Understand and compare
Mistral Large
vs.
Gemini Ultra
Overview
Mistral Large
|
Gemini Ultra
|
|
|---|---|---|
|
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
|
Unknown
|
Pricing
|
Mistral Large
|
Gemini Ultra
|
|
|---|---|---|
|
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 Ultra.
|
Mistral Large
|
Gemini Ultra
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
81.2
(5-shot)
|
83.7
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
Benchmark not available.
|
59.4
(0-shot pass@1)
|
|
HellaSwag
A challenging sentence completion benchmark.
|
89.2
(10-shot)
|
Benchmark not available.
|
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 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.
Claude 3 Haiku
GPT-3.5 Turbo 0125
Llama 2 Chat 13B
Measure & Improve LLM
Product Performance.
Get Started
© 2023-2024 Context