU benchmark under 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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