GPT-4o mini (non-reasoning)

The most significant cost factor is the four-times multiplier on output tokens. Your primary goal should be to minimize generated text wherever possible.

• Prompt for Brevity: Explicitly instruct the model to be concise. Use phrases like "Answer in one sentence," "Use bullet points," or "Provide a 'yes' or 'no' answer."
• Use Function Calling: For structured data extraction, use function calling instead of asking the model to generate a JSON object in plain text. This often results in a much smaller token footprint for the output.
• Implement Output Limits: Use the max_tokens parameter to set a hard ceiling on the length of the response, preventing the model from generating excessively long and expensive text.

The 128k context window is a powerful feature, but filling it on every API call is a recipe for high costs and slow response times. Use it strategically.

• Don't Send Redundant History: In chat applications, summarize or trim the conversation history instead of sending the entire transcript each time.
• Cache RAG Context: For RAG applications, if multiple users are asking questions about the same document, process and cache the document's context once rather than re-inserting it for every query.
• Context-Aware Task Routing: Use a cheaper, smaller model to determine if a task actually requires a large context. If not, route it to an endpoint where you send less information.

GPT-4o mini's performance profile (low latency, low throughput) makes it suitable for specific interaction patterns. Align your application design with its strengths.

• Stream Responses: For any generative task, stream the output tokens. This leverages the low time-to-first-token and makes the application feel responsive, even if the full generation is slow.
• Avoid Long-Form Generation: If your application needs to write long articles or reports quickly, GPT-4o mini is a poor choice. Its slow token-per-second rate will create a bottleneck. Consider a different, faster model for these tasks.
• Use for Interactive Agents: The model is perfect for conversational agents where a user types a query and waits for a response. The quick first token makes the agent feel 'alive'.

GPT-4o mini is a distilled version of GPT-4o. It is significantly cheaper, particularly for input tokens, and designed to be faster for certain tasks. However, this comes at the cost of intelligence; it scores much lower on reasoning and complex instruction-following benchmarks. Both models share the same 128k context window and multimodal (vision) capabilities.

Multimodality means the model can process more than one type of data in its input. For GPT-4o mini, this specifically refers to its ability to analyze and understand the content of images provided alongside text prompts. You can ask it questions about a picture, have it describe what's happening, or extract text from an image.

No, it is generally not recommended for these tasks. Its intelligence score of 21 is well below average, indicating that it struggles with multi-step reasoning, logical puzzles, and generating complex, correct code. For such tasks, you should use a higher-tier model like GPT-4 Turbo, Claude 3 Opus, or the full GPT-4o.

Choose GPT-4o mini over open-source alternatives when you need its specific commercial features. Key differentiators include its native vision capabilities, the convenience and reliability of the OpenAI or Azure API, and when you are already integrated into that ecosystem. If your task is purely text-based and you have the infrastructure to host your own models, a fine-tuned open-source model might be more cost-effective and performant.

The knowledge cutoff means the model's internal training data does not include information about events, discoveries, or data from after September 2023. It will not be aware of news or developments that have occurred since that time. This is why it's often used in Retrieval-Augmented Generation (RAG) systems, which provide it with up-to-date information in the prompt context.

The large context window allows the model to consider vast amounts of information (over 200 pages of text) in a single prompt. This is a major advantage for analyzing large documents or maintaining long conversations. However, performance is directly affected: the more tokens you include in the context, the longer the model will take to process the input and begin its response, and the higher the cost will be. Effective use requires balancing the need for context with the impact on latency and cost.