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Discover how Meta's Llama 3.2 1B and Meta's Llama 3.1 405B Instruct stack up against each other in this comprehensive comparison of two leading AI language models.

Released in September 2024 and July 2024 respectively, these models represent significant advancements in artificial intelligence, with Llama 3.2 1B offering a 128,000-token context window and Llama 3.1 405B Instruct featuring a 128,000-token capacity. Their distinct approaches to natural language processing are reflected in their benchmark performances, with Llama 3.2 1B achieving 49.3% on MMLU and Llama 3.1 405B Instruct scoring 88.6%, making this comparison essential for developers and organizations seeking the right AI solution for their specific needs.

Models Overview

Meta Llama 3.2 1B
Meta Llama 3.1 405B Instruct

Provider

Company that developed the model
Meta Meta

Context Length

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

Maximum Output

Maximum number of tokens the model can generate in a single response
Unknown 2048

Release Date

Date when the model was released
25-09-2024 23-07-2024

Knowledge Cutoff

Training data cutoff date
December 2023 December 2023

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, Vertex AI, NVIDIA NIM, IBM watsonx, Hugging Face

Pricing Comparison

Compare the pricing of Meta's Llama 3.2 1B and Meta's Llama 3.1 405B Instruct to determine the most cost-effective solution for your AI needs.

Meta Llama 3.2 1B
Meta Llama 3.1 405B Instruct

Input Cost

Cost per million input tokens
Pricing not available Pricing not available

Output Cost

Cost per million tokens generated
Pricing not available Pricing not available

Comparing Benchmarks and Performance

Compare the performances of Meta's Llama 3.2 1B and Meta's Llama 3.1 405B Instruct on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.

Meta Llama 3.2 1B
Meta Llama 3.1 405B Instruct

MMLU

Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
49.3% 88.6%

MMMU

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

HellaSwag

A challenging sentence completion benchmark.
41.2% Benchmark not available

GSM8K

Grade-school math problems benchmark.
44.4% 96.8%

HumanEval

A benchmark to measure functional correctness for synthesizing programs from docstrings.
Benchmark not available 89%

MATH

Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
30.6% 73.8%

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