Understand and compare
Mistral Large
vs.
Gemini Pro
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Podial
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Overview
Mistral Large was released
3 months after
Gemini Pro.
Mistral Large
|
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.
|
4,096
tokens
|
8,192
characters
|
|
Release Date
When the model was first released.
|
2024-02-26
|
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 Large
|
Gemini Pro
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
$8.00
per million tokens
|
Pricing not available.
|
|
Output
Cost of output tokens generated by the model.
|
$8.00
per million tokens
|
Pricing not available.
|
Benchmarks
Compare relevant benchmarks between Mistral Large
and Gemini Pro.
|
Mistral Large
|
Gemini Pro
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
81.2
(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.
|
89.2
(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.
|
Benchmark not available.
|
32.6
(4-shot Minerva Prompt)
|
Mistral Large, developed by Mistral, features a context window of 32000 tokens. The model is priced at 0.8 cents per thousand tokens for both input and output. It was released on February 26, 2024, and has achieved impressive scores in benchmarks like MMLU (81.2 in a 5-shot scenario) and HellaSwag (89.2 in a 10-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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