Understand and compare
Mistral Large
vs.
Gemini 1.5 Pro
Overview
Mistral Large was released
11 days after
Gemini 1.5 Pro.
Mistral Large
|
Gemini 1.5 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
|
1M
tokens
|
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
4,096
tokens
|
8,192
tokens
|
|
Release Date
When the model was first released.
|
2024-02-26
|
2024-02-15
|
|
Knowledge Cutoff
Limit on the knowledge base used by the model.
|
Unknown
|
November 2023
|
|
Open Source
|
|
|
|
API Providers
The providers that offer this model. (This is not an exhaustive list.)
|
|
|
Pricing
Mistral Large is
roughly 14.3% more expensive compared
to Gemini 1.5 Pro for input tokens and
roughly 2.6x cheaper
for output tokens.
|
Mistral Large
|
Gemini 1.5 Pro
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
$8.00
per million tokens
|
$7.00
per million tokens
|
|
Output
Cost of output tokens generated by the model.
|
$8.00
per million tokens
|
$21.00
per million tokens
|
Benchmarks
Compare relevant benchmarks between Mistral Large
and Gemini 1.5 Pro.
|
Mistral Large
|
Gemini 1.5 Pro
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
81.2
(5-shot)
|
81.9
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
Benchmark not available.
|
58.5
(0-shot)
|
|
HellaSwag
A challenging sentence completion benchmark.
|
89.2
(10-shot)
|
93.3
(10-shot)
|
|
GSM8K
Grade-school math problems benchmark.
|
Benchmark not available.
|
90.8
(11-shot)
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
Benchmark not available.
|
84.1
(0-shot)
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
Benchmark not available.
|
67.7
(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 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.
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