Lesson 83 of 1570
GPT-5.5 vs. GPT-5.4 mini — when to pay for the flagship
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.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1Same family, different jobs
- 2GPT-5.5
- 3GPT-5.4 mini
- 4reasoning effort
Concept cluster
Terms to connect while reading
Section 1
Same family, different jobs
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.
Compare the options
| 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 |
Reach for GPT-5.5 when
- The task genuinely requires multi-step planning
- You are generating production code with subtle invariants
- You need research-grade answers with citations
- Mini keeps missing the same important edge case
Use the Responses API and raise reasoning effort only when the task earns it.
from openai import OpenAI
client = OpenAI()
response = client.responses.create(
model="gpt-5.5",
reasoning={"effort": "high"},
input=task,
)
print(response.output_text)Key terms in this lesson
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