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Discover how Open AI's GPT-4 and Mistral's Mistral Large stack up against each other in this comprehensive comparison of two leading AI language models.

Released in March 2023 and February 2024 respectively, these models represent significant advancements in artificial intelligence, with GPT-4 offering a 8,192-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 GPT-4 achieving 86.4% 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

Open AI GPT-4
Open AI Mistral Large

Provider

Company that developed the model
Open AI Mistral

Context Length

Maximum number of tokens the model can process
undefined 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
14-03-2023 26-02-2024

Knowledge Cutoff

Training data cutoff date
September 2021 Unknown

Open Source

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

API Providers

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

Pricing Comparison

Compare the pricing of Open AI's GPT-4 and Mistral's Mistral Large to determine the most cost-effective solution for your AI needs.

Open AI GPT-4
Open AI Mistral Large

Input Cost

Cost per million input tokens
$30 / 1M tokens $8 / 1M tokens

Output Cost

Cost per million tokens generated
$60 / 1M tokens $8 / 1M tokens

Comparing Benchmarks and Performance

Compare the performances of Open AI's GPT-4 and Mistral's Mistral Large on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.

Open AI GPT-4
Open AI Mistral Large

MMLU

Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
86.4% 81.2%

MMMU

A wide ranging multi-discipline and multimodal benchmark.
34.9% Benchmark not available

HellaSwag

A challenging sentence completion benchmark.
95.3% 89.2%

GSM8K

Grade-school math problems benchmark.
92% 81%

HumanEval

A benchmark to measure functional correctness for synthesizing programs from docstrings.
67% 45.1%

MATH

Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
Benchmark not available 45%

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