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Discover how Mistral's Mistral Large and Mistral's Mistral 7B Instruct stack up against each other in this comprehensive comparison of two leading AI language models.

Released in February 2024 and September 2023 respectively, these models represent significant advancements in artificial intelligence, with Mistral Large offering a 32,000-token context window and Mistral 7B Instruct featuring a 32,000-token capacity. Their distinct approaches to natural language processing are reflected in their benchmark performances, with Mistral Large achieving 81.2% on MMLU and Mistral 7B Instruct scoring 60.1%, making this comparison essential for developers and organizations seeking the right AI solution for their specific needs.

Models Overview

Mistral Mistral Large
Mistral Mistral 7B Instruct

Provider

Company that developed the model
Mistral Mistral

Context Length

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

Maximum Output

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

Release Date

Date when the model was released
26-02-2024 27-09-2023

Knowledge Cutoff

Training data cutoff date
Unknown Unknown

Open Source

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

API Providers

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

Pricing Comparison

Compare the pricing of Mistral's Mistral Large and Mistral's Mistral 7B Instruct to determine the most cost-effective solution for your AI needs.

Mistral Mistral Large
Mistral Mistral 7B Instruct

Input Cost

Cost per million input tokens
$8 / 1M tokens $0.25 / 1M tokens

Output Cost

Cost per million tokens generated
$8 / 1M tokens $0.25 / 1M tokens

Comparing Benchmarks and Performance

Compare the performances of Mistral's Mistral Large and Mistral's Mistral 7B Instruct on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.

Mistral Mistral Large
Mistral Mistral 7B Instruct

MMLU

Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
81.2% 60.1%

MMMU

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

HellaSwag

A challenging sentence completion benchmark.
89.2% 81%

GSM8K

Grade-school math problems benchmark.
81% 50%

HumanEval

A benchmark to measure functional correctness for synthesizing programs from docstrings.
45.1% 26.2%

MATH

Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
45% 12.7%

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