Understand and compare
Gemini Ultra
vs.
Mistral Large
Try
Podial
Turn your documents into engaging podcast discussions.
Overview
Gemini Ultra
|
Mistral Large
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
Google
|
Mistral
|
|
Input Context Window
The number of tokens supported by the input context window.
|
32.8K
characters
|
32K
tokens
|
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
8,192
characters
|
4,096
tokens
|
|
Release Date
When the model was first released.
|
Unknown
|
2024-02-26
|
|
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
|
Gemini Ultra
|
Mistral Large
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
Pricing not available.
|
$8.00
per million tokens
|
|
Output
Cost of output tokens generated by the model.
|
Pricing not available.
|
$8.00
per million tokens
|
Benchmarks
Compare relevant benchmarks between Gemini Ultra
and Mistral Large.
|
Gemini Ultra
|
Mistral Large
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
83.7
(5-shot)
|
81.2
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
59.4
(0-shot pass@1)
|
Benchmark not available.
|
|
HellaSwag
A challenging sentence completion benchmark.
|
Benchmark not available.
|
89.2
(10-shot)
|
|
GSM8K
Grade-school math problems benchmark.
|
88.9
(11-shot)
|
Benchmark not available.
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
74.4
(0-shot)
|
Benchmark not available.
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
53.2
(4-shot Minerva Prompt)
|
Benchmark not available.
|
Gemini Ultra, developed by Google, features a large context window of 32768 tokens. The model has excelled in benchmarks like MMMU with a score of 59.4 in a 0-shot pass@1 scenario and MMLU with a score of 83.7 in a 5-shot scenario.
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).
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