AI Model OVERVIEW

GPT-4o by OpenAI

GPT-4o is OpenAI’s fully multimodal model, capable of understanding and generating text, images, and audio in real time. The LLM is engineered for speed, clarity, and versatility across domains—from software engineering and STEM problem solving to creative content, data analysis, and visual comprehension.

Multimodal Intelligence

Understands and generates across text, vision, and audio, enabling seamless, real-time interactions.

STEM and Coding Optimized

Excels on math, science, and programming tasks; benchmarks improved on GPQA, MATH, and MMLU.

Key Parameters of GPT-4o

GPT-4o is a versatile model combining multimodal comprehension, high throughput, and enterprise-grade capabilities. It balances performance with ethical design principles and advanced instruction handling, making it an ideal choice for both production and experimentation.

Provider

OpenAI

Context Window

128,000 tokens

Maximum Output

16,384 tokens

Input Cost

$2.50 / 1M input tokens

Output Cost

$10.00 / 1M output tokens

Release Date

May 13, 2024

Knowledge Cut-Off

October 1, 2023

Multimodal

Yes

Enterprise Use Cases Evaluation

We benchmarked GPT-4o 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 strong, well-structured refactoring that closely followed the requirements. It demonstrated a solid grasp of Flask architecture, database integration, and scalable API practices. While minor formatting inconsistencies were noted, the response remained technically accurate, readable, and aligned with real-world engineering standards.

Strengths Observed

Accurate implementation
Effectively used SQLAlchemy, Flask Blueprints, input validation, pagination, and environment-based configuration

RESTful design adherence
Applied proper HTTP methods and status codes for clear API structure

Readable and clear output
Maintained clarity and consistency across the response

Limitations in This Use Case

Minor formatting deviations
Some parts, like custom JSON encoder structure and type annotations, lacked full alignment with formatting expectations

Room for refinement
Could benefit from stricter adherence to requested conventions in highly structured code tasks

GPT-4o by OpenAI

Key Parameters of GPT-4o

Enterprise Use Cases Evaluation

Micro-Refactoring for Codebases

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