Understand and compare
Gemini 1.5 Pro
vs.
Mistral 8x7B Instruct
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Podial
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Overview
Gemini 1.5 Pro was released
2 months after
Mistral 8x7B Instruct.
Gemini 1.5 Pro
|
Mistral 8x7B Instruct
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
Google
|
Mistral
|
|
Input Context Window
The number of tokens supported by the input context window.
|
1M
tokens
|
32K
tokens
|
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
8,192
tokens
|
4,096
tokens
|
|
Release Date
When the model was first released.
|
2024-02-15
|
2023-12-11
|
|
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 is
roughly 10x more expensive compared
to Mistral 8x7B Instruct for input tokens and
roughly 30x more expensive
for output tokens.
|
Gemini 1.5 Pro
|
Mistral 8x7B Instruct
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
$7.00
per million tokens
|
$0.70
per million tokens
|
|
Output
Cost of output tokens generated by the model.
|
$21.00
per million tokens
|
$0.70
per million tokens
|
Benchmarks
Compare relevant benchmarks between Gemini 1.5 Pro
and Mistral 8x7B Instruct.
|
Gemini 1.5 Pro
|
Mistral 8x7B Instruct
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
81.9
(5-shot)
|
70.6
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
58.5
(0-shot)
|
Benchmark not available.
|
|
HellaSwag
A challenging sentence completion benchmark.
|
93.3
(10-shot)
|
Benchmark not available.
|
|
GSM8K
Grade-school math problems benchmark.
|
90.8
(11-shot)
|
Benchmark not available.
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
84.1
(0-shot)
|
Benchmark not available.
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
67.7
(4-shot Minerva Prompt)
|
Benchmark not available.
|
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.
Mistral 8x7B Instruct, developed by Mistral, features a context window of 32000 tokens. The model costs 0.07 cents per thousand tokens for both input and output. It was released on December 11, 2023, and achieved a score of 70.6 in the MMLU benchmark in a 5-shot scenario.
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