GPT-4 (non-reasoning)

This is the most impactful strategy. Instead of sending every request to GPT-4, use a 'router' or 'cascade' system.

• Initial Triage: Send all incoming requests to a much cheaper, faster model (e.g., one costing less than $1.00 per million tokens).
• Quality Check: Analyze the cheap model's output. If it's sufficient for the task, return it to the user.
• Escalation: Only if the initial output is poor or the query is flagged as highly complex should you escalate the request to the expensive GPT-4. This ensures you only pay the premium for tasks that genuinely require it.

The $60/M output token cost is crippling. Uncontrolled verbosity will destroy your budget. You must actively manage the length of the model's responses.

• Use `max_tokens`: Always set the `max_tokens` parameter in your API call to a reasonable ceiling for the task. Never leave it unlimited.
• Prompt Engineering: Instruct the model to be concise. Use phrases like "Be brief," "Answer in one paragraph," or "Use bullet points."
• Post-processing Truncation: As a final backstop, truncate the response in your application code if it exceeds a certain length, though this is less ideal than controlling it at the source.

While cheaper than output, the $30/M input token cost is still the highest in its class. Minimize the data you send in every API call.

• Summarize Chat History: For conversational bots, don't send the entire raw chat history. Create a running summary of the conversation and send that instead.
• Strip Unnecessary Data: Remove boilerplate, HTML tags, irrelevant metadata, and excessive examples from your prompts.
• Contextual Pruning: For RAG systems, be selective about the context you inject. Use embedding similarity scores to include only the most relevant chunks of text, not entire documents.

Many applications receive duplicate or near-duplicate requests. Calling the API for the same query repeatedly is a waste of money and time.

• Identify Cacheable Queries: Determine which types of requests are likely to be repeated. Good candidates include requests for definitions, common questions, or data extraction from static documents.
• Store Results: Use a fast key-value store like Redis or a simple database to store the request (or a hash of it) and its corresponding response.
• Check Cache First: Before making an API call to OpenAI, check your cache. A cache hit saves you the full cost and latency of an API call.

The pricing of $30/$60 per million tokens reflects its original positioning as a top-tier, flagship model. OpenAI has since released newer, more efficient, and more powerful models (like GPT-4 Turbo and GPT-4o) at significantly lower price points. The high price of this legacy version remains, making it uncompetitive from a cost perspective. It effectively encourages users to migrate to the newer offerings.

The Artificial Analysis Intelligence Index is a comprehensive benchmark that tests models on a wide range of complex reasoning, instruction-following, and multi-step tasks. While GPT-4 was state-of-the-art upon its release, the field has advanced rapidly. Newer models are specifically trained and fine-tuned to excel on these types of benchmarks. GPT-4's score of 21, while low on our current scale, reflects its performance against a field of hyper-optimized modern competitors, not necessarily that it is an 'unintelligent' model in a vacuum.

No. The models powering the various tiers of the ChatGPT consumer product are often different from the base models offered via the API. They are typically fine-tuned for chat, may be updated more frequently, and can have different performance characteristics. This analysis is specific to the `gpt-4` base model available through the OpenAI API, which has a distinct and stable performance and cost profile.

Its use cases are now very niche. The primary justification would be for maintaining legacy applications that were specifically built and tuned for this model's unique output style, where the cost and effort of migrating to a new model are prohibitive. It might also be used for low-volume, non-urgent, asynchronous tasks where budget is not a concern and the developer wants to use the OpenAI ecosystem without upgrading their code to a newer model endpoint.

An 8,192-token context window (roughly 6,000 words) is a significant limitation in modern applications. It means the model cannot process long documents, extensive codebases, or maintain long conversational histories in a single prompt. For tasks like summarizing a lengthy legal contract, analyzing a full research paper, or building a chatbot that remembers an entire day's conversation, the 8k context is insufficient and requires complex and often lossy workarounds like chunking and summarizing.

Yes, overwhelmingly so. Within the OpenAI ecosystem, models like GPT-4o and GPT-4 Turbo offer vastly superior intelligence, much larger context windows (128k), and are dramatically cheaper (often 5-10x less expensive). Outside of OpenAI, models from providers like Anthropic (Claude 3 series), Google (Gemini series), and various high-performance open-source models offer better performance on nearly every metric—speed, intelligence, and cost—than this specific version of GPT-4.