GPT-4o (Nov) (non-reasoning)

This model's tendency to be less verbose is a built-in cost-saving feature. You pay for fewer output tokens compared to more loquacious models. To maximize this benefit:

• Craft prompts that ask for direct, brief answers (e.g., "Summarize in three bullet points" or "Extract the key entities").
• Avoid open-ended prompts that might encourage longer, less focused responses.
• Use its conciseness as a feature for applications like headline generation, tweet creation, or quick summaries where brevity is valued.

The $2.50 per million input tokens is the primary cost driver for many applications. Controlling this is essential for economic viability.

• Pre-process documents: Before sending a large document, use a cheaper, simpler model or a rule-based script to extract only the most relevant sections.
• Summarize recursively: For documents exceeding the context window or for cost savings, create summaries of chunks and then summarize the summaries.
• Cache results: For repeated queries with identical inputs, store the result to avoid reprocessing and incurring costs again.

The choice between OpenAI and Azure has direct cost and performance implications, even with identical list prices.

• Choose OpenAI for performance-critical apps: If your application's success depends on low latency and high throughput (e.g., a real-time chatbot), the performance gains from using OpenAI's API will likely outweigh potential cost savings from Azure.
• Choose Azure for enterprise and potential discounts: If you are already an Azure customer, you may be eligible for committed use discounts or other pricing advantages. The integration with Azure's security and compliance framework can also reduce operational overhead.

GPT-4o (Nov)'s intelligence is average. Forcing it to perform tasks beyond its capabilities is inefficient and costly.

• Identify reasoning tasks: If your workflow involves complex logic, multi-step problem solving, or advanced mathematics, use a dedicated reasoning model. Using GPT-4o (Nov) will lead to more errors, retries, and ultimately higher costs.
• Use cheaper models for simple tasks: For basic classification, sentiment analysis, or simple data extraction, a much cheaper and smaller model will suffice. Reserve GPT-4o (Nov) for tasks that need its specific blend of speed, large context, and moderate intelligence.

GPT-4o (Nov) is a specific version of OpenAI's GPT-4o model, released in November 2024. It is differentiated by its strong focus on performance, delivering higher output speeds and lower latency compared to many other GPT-4 variants. This comes at the cost of slightly lower performance on pure reasoning benchmarks, positioning it as a 'workhorse' model for general-purpose, speed-sensitive applications.

Its intelligence, measured at 27 on the Artificial Analysis Intelligence Index, is considered below average when compared against the entire field of models, which includes top-tier reasoning specialists. It is less capable at complex, multi-step logic than models like GPT-4 Turbo. However, for a wide range of tasks like summarization, translation, and general Q&A, its intelligence is more than sufficient.

It can be, but with a caveat. Its natural tendency towards conciseness means it might produce shorter, more direct text than you'd want for elaborate storytelling or descriptive prose. It's excellent for generating quick ideas, outlines, or short-form content. For longer, more nuanced creative pieces, a more verbose model might be a better choice.

Multimodal means the model can process more than one type of data in a single input. For GPT-4o (Nov), this specifically refers to its ability to accept and understand both text and images. You can upload an image and ask questions about it, have it describe what's happening, or read text within the image, all in one prompt.

The input price of $2.50 per million tokens is higher than the market average for models in a similar performance class (which is around $2.00). This makes it relatively more costly for use cases that involve processing very large amounts of text, such as analyzing entire books, long legal documents, or extensive codebases.

The decision is a classic speed vs. integration trade-off. Use OpenAI if your top priority is the lowest possible latency and the highest throughput for a user-facing application. Use Microsoft Azure if you are embedded in the Azure ecosystem, require its enterprise-grade security and compliance features, or can access volume-based pricing discounts that make it more economical at scale.