AI Model OVERVIEW

LLaMA 3 70B by Meta AI

Llama 3 70B is a state-of-the-art open-source language model developed by Meta, built for high-performance dialogue, reasoning, and multilingual applications. Despite its size, it’s optimized for efficiency, offering near-parity with larger models while remaining accessible for real-world deployment. With expanded safety alignment and support for very long contexts, it's suitable for enterprise workloads.

Long Context Understanding

Supports context windows up to 128,000 tokens in specialized implementations, enabling coherent analysis of long documents and threads

Aligned and Safe Outputs

Trained using RLHF and supervised fine-tuning for improved helpfulness and reduced harmful or refused responses.

Key Parameters of LLaMA 3 70B

The AI model combines powerful transformer architecture with practical deployment flexibility, making it suitable for high-demand enterprise workflows and R&D environments.

Provider

OpenAI

Context Window

8,000 tokens (up to 128k in variants)

Maximum Output

2,048 tokens

Input Cost

$2.68 / 1M tokens

Output Cost

$3.54 / 1M tokens

Release Date

April 18, 2023

Knowledge Cut-Off

March 1, 2023

Multimodal

Enterprise Use Cases Evaluation

We benchmarked LLaMA 3 70B against real-world, enterprise-grade scenarios based on anonymized client case studies. Each use case was evaluated using our Automated Agent Evaluation tool.

Correctness

10.0

Formatting

10.0

Consistency

9.0

Sentiment

10.0

Clarity

9.0

coding Use Case

Micro-Refactoring for Codebases

The model produced a clear, well-structured refactored solution that adhered closely to the Flask API improvement instructions. While the implementation was technically sound and easy to follow, it did miss pagination, a requirement noted in the task, resulting in a minor gap in completeness.

Strengths Observed

Highly Structured Output
Excellent formatting, clarity, and consistency across the response

Technical Precision
Solid implementation of ORM, error handling, and modularity using Flask best practices

Maintainability Enhancements
Correct use of environment variables and type hints improves readability and scalability

Limitations in This Use Case

Missed Pagination Implementation
The task explicitly called for pagination, which was acknowledged but not executed

Partial Requirement Coverage
Suggests occasional lapses in following all detailed task components

LLaMA 3 70B by Meta AI

Key Parameters of LLaMA 3 70B

Enterprise Use Cases Evaluation

Micro-Refactoring for Codebases

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