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Discover how Meta's Llama 3.2 3B and Mistral's Mistral 8x7B Instruct stack up against each other in this comprehensive comparison of two leading AI language models.

Released in September 2024 and December 2023 respectively, these models represent significant advancements in artificial intelligence, with Llama 3.2 3B offering a 128,000-token context window and Mistral 8x7B Instruct featuring a 32,000-token capacity. Their distinct approaches to natural language processing are reflected in their benchmark performances, with Llama 3.2 3B achieving 63.4% on MMLU and Mistral 8x7B Instruct scoring 70.6%, making this comparison essential for developers and organizations seeking the right AI solution for their specific needs.

Models Overview

Meta Llama 3.2 3B
Meta Mistral 8x7B Instruct

Provider

Company that developed the model
Meta Mistral

Context Length

Maximum number of tokens the model can process
128K 32K

Maximum Output

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

Release Date

Date when the model was released
25-09-2024 11-12-2023

Knowledge Cutoff

Training data cutoff date
December 2023 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, Vertex AI, NVIDIA NIM, IBM watsonx, Hugging Face Azure AI, AWS Bedrock, Google Cloud Vertex AI Model Garden, Snowflake Cortex, Hugging Face

Pricing Comparison

Compare the pricing of Meta's Llama 3.2 3B and Mistral's Mistral 8x7B Instruct to determine the most cost-effective solution for your AI needs.

Meta Llama 3.2 3B
Meta Mistral 8x7B Instruct

Input Cost

Cost per million input tokens
Pricing not available $0.7 / 1M tokens

Output Cost

Cost per million tokens generated
Pricing not available $0.7 / 1M tokens

Comparing Benchmarks and Performance

Compare the performances of Meta's Llama 3.2 3B and Mistral's Mistral 8x7B Instruct on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.

Meta Llama 3.2 3B
Meta Mistral 8x7B Instruct

MMLU

Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
63.4% 70.6%

MMMU

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

HellaSwag

A challenging sentence completion benchmark.
69.8% 84.4%

GSM8K

Grade-school math problems benchmark.
77.7% 74.4%

HumanEval

A benchmark to measure functional correctness for synthesizing programs from docstrings.
Benchmark not available 40.2%

MATH

Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
48% 28.4%

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