OpenAI adds user control over GPT‑5 “thinking” depth for Plus and Pro subscribers

What OpenAI announced

OpenAI has begun rolling out a new toggle that lets users select how “hard” the GPT‑5‑thinking model should work on a given prompt. The feature is being made available to ChatGPT Plus and Pro subscribers, enabling users to adjust the model’s internal effort level per request.

OpenAI is finally rolling out a toggle that allows you to decide how hard the GPT‑5‑thinking model can think. This feature is rolling out to Plus and Pro subscribers.

Background and why this matters

AI model behavior is determined both by architecture and by runtime controls. Developers and end users have long relied on exposed parameters — such as temperature, max tokens and model selection — to shape output style, length and randomness. The new toggle addresses a different axis: the amount of internal computation or reasoning the model undertakes before producing an answer.

• Operational trade-offs: Inference effort generally affects latency and cost. More internal reasoning typically requires more compute and time, while lighter effort yields faster, cheaper responses.
• Task fit: Some queries — simple lookups, formatting, or short factual responses — do not need deep multi‑step reasoning. Others — complex planning, debugging, or multi‑hop inference — benefit from higher internal effort.
• User experience: Giving users control over this trade‑off lets them optimize for responsiveness or for depth as needed, improving both power users’ workflows and high‑volume applications’ economics.

Expert analysis for practitioners

For engineers, product managers and prompt designers, this toggle introduces a new variable in system design and prompt engineering. Consider the following practitioner‑oriented observations and recommended approaches.

• Design for modes. Architect APIs and UI flows around at least two operational modes: a low‑effort “fast” mode for short, high‑throughput interactions and a high‑effort “deep” mode for complex reasoning. Expose mode selection where user choice matters, and set sensible defaults for unattended automation.
• Benchmark with intent. When evaluating the toggle, measure key metrics by intent class: latency, token usage (and inferred cost), factuality/hallucination rate, and task success. Run A/B tests to determine where higher thinking materially improves outcomes.
• Instrument and monitor. Add observability for mode selection in logs and dashboards: per‑request mode, latency, downstream error rates, and user satisfaction. These signals will show whether higher effort justifies extra cost or delay.
• Prompt engineering implications. For chain‑of‑thought or multi‑step reasoning prompts, higher effort may amplify benefits of explicit reasoning prompts; for templated outputs, lower effort often suffices. Avoid assuming higher effort always reduces hallucinations — test empirically.
• Cost management. If billing correlates with compute, implement quotas, rate limits or conditional escalation: start requests in a low‑effort band and only elevate to high effort on failure or user request.

Comparable approaches and industry context

Exposing runtime controls is consistent with an industry trend toward giving users and developers finer‑grained control over model behavior and cost. Historically, APIs have exposed parameters such as temperature, top‑p, and max tokens; the new toggle targets reasoning depth rather than randomness or length.

• Control knobs are not new: temperature and sampling settings have been standard for shaping creativity and variability.
• Model choice is another lever: selecting smaller or larger models balances capability against latency and cost; the new toggle adds an orthogonal choice within a single model family.
• Operationally, enterprises have long tuned models for throughput and latency; adding a reasoning‑depth control aligns product UX with these operational concerns.

Risks, implications and actionable recommendations

While the toggle increases flexibility, it also introduces new operational and safety considerations. Below are the main risks and concrete recommendations to mitigate them.

• Risk — Overreliance on higher effort: Users may assume that increasing the model’s thinking depth always yields more accurate or reliable answers. In reality, deeper internal computation can exacerbate confidently stated errors if the model’s underlying knowledge is wrong.
  • Recommendation: Validate high‑effort outputs with external verification or fallbacks. Use fact‑checking layers or tooling to corroborate critical responses.
• Risk — Cost and latency surprises: Elevated reasoning depth typically raises compute use and response time, which can unexpectedly increase operational costs or degrade user experience.
  • Recommendation: Apply rate limits, implement soft default to lower effort for anonymous/high‑volume traffic, and surface cost/latency expectations in UI for advanced users.
• Risk — Privacy and data handling: Longer or more complex internal processes can increase the amount of transient context or intermediate states handled by the model, affecting data governance.
  • Recommendation: Ensure logging and data retention policies are clear; redact or minimize sensitive inputs and keep audit trails for high‑effort interactions.
• Risk — Safety and misuse: Different thinking depths can change how the model responds to adversarial prompts or attempts to bypass safeguards.
  • Recommendation: Test safety guards across effort levels and maintain consistent content filters. Include automatic escalation rules that route uncertain or policy‑sensitive outputs to manual review regardless of effort level.
• Risk — Cognitive bias amplification: When higher effort produces more elaborate justifications, users may be more persuaded by plausibly reasoned but incorrect answers.
  • Recommendation: Use provenance or confidence indicators alongside high‑effort answers; show citations, sources, or “confidence scores” where possible.

Conclusion

OpenAI’s new toggle for controlling how hard the GPT‑5‑thinking model works gives users and developers a pragmatic lever to balance latency, cost and answer depth. For practitioners, the immediate priorities are empirical benchmarking, robust monitoring, careful cost controls and safety testing across effort levels. The feature is a practical step toward more configurable LLM deployments, but it also raises predictable operational and trust challenges that teams must address systematically.

Source: www.bleepingcomputer.com