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

The most effective cost-saving measure is to not use GPT-4o when a cheaper model will suffice. A model router or cascade system can intelligently route user prompts based on complexity.

• Initial Triage: Send all incoming requests to a much cheaper, faster model first (e.g., Claude 3 Haiku or GPT-3.5 Turbo).
• Escalation Path: If the cheaper model fails to provide a satisfactory answer (which you can determine via automated checks, user feedback, or keyword analysis), the request is then escalated to GPT-4o.
• Result: This reserves the expensive model for only the most challenging tasks, drastically cutting costs for the majority of simpler queries.

With output tokens costing three times as much as input tokens, controlling response length is critical. You can guide the model's verbosity directly in your prompts.

• Use System Prompts: Instruct the model to be concise. For example: "You are a helpful assistant. Your answers must be clear and brief. Do not exceed 100 words unless absolutely necessary."
• Request Structured Data: Ask for output in a structured format like JSON. This is often more compact than a natural language paragraph and is easier to parse in your application. For example: "Extract the user's name and request. Respond only with a JSON object containing 'name' and 'request' keys."
• Post-processing: If necessary, truncate the model's output in your application logic to enforce hard limits.

Many applications receive repetitive queries. Caching responses to identical prompts avoids redundant API calls and their associated costs.

• Exact Match Caching: Store the exact prompt and its corresponding response. Before making an API call, check if the prompt exists in your cache.
• Semantic Caching: For more advanced use cases, use embedding models to cache responses based on semantic similarity. If a new prompt is very similar to a previously answered one, you can serve the cached response.
• Cache Expiration: Set a reasonable time-to-live (TTL) for your cache to ensure information stays fresh, especially for queries that depend on recent data.

While the 128k context window is a powerful feature, filling it is expensive. Only provide the information that is strictly necessary for the task at hand.

• Context Summarization: For long-running conversations, instead of sending the entire chat history, create a rolling summary of the conversation and include that with the most recent messages.
• Data Chunking: When analyzing long documents, break the document into smaller, relevant chunks using an embedding-based search (RAG) instead of feeding the entire text into the prompt.
• Token-Aware Truncation: Before sending a request, count the tokens in your prompt and truncate less relevant information (e.g., older parts of a conversation) to stay within a self-imposed, cost-effective limit.

The 'o' stands for 'omni.' It signifies the model's native, built-in ability to understand and generate a mix of text, audio, and visual information. This is a departure from previous models that often required separate systems for tasks like speech-to-text or image analysis. GPT-4o integrates these capabilities into a single, cohesive neural network.

GPT-4o is positioned as the direct successor to GPT-4 Turbo. According to OpenAI, it matches GPT-4 Turbo's performance on text and code intelligence benchmarks while being significantly faster and 50% cheaper. Its main advantages are its speed, lower cost, and native multimodal capabilities, making it the preferred choice for most new applications.

It's a trade-off. While GPT-4o is highly capable, its score on the Artificial Analysis Intelligence Index (26) is below average for its premium price class. This suggests that for tasks requiring the absolute highest level of nuanced, multi-step reasoning, other specialized or more expensive models might perform better. However, GPT-4o's incredible speed may make it 'smarter' in practice for interactive use cases where a fast, very good answer is better than a slow, perfect one.

Native multimodality unlocks more intuitive and powerful applications. For example, a user could:

• Point their phone camera at a piece of furniture and ask the model how to assemble it.
• Have a real-time spoken conversation with an AI tutor that can see their math homework and hear their questions.
• Upload a chart or graph and ask for a summary of the key trends.

This removes the latency and complexity of stitching together separate models for vision, speech, and text.

API providers run models on their own distinct global infrastructure. Factors like the specific GPU hardware used, software optimizations (like NVIDIA's TensorRT-LLM), server load, and the physical distance between the user and the data center can all lead to variations in performance. Azure often optimizes for maximum throughput, while OpenAI's API may be tuned for the lowest possible latency.

It depends entirely on the use case and budget. While the capability is there, it's expensive. A single prompt with 120,000 input tokens would cost $0.60. It is best reserved for specific, high-value tasks that genuinely require the model to process and reference a massive body of text at once, such as legal contract analysis or querying a full codebase. For most common tasks, using techniques like Retrieval-Augmented Generation (RAG) to provide smaller, more relevant context is far more cost-effective.