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Discover how Google's Gemini Pro and Mistral's Mistral Large stack up against each other in this comprehensive comparison of two leading AI language models.

Released in December 2023 and February 2024 respectively, these models represent significant advancements in artificial intelligence, with Gemini Pro offering a 32,800-token context window and Mistral Large featuring a 32,000-token capacity. Their distinct approaches to natural language processing are reflected in their benchmark performances, with Gemini Pro achieving 71.8% on MMLU and Mistral Large scoring 81.2%, making this comparison essential for developers and organizations seeking the right AI solution for their specific needs.

Models Overview

Google Gemini Pro
Google Mistral Large

Provider

Company that developed the model
Google Mistral

Context Length

Maximum number of tokens the model can process
32.8K 32K

Maximum Output

Maximum number of tokens the model can generate in a single response
8192 4096

Release Date

Date when the model was released
13-12-2023 26-02-2024

Knowledge Cutoff

Training data cutoff date
Unknown Unknown

Open Source

Whether the model's code is open-source
FALSE TRUE

API Providers

API providers that offer access to the model
Vertex AI Azure AI, AWS Bedrock, Google Cloud Vertex AI Model Garden, Snowflake Cortex, Hugging Face

Pricing Comparison

Compare the pricing of Google's Gemini Pro and Mistral's Mistral Large to determine the most cost-effective solution for your AI needs.

Google Gemini Pro
Google Mistral Large

Input Cost

Cost per million input tokens
Pricing not available $8 / 1M tokens

Output Cost

Cost per million tokens generated
Pricing not available $8 / 1M tokens

Comparing Benchmarks and Performance

Compare the performances of Google's Gemini Pro and Mistral's Mistral Large on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.

Google Gemini Pro
Google Mistral Large

MMLU

Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
71.8% 81.2%

MMMU

A wide ranging multi-discipline and multimodal benchmark.
47.9% Benchmark not available

HellaSwag

A challenging sentence completion benchmark.
84.7% 89.2%

GSM8K

Grade-school math problems benchmark.
77.9% 81%

HumanEval

A benchmark to measure functional correctness for synthesizing programs from docstrings.
67.7% 45.1%

MATH

Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
32.6% 45%

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