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Discover how Anthropic's Claude 2 and Meta's Llama 3.2 3B stack up against each other in this comprehensive comparison of two leading AI language models.

Released in July 2023 and September 2024 respectively, these models represent significant advancements in artificial intelligence, with Claude 2 offering a 100,000-token context window and Llama 3.2 3B featuring a 128,000-token capacity. Their distinct approaches to natural language processing are reflected in their benchmark performances, with Claude 2 achieving 78.5% on MMLU and Llama 3.2 3B scoring 63.4%, making this comparison essential for developers and organizations seeking the right AI solution for their specific needs.

Models Overview

Anthropic Claude 2
Anthropic Llama 3.2 3B

Provider

Company that developed the model
Anthropic Meta

Context Length

Maximum number of tokens the model can process
100K 128K

Maximum Output

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

Release Date

Date when the model was released
11-07-2023 25-09-2024

Knowledge Cutoff

Training data cutoff date
Early 2023 December 2023

Open Source

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

API Providers

API providers that offer access to the model
Anthropic API, Vertex AI, AWS Bedrock Azure AI, AWS Bedrock, Vertex AI, NVIDIA NIM, IBM watsonx, Hugging Face

Pricing Comparison

Compare the pricing of Anthropic's Claude 2 and Meta's Llama 3.2 3B to determine the most cost-effective solution for your AI needs.

Anthropic Claude 2
Anthropic Llama 3.2 3B

Input Cost

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

Output Cost

Cost per million tokens generated
$24 / 1M tokens Pricing not available

Comparing Benchmarks and Performance

Compare the performances of Anthropic's Claude 2 and Meta's Llama 3.2 3B on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.

Anthropic Claude 2
Anthropic Llama 3.2 3B

MMLU

Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
78.5% 63.4%

MMMU

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

HellaSwag

A challenging sentence completion benchmark.
Benchmark not available 69.8%

GSM8K

Grade-school math problems benchmark.
Benchmark not available 77.7%

HumanEval

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

MATH

Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
Benchmark not available 48%

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