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Ultrathink++ ​

Ultrathink++ for OpenAI Codex / GPT Models ​

In the OpenAI ecosystem, the analogue of Claude's ultrathink is using inference-time compute scaling: instructing the model (via prompt or API settings) to dedicate more processing (deeper reasoning, more chain-of-thought steps) before producing output.

Ultrathink & Planning in OpenAI Workflows ​

For example, when using GPT-5-Codex, the model is built to "adjust its reasoning time to task complexity." (cookbook.openai.com) You can pair that with prompt strategies like "plan your approach, then execute step-by-step" to simulate a "Plan Mode" overlay. This often helps to bridge the gap before jumping to more expensive configurations.

Revving the Engine (Iterative Critique) ​

"Rev the engine" translates to running multiple passes of critique on generated plans or code.

  • Ask: "Evaluate your plan for missing edge cases, inefficiencies, or ordering issues."
  • Use subsequent rounds of the model or other tools to refine before final execution. This is conceptually similar to giving more inference compute or letting the model "think longer" on hard tasks.

Layered Stack Strategy ​

Here's a distilled stack for OpenAI contexts:

  1. Inference Scaling — request deeper reasoning or permit "slow mode"
  2. Planning Prompts — enforce explicit plan & reflection stages
  3. Iterative Refinement / Critique — run multiple rounds of review & improvement
  4. Tool / Agent Delegation — split tasks among sub-agents or separate prompt threads
  5. Fallback to Higher Model — only if the lower-cost model fails, use GPT-5-Codex or equivalent

Why do this before switching models? ​

Because inference-time scaling often yields diminishing returns and you want to optimize marginal gains before paying for a more expensive model. OpenAI research shows that adding inference-time compute in reasoning models (e.g. o1-preview, o1-mini) improves robustness and performance against adversarial inputs. (OpenAI) Thus, rationally stacking reasoning techniques before immediately upgrading the model is both cost-efficient and technically sound.