GPT-4o (Mar) (Omni)

A cascade, or router, is the most effective cost-control strategy. It involves using cheaper, faster models for simple queries and only 'escalating' to GPT-4o when high-level intelligence or multimodality is required.

• Triage Layer: Use a small, inexpensive model (like Haiku, Llama 3 8B, or Gemma) to classify the user's intent. If the task is simple (e.g., greeting, simple Q&A, data formatting), the cheap model handles it.
• Escalation Logic: If the triage layer detects a complex, nuanced, or multimodal query, it routes the request to GPT-4o.
• Benefit: This reserves the expensive capabilities of GPT-4o for tasks that truly need them, drastically reducing the cost of the vast majority of routine interactions.

The single biggest driver of GPT-4o's cost is its $15.00/1M output token price. Controlling the length of its responses is critical.

• Use max_tokens: Always set a reasonable max_tokens parameter in your API calls to prevent the model from generating excessively long—and expensive—responses.
• Prompt Engineering for Brevity: Instruct the model within the prompt to be concise. Phrases like "Answer in a single sentence," "Provide three bullet points," or "Be brief" can effectively guide the model to produce shorter output.
• Structure Output: Use function calling or JSON mode to force the model to populate a predefined, concise structure rather than generating freeform text.

Strategically choose use cases that play to the model's pricing structure, which is 3x cheaper for input than for output.

• Good Use Cases: Summarization, classification, data extraction, and question-answering over large documents. These tasks involve processing a large amount of input text to produce a small, targeted output.
• Costly Use Cases: Content generation, creative writing, and chatty, conversational agents that produce long paragraphs of text. These output-heavy tasks will maximize your costs.
• Application Design: When designing your application, consider if you can frame the problem in a way that requires more reading and less writing from the AI.

Many applications receive redundant requests. Calling the API for the same input repeatedly is an unnecessary expense.

• Identify Repeatable Queries: Determine which API calls in your system are likely to be identical. This is common in Q&A over static documents or for common user intents.
• Cache Responses: Store the results of an API call using a key generated from the input (e.g., the prompt and model parameters). Before making a new API call, check your cache for an existing response.
• Set TTLs: Use a Time-to-Live (TTL) on cached entries to ensure the data doesn't become stale, especially if the underlying information or model changes.

This naming convention provides context for the benchmark data.

• GPT-4o: The base model name. The 'o' stands for 'omni,' highlighting its native multimodal capabilities.
• (Mar): Refers to the 'March 2025' time period during which this specific version of the model was benchmarked.
• API Endpoint: This data corresponds to performance on the chatgpt-4o-latest API endpoint, which points to the most current version of the model.

GPT-4o is a successor to GPT-4 Turbo, designed with different priorities. The key differences are:

• Speed: GPT-4o is significantly faster and has lower latency, making it far better for real-time interaction.
• Cost: GPT-4o was launched with a price point that is 50% cheaper than GPT-4 Turbo's launch price, making high-end intelligence more accessible.
• Architecture: GPT-4o is a single, natively 'omni' model built from the ground up to handle text, vision, and audio. GPT-4 Turbo was primarily a text model with multimodal features added on, which could result in higher latencies for those tasks.
• Intelligence: While both are highly intelligent, GPT-4 Turbo may still edge out GPT-4o on some complex, pure-reasoning benchmarks. GPT-4o optimizes for a balance of speed and intelligence.

Not necessarily. While it is a very high-performing model with an intelligence score of 36 (well above the average of 30), it is not the absolute leader on all reasoning benchmarks. Some specialized models or even previous versions of GPT-4 Turbo might outperform it on specific, complex reasoning tasks. GPT-4o's primary innovation is its combination of strong intelligence with market-leading speed, not just its raw intelligence score alone.

GPT-4o excels in applications where responsiveness and multimodal understanding are critical. Its ideal use cases include:

• Real-time Conversational AI: Powering sophisticated, low-lag chatbots and voice assistants.
• Visual Analysis Tools: Analyzing images or video frames to describe scenes, extract text, or answer questions about visual content.
• Live Support and Sales Agents: Assisting human agents by providing instant information and context during live calls or chats.
• Interactive Coding Assistants: Providing fast code completions and explanations within an IDE.

Due to its high price, GPT-4o is a poor choice for tasks that do not require its unique combination of speed and intelligence. Avoid using it for:

• Bulk Content Generation on a Budget: The high output token cost makes generating large volumes of text (e.g., thousands of articles) prohibitively expensive.
• Simple Backend Tasks: Using GPT-4o for simple classification, data formatting, or sentiment analysis is financial overkill. A much cheaper model can perform these tasks effectively.
• Batch Processing: For offline tasks where a delay of a few seconds or minutes is acceptable, using a slower but cheaper model is more cost-effective.

The blended price is an estimated cost per million tokens that assumes a specific ratio of input to output. The standard calculation assumes a 3:1 input-to-output ratio, which is common in tasks like retrieval-augmented generation (RAG).

The formula is: ( (3 * Input Price) + (1 * Output Price) ) / 4
For GPT-4o: ( (3 * $5.00) + (1 * $15.00) ) / 4 = ($15.00 + $15.00) / 4 = $7.50

It's useful for a quick comparison between models with different pricing structures, but you should always calculate your expected cost based on your own application's typical input/output ratio for an accurate estimate.