Compare Mistral Large vs. Gemini Ultra

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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.	February 26th, 2024 9 months ago 2024-02-26	Unknown
Knowledge Cutoff Limit on the knowledge base used by the model.	Unknown	Unknown
Open Source	Yes	No
API Providers The providers that offer this model. (This is not an exhaustive list.)	Azure AI, AWS Bedrock, Google Cloud Vertex AI Model Garden, Snowflake Cortex	Google Cloud Vertex AI Model Garden

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) Source	83.7 (5-shot) Source
MMMU A wide ranging multi-discipline and multimodal benchmark.	Benchmark not available.	59.4 (0-shot pass@1) Source
HellaSwag A challenging sentence completion benchmark.	89.2 (10-shot) Source	Benchmark not available.
GSM8K Grade-school math problems benchmark.	Benchmark not available.	88.9 (11-shot) Source
HumanEval A benchmark to measure functional correctness for synthesizing programs from docstrings.	Benchmark not available.	74.4 (0-shot) Source
MATH Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.	Benchmark not available.	53.2 (4-shot Minerva Prompt) Source

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.

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