AI Model OVERVIEW

GPT-4 Model by OpenAI

GPT-4 is a flagship LLM known for its superior reasoning, clean generation, and tool-using capabilities. It powers advanced agents across industries, from legal automation to data transformation and customer support. GPT-4 supports vision input, multilingual tasks, and structured API calling, making it a reliable core for enterprise-grade AI systems.

Robust Reasoning Capabilities

Best-in-class on logic-heavy tasks and chain-of-thought problem solving.

Fine-Tuning Ready

Supports domain-specific model customization via gpt-4-0613 variant.

Key Parameters of GPT - 4

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Provider

OpenAI

Context Window

8,192 tokens

Maximum Output

8,192 tokens

Input Cost

$30.00 / 1M tokens

Output Cost

$60.00 / 1M tokens

Release Date

March 14, 2023

Knowledge Cut-Off

September 1, 2021

Multimodal

Output Cost

Enterprise Use Cases Evaluation

We benchmarked GPT-4 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

GPT-4 showed exceptional capability in handling structured code refactoring tasks, demonstrating deep understanding of modular architecture and best practices in Python web development. The model correctly applied advanced patterns such as separation of concerns, use of environment variables, and replacement of raw SQL with ORM (SQLAlchemy), without explicit prompting for each.

Strengths Observed

High-level architectural reasoning.
Clean decomposition into config.py, models.py, and modular route files.

Security-aware decisions.
Switched from raw SQL to ORM for safer DB interaction.

Code clarity.
Added type hints, proper formatting, and datetime serialization via a custom encoder.

Production readiness.
Included robust error handling, logging, pagination, and parameter validation without overengineering.

Limitations

Lacked minor contextual commentary on API method coverage beyond GET (e.g., POST, PUT)
— a documentation and reasoning gap, not a functional error.

coding Use Case

Micro-Refactoring for Codebases

Correctness

10.0

Formatting

10.0

Consistency

9.0

Sentiment

10.0

Clarity

9.0

Strengths Observed

High-level architectural reasoning.
Clean decomposition into config.py, models.py, and modular route files.

Security-aware decisions.
Switched from raw SQL to ORM for safer DB interaction.

Code clarity.
Added type hints, proper formatting, and datetime serialization via a custom encoder.

Production readiness.
Included robust error handling, logging, pagination, and parameter validation without overengineering.

Limitations

Lacked minor contextual commentary on API method coverage beyond GET (e.g., POST, PUT)
— a documentation and reasoning gap, not a functional error.

GPT-4 Model by OpenAI

Key Parameters of GPT - 4

Enterprise Use Cases Evaluation

Micro-Refactoring for Codebases

Micro-Refactoring for Codebases

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