OpenAI has posted an update on GPT-5.2 + Codex. Before you rush to integrate any new model, use this concise checklist to evaluate capabilities, costs, and risks for your stack.
What to look for in the announcement
- Capability deltas: Look for measurable gains in coding accuracy, reasoning, and multimodal support (text, image, audio).
- Latency and throughput: Median/95th percentile response times and tokens-per-minute limits matter for UX and SLAs.
- Cost per 1K tokens: Compare input/output pricing and expected prompt/response sizes.
- Context window and memory: Larger windows reduce chunking complexity but can increase cost.
- Structured outputs and function calling: Native JSON or schema-based outputs ease integration and reduce parsing errors.
- Tool use and retrieval: How well it calls functions, searches docs, or uses your RAG pipeline.
- Safety and guardrails: Updated policies, refusal behavior, and red-teaming notes.
- Versioning and deprecation: Model aliases, stability windows, and migration timelines.
Start with the source announcement to confirm details and limits: OpenAI: Introducing GPT-5.2 + Codex.
Upgrade checklist (fast but safe)
- Define success metrics: Target acceptance criteria for quality (pass@k, BLEU/ROUGE, hallucination rate), latency, and cost per task.
- Build a realistic eval set: Use production-like prompts, edge cases, and safety-sensitive examples.
- Run side-by-side evals: Compare your current model vs. the new one with identical prompts and seeds.
- Track regression risk: Flag drops on critical tasks even if averages improve.
- Tune for determinism: Stabilize outputs with temperature/top_p and JSON schema where possible.
- Exercise tool use: Validate function calling, RAG, and sandbox execution paths under load.
- Cost guardrails: Simulate monthly spend with projected traffic; add max-token, retry, and truncation policies.
- Rate limits and backoffs: Confirm TPM/RPM and implement exponential backoff with jitter.
- Safety checks: Re-run policy tests (prompt injection, data exfiltration, PII handling, jailbreak attempts).
- Stage rollout: Canary by user cohort or endpoint; enable instant rollback and feature flags.
Migration tips for code-heavy apps
- Structured outputs first: Prefer JSON schema or tool/function outputs over free-form text parsing.
- Constrain the task: Include file trees, test specs, and explicit constraints in the prompt.
- Short, iterative loops: Break big tasks into verifiable steps with unit tests after each step.
- Cache and reuse: Use response caching or prompt templates for common scaffolds to cut cost and latency.
- Eval with real repos: Score pass@k on your codebase snippets, not just academic benchmarks.
- Guard the filesystem: If using code execution, sandbox aggressively and limit network/file access.
Observability essentials
- Token accounting: Track tokens, cost, and truncation events per route.
- Quality telemetry: Log pass/fail, hallucination tags, and user feedback signals.
- Error profiles: Distinguish model errors from tool/infra errors to avoid misattribution.
- A/B switches: Keep model aliasing and feature flags to compare models live.
When not to upgrade (yet)
- Costs climb without clear quality gains on your tasks.
- Latency breaks interactive UX targets.
- Compliance or data residency is unclear.
- Deprecation risk for existing stable endpoints is low.
Resources
Takeaway
Treat every new model release as an engineering upgrade. Ship only after it beats your baseline on your data, within your cost and latency budgets.
Get weekly, no-fluff playbooks like this. Subscribe to The AI Nuggets newsletter.
