Lesson 388 of 2116
OpenAI Model Picker: GPT-5.5, GPT-5.4, Mini, Nano, and Codex
A practical picker for current OpenAI models: when to pay for the frontier model, when to use a smaller model, and when Codex-specific models make sense.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1Start With the Job, Not the Leaderboard
- 2GPT-5.5
- 3GPT-5.4
- 4GPT-5.4 mini
Concept cluster
Terms to connect while reading
Section 1
Start With the Job, Not the Leaderboard
The OpenAI model lineup changes quickly, but the decision pattern is stable: pick the smallest model that can reliably do the job, then upgrade only when evals show it is worth the latency and cost.
Compare the options
| Need | Start here | Why |
|---|---|---|
| Complex coding, planning, professional reasoning | gpt-5.5 | Flagship quality and strong default for hard work |
| Cost-sensitive coding or subagents | gpt-5.4-mini | Strong small model with lower latency and cost |
| Classification, extraction, ranking, simple routing | gpt-5.4-nano | Cheapest GPT-5.4-class option for high volume |
| Agentic coding inside Codex-like harnesses | gpt-5.3-codex | Optimized for coding-agent loops |
| Very hard slow analysis | pro variant or background mode | More compute, but design for waiting |
- 1Build a tiny eval set from real user tasks.
- 2Run the frontier model to establish a quality ceiling.
- 3Run mini and nano to find the cost floor.
- 4Measure latency, refusals, schema validity, and human acceptance.
- 5Lock the chosen model ID in config, not scattered across code.
Centralize model choices so migrations are one config change instead of a repo-wide hunt.
const MODEL_BY_TASK = {
hardCoding: "gpt-5.5",
routineSubagent: "gpt-5.4-mini",
extraction: "gpt-5.4-nano",
codexHarness: "gpt-5.3-codex",
} as const;Key terms in this lesson
The big idea: model selection is an economics problem wrapped in a quality problem. Measure both.
End-of-lesson quiz
Check what stuck
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Tutor
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