AI / Core OpenAI Codex Application Fundamentals Interview Questions
What are the current recommended models for OpenAI Codex and the API, and when do you choose each?
Model selection in the OpenAI / Codex ecosystem as of mid-2026 centres on the GPT-5.x family. The right choice depends on task complexity, latency requirements, and cost.
| Model | Best for | Context window | Key capability |
|---|---|---|---|
| gpt-5.5 | API-based code generation and general agentic tasks | Large | Strongest for complex coding, reasoning, and professional workflows |
| gpt-5.4 | Codex app/CLI default - most tasks | 1M (experimental) | Native computer use, complex tool use, long-horizon tasks |
| gpt-5.4-mini | Faster, lower-cost option for lighter tasks or subagents | Standard | Speed and cost efficiency |
| gpt-5.3-codex | Maximum agentic coding capability, complex real-world SE | Standard | 25% faster than predecessor; strongest coding + reasoning fusion |
| gpt-5.3-codex-spark | Near-instant coding iteration (research preview, Pro only) | 128k tokens | 1000+ tokens/second; text-only at launch; powered by Cerebras |
| codex-mini-latest | CLI optimised, low-latency code Q&A | Standard | Priced $1.50/1M input, $6/1M output via Responses API |
Key guidance:
- For API-based code generation: start with
gpt-5.5 - For Codex app/CLI most tasks:
gpt-5.4is the default - For maximum agentic coding power:
gpt-5.3-codex - For speed and lower cost:
gpt-5.4-miniorcodex-mini-latest
Note: gpt-5.2-codex and gpt-5.3-codex (when accessed via ChatGPT sign-in) are deprecated in the Codex surfaces - update references to current models.
Which model is currently recommended for most API-based code generation use cases?
What makes gpt-5.3-codex-spark distinctive among Codex models?
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