Compare Gemini Pro vs. Mistral 7B Instruct

Pricing About Us Blog Docs

Book a demo Get Started

Compare

Understand and compare Gemini Pro vs. Mistral 7B Instruct

Generate side-by-side samples for these models in the Context.ai playground

Overview

Gemini Pro was released 3 months after Mistral 7B Instruct.

	Gemini Pro	Mistral 7B Instruct
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	8,192 tokens
Release Date When the model was first released.	December 13th, 2023 7 months ago 2023-12-13	September 27th, 2023 9 months ago 2023-09-27

Pricing

	Gemini Pro	Mistral 7B Instruct
Input Cost of input data provided to the model.	Pricing not available.	$0.25 per million tokens
Output Cost of output tokens generated by the model.	Pricing not available.	$0.25 per million tokens

Benchmarks

Compare relevant benchmarks between Gemini Pro and Mistral 7B Instruct.

	Gemini Pro	Mistral 7B Instruct
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.	71.8 (5-shot) Source	60.1 (5-shot) Source
MMMU A wide ranging multi-discipline and multimodal benchmark.	47.9 (pass@1) Source	Benchmark not available.
HellaSwag A challenging sentence completion benchmark.	Benchmark not available.	Benchmark not available.

Gemini Pro, developed by Google, features a context window of 32768 tokens. The model costs 0.0125 cents per thousand tokens for input and 0.0375 cents per thousand tokens for output. It was released on December 13, 2023, and has achieved a score of 47.9 in the MMMU benchmark with a "pass@1" caveat and a score of 71.8 in the MMLU benchmark in a 5-shot scenario.

Mistral 7B Instruct, developed by Mistral, features a large context window of 32000 tokens. The model is priced at 0.025 cents per thousand tokens for both input and output. It was released on September 27, 2023, and achieved a score of 60.1 in the MMLU benchmark under a 5-shot scenario.

Compare more models

Gemini Pro vs.

GPT-4 Turbo 0125