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Discover how Meta's Llama 3.3 70B and Anthropic's Claude 3.5 Haiku stack up against each other in this comprehensive comparison of two leading AI language models.

Released in December 2024 and October 2024 respectively, these models represent significant advancements in artificial intelligence, with Llama 3.3 70B offering a 128,000-token context window and Claude 3.5 Haiku featuring a 200,000-token capacity. Their distinct approaches to natural language processing are reflected in their benchmark performances, with Llama 3.3 70B achieving 86% on MMLU and Claude 3.5 Haiku scoring Unknown%, making this comparison essential for developers and organizations seeking the right AI solution for their specific needs.

Models Overview

Meta Llama 3.3 70B
Meta Claude 3.5 Haiku

Provider

Company that developed the model
Meta Anthropic

Context Length

Maximum number of tokens the model can process
128K 200K

Maximum Output

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

Release Date

Date when the model was released
06-12-2024 22-10-2024

Knowledge Cutoff

Training data cutoff date
December 2023 April 2024

Open Source

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

API Providers

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

Pricing Comparison

Compare the pricing of Meta's Llama 3.3 70B and Anthropic's Claude 3.5 Haiku to determine the most cost-effective solution for your AI needs.

Meta Llama 3.3 70B
Meta Claude 3.5 Haiku

Input Cost

Cost per million input tokens
$0.59 / 1M tokens $0.25 / 1M tokens

Output Cost

Cost per million tokens generated
$0.77 / 1M tokens $1.25 / 1M tokens

Comparing Benchmarks and Performance

Compare the performances of Meta's Llama 3.3 70B and Anthropic's Claude 3.5 Haiku on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.

Meta Llama 3.3 70B
Meta Claude 3.5 Haiku

MMLU

Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
86% Benchmark not available

MMMU

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

HellaSwag

A challenging sentence completion benchmark.
Benchmark not available Benchmark not available

GSM8K

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

HumanEval

A benchmark to measure functional correctness for synthesizing programs from docstrings.
86% 88.1%

MATH

Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
76% 69.4%

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