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Discover how Open AI's GPT-3.5 Turbo and Meta's Llama 3.2 90B stack up against each other in this comprehensive comparison of two leading AI language models.

Released in November 2022 and September 2024 respectively, these models represent significant advancements in artificial intelligence, with GPT-3.5 Turbo offering a 16,385-token context window and Llama 3.2 90B featuring a 128,000-token capacity. Their distinct approaches to natural language processing are reflected in their benchmark performances, with GPT-3.5 Turbo achieving 70% on MMLU and Llama 3.2 90B scoring 86%, making this comparison essential for developers and organizations seeking the right AI solution for their specific needs.

Models Overview

Open AI GPT-3.5 Turbo
Open AI Llama 3.2 90B

Provider

Company that developed the model
Open AI Meta

Context Length

Maximum number of tokens the model can process
16.39K 128K

Maximum Output

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

Release Date

Date when the model was released
28-11-2022 25-09-2024

Knowledge Cutoff

Training data cutoff date
September 2021 December 2023

Open Source

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

API Providers

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

Pricing Comparison

Compare the pricing of Open AI's GPT-3.5 Turbo and Meta's Llama 3.2 90B to determine the most cost-effective solution for your AI needs.

Open AI GPT-3.5 Turbo
Open AI Llama 3.2 90B

Input Cost

Cost per million input tokens
$0.5 / 1M tokens Pricing not available

Output Cost

Cost per million tokens generated
$1.5 / 1M tokens Pricing not available

Comparing Benchmarks and Performance

Compare the performances of Open AI's GPT-3.5 Turbo and Meta's Llama 3.2 90B on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.

Open AI GPT-3.5 Turbo
Open AI Llama 3.2 90B

MMLU

Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
70% 86%

MMMU

A wide ranging multi-discipline and multimodal benchmark.
Benchmark not available 60.3%

HellaSwag

A challenging sentence completion benchmark.
85.5% 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.
Benchmark not available Benchmark not available

MATH

Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
43.1% 68%

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