Understand and compare
Gemini Ultra
vs.
Mistral Large
Overview
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Provider
The entity that provides this model.
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Input Context Window
The number of tokens supported by the input context window.
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32.8K
characters
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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
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2024-02-26
|
Pricing
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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.
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MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
83.7
(5-shot)
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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)
|
![](https://with.context.ai/assets/google-c8f988d7a45b564da5965132d7479ae30327702e3e9fbc3df8f03c2842e0834e.png)
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).
![](https://with.context.ai/assets/mistral-9e9f2d79ccfc3f09ad90b1a79c4072f3ce8345e5582acb227da152e6db07b217.png)
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