GPT-5 vs GPT-4o cost comparison
Updated 1 August 2026
GPT-5 is more expensive than GPT-4o at the rate-card level. The question is whether it is more expensive at the outcome level. On simple tasks, GPT-4o usually wins. On complex tasks, GPT-5's higher success rate can make it cheaper per successful result. The right comparison is not price per token; it is cost per completed task.
Rate card comparison
| Model | Input $/1M | Output $/1M | Cache read $/1M |
|---|---|---|---|
| GPT-4o | $2.50 | $10.00 | $1.25 |
| GPT-4o mini | $0.15 | $0.60 | $0.075 |
| GPT-5.6 Luna | $1.00 | $6.00 | $0.10 |
| GPT-5.6 Terra | $2.50 | $15.00 | $0.25 |
| GPT-5.6 Sol | $5.00 | $30.00 | $0.50 |
At first glance, GPT-5.6 Sol is 2x more expensive than GPT-4o on input and 3x on output. But the rate card does not tell the whole story.
Cost per successful task: the fair metric
Imagine a coding task with 1,500 input tokens and 600 output tokens. We compare GPT-4o and GPT-5.6 Sol, including the retries needed to get working code.
| Model | Cost per call | First-pass success | Cost per working function |
|---|---|---|---|
| GPT-4o | $0.00975 | 65% | $0.0150 |
| GPT-5.6 Terra | $0.0135 | 80% | $0.0169 |
| GPT-5.6 Sol | $0.0255 | 88% | $0.0290 |
On this task, GPT-4o is cheaper per working function than Sol. Terra sits in the middle. But this changes as task complexity increases.
When GPT-5 becomes cheaper than GPT-4o
GPT-5 pulls ahead when complexity raises the retry gap. Consider a multi-step reasoning task where GPT-4o succeeds 40% of the time and GPT-5.6 Sol succeeds 80%.
- GPT-4o: $0.00975 per call / 0.40 = $0.0244 per success
- GPT-5.6 Sol: $0.0255 per call / 0.80 = $0.0319 per success
Sol is still more expensive here, but the gap narrows. If the task also requires longer reasoning tokens on GPT-4o retries, or if a failed GPT-4o result requires human intervention, GPT-5 can become the cheaper total solution.
Tasks where GPT-5 typically wins on total cost:
- Multi-hop research and synthesis
- Complex code generation with strict correctness requirements
- Agentic workflows with tool use and planning
- Long-document analysis where context understanding reduces follow-up questions
When GPT-4o is the clear choice
GPT-4o remains the better economic choice for high-volume, low-complexity work. Good candidates include:
- Classification and intent routing
- Structured extraction from short documents
- Simple summarization
- High-volume batch processing
- Any task where a cheaper model can be verified automatically
For these tasks, GPT-5's quality advantage is wasted. The cheaper model does the job and the savings scale directly with volume.
The routing answer: use both
Most production systems should not choose one model. They should route requests based on complexity. A lightweight classifier sends simple work to GPT-4o or GPT-4o mini and escalates hard work to GPT-5. This captures most of the savings of GPT-4o while keeping GPT-5 for the tasks where it earns its price.
Other factors beyond token cost
- Latency. GPT-5 is slower. For real-time applications, the user-experience cost may outweigh token savings.
- Context window. Both models support long context, but GPT-5 uses it more efficiently on complex documents.
- Reasoning tokens. GPT-5's reasoning is billed but visible in cost tracking. GPT-4o may hide reasoning in longer outputs.
- Provider risk. Relying on a single model increases exposure to price changes and outages.
Related
- GPT-5 pricing and budget impact — worked budget scenarios.
- How much does GPT-5 cost? — full pricing guide.
- GPT-5.6 pricing tier guide — Luna, Terra, Sol routing.
- Model routing — building a cost-effective cascade.
Want this applied to your own LLM spend? FinOps LLM runs a free audit of your AI costs and shows where the savings are. Book free audit →
FAQ
Is GPT-5 more expensive than GPT-4o?
Yes, GPT-5 API rates are roughly 2–3x higher than GPT-4o. However, GPT-5 can be cheaper per successful task on complex workloads because it often succeeds in one pass where GPT-4o needs retries.
When should I use GPT-5 instead of GPT-4o?
Use GPT-5 for multi-step reasoning, complex code generation, long-document analysis, and agentic workflows. Use GPT-4o for classification, extraction, summarization, and high-volume simple tasks.
How do I compare GPT-5 and GPT-4o costs fairly?
Divide total API cost by the number of successful outcomes. A cheaper model that fails more often can cost more per successful task than an expensive model that succeeds on the first try.