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GPT-5.5 is the hard-problem default; GPT-5.4 mini is the cost-sensitive workhorse. Learn when quality is worth the extra latency and tokens.
OpenAI's current GPT lineup is better thought of as a routing ladder. GPT-5.4 mini handles high-volume product work at lower cost; GPT-5.5 is the flagship for complex reasoning, coding, and professional workflows. Both can use the Responses API and reasoning effort controls, so the real decision is how much quality, latency, and cost the task deserves.
| Dimension | GPT-5.4 mini | GPT-5.5 |
|---|---|---|
| Role | High-volume workhorse | Flagship hard-problem solver |
| Latency | Faster | Fast, but heavier per call |
| Reasoning effort | Use none/low/medium first | Use medium/high/xhigh for hard tasks |
| Cost | $0.75 in / $4.50 out per M tokens | $5 in / $30 out per M tokens |
| Best at | RAG, agents, summarization, routine tool calls | complex code, research, multi-step planning |
from openai import OpenAI client = OpenAI() response = client.responses.create( model="gpt-5.5", reasoning={"effort": "high"}, input=task, ) print(response.output_text)Use the Responses API and raise reasoning effort only when the task earns it.8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-modelx-gpt5-turbo-vs-pro-builders
What is the main idea of "GPT-5.5 vs. GPT-5.4 mini — when to pay for the flagship"?
Which concept is most central to "GPT-5.5 vs. GPT-5.4 mini — when to pay for the flagship"?
Which use of AI fits this topic best?
What should a careful learner remember about "Route, don't default"?
You want to use AI after this lesson. What is the safest next step?
How should AI output about GPT-5.5 be treated?
Name one way to verify an AI answer about GPT-5.5.
Which action would help you apply "GPT-5.5 vs. GPT-5.4 mini — when to pay for the flagship" responsibly?