Scenario 2: Code Generation (200 requests/day)

Average: 3,000 input tokens, 1,200 output tokens per request. 30 days/month.

Scenario 3: RAG Pipeline (500 queries/day)

Average: 5,000 input tokens, 800 output tokens per query. 30 days/month.

Scenario 4: Document Summarization (100 documents/day)

Average: 10,000 input tokens, 500 output tokens per document. 30 days/month.

Why GPT-5 Is Cheaper Despite Being Better

OpenAI's pricing strategy with GPT-5 is clear: drive adoption by making the better model cheaper. GPT-5's input price ($1.25) is half of GPT-4o's ($2.50), while output pricing stays the same ($10.00).

This makes sense from OpenAI's perspective:

• GPT-5 is more efficient. Better architecture means lower inference costs per token.
• Adoption drives revenue. Cheaper input encourages developers to send more context, which increases total spend.
• GPT-4o is being sunset. Lower pricing on GPT-5 incentivizes migration away from the older model.

The result: you get a better model at a lower price. The only reason to stay on GPT-4o is if you have legacy prompts specifically tuned for its behavior.

The Context Window Advantage

GPT-5's 272K context window is 2.1x larger than GPT-4o's 128K. This matters for:

• RAG pipelines. Feed more retrieved documents into the context without chunking.
• Code generation. Include entire codebases for better context-aware completions.
• Document analysis. Process longer documents in a single request.
• Multi-turn conversations. Maintain more conversation history without truncation.

With GPT-4o, you'd need to split large contexts across multiple requests — multiplying costs. GPT-5's larger window eliminates this overhead.

When to Use GPT-4o Instead

There are a few edge cases where GPT-4o might still make sense:

• Legacy prompt compatibility. If you have prompts specifically engineered for GPT-4o's behavior and don't want to re-test.
• Budget constraints on output. Both models charge $10/1M output, so if output is your bottleneck, there's no cost difference.
• Specific fine-tuning. If you've fine-tuned GPT-4o for your use case, switching means retraining.

For everyone else: switch to GPT-5 today.

GPT-4o mini: Still the Budget King

If cost is your primary concern, GPT-4o mini ($0.15/$0.60) remains the cheapest OpenAI model for simple tasks. Here's how it compares:

Recommended strategy: Use GPT-5 for complex tasks, GPT-4o mini for simple ones. Skip GPT-4o entirely — it's in an awkward middle ground where it's more expensive than GPT-5 but not as capable.

How to Migrate from GPT-4o to GPT-5

Migrating is straightforward since both use the same OpenAI API:

1. Change the model parameter. Replace "model": "gpt-4o" with "model": "gpt-5" in your API calls.
2. Test your prompts. GPT-5 may respond slightly differently to the same prompts. Run your test suite.
3. Update max_tokens. GPT-5 can generate more tokens with its larger context. Adjust limits if needed.
4. Monitor costs. Input costs will drop 50%. Output costs stay the same. Track the savings.
5. Update documentation. If you reference GPT-4o in docs or marketing, update to GPT-5.

Most developers report that GPT-5 works as a drop-in replacement with no prompt changes needed. The migration typically takes less than an hour.