OpenAI Expands GPT-5 Choices and Brings Back Old Models

OpenAI faced immediate pushback after launching GPT-5 as fans of older models complained the new default lacked familiar characteristics. In response, the company expanded user control: you can now choose between multiple GPT-5 modes and re-enable legacy models from settings.

What changed in the GPT-5 rollout

OpenAI had intended GPT-5 to be a single, adaptive system that routes prompts between a fast, lightweight model and a heavier reasoning model. That routing (Auto) remains the default, but users can now pick a direct mode when they prefer predictable performance or depth.

• Auto — System decides whether a query needs speed or depth; recommended for most people.
• Fast — Routes straight to the light, quick model for basic answers and low latency.
• Thinking — The reasoning model that chains steps, uses tools and searches, but has usage limits today.
• Thinking mini — A lighter alternative when the full Thinking model hits limits.
• Pro — The most capable reasoning tier, limited to higher-priced plans for now.

Paid subscribers can also re-enable older models like GPT-4o, GPT-4.1, 4o-mini and 3o by toggling "Show additional models" in Settings. That move addresses the loudest complaints: enthusiasts and production teams that relied on previous behaviors can recover them without losing access to GPT-5.

OpenAI also emphasized personality customization — you can tune tone (sassy, nerdy, etc.) without swapping models. The long-term aim appears to be one adaptable engine plus much richer user controls for style and behavior.

The timing matters: GPT-5 arrives as Anthropic, Google and others ship advanced systems, and as legal and IP scrutiny of model training grows. Those dynamics make transparent choices, usage limits and auditability more important than ever for organizations.

What this means for teams and leaders

• Developers should A/B test Fast vs Thinking for latency-sensitive features and complex reasoning tasks.
• Product owners must define KPIs (accuracy, cost, latency) and map them to model choice for each workflow.
• Compliance and legal teams need audit trails and usage policies, especially when switching between reasoning tiers.

A simple example: a support chatbot can use Fast for FAQs to save cost and reduce latency, then route escalations to Thinking for investigative, multi-step cases. That hybrid approach mirrors the Auto routing but gives teams explicit control over costs and guarantees.

Practical constraints remain: usage caps (e.g., 3,000 Thinking messages per week), plan-based access to Pro, and the need to keep older models available for reproducibility. Organizations should treat model choice as part of their release and governance planning, not an afterthought.

Actionable next steps

• Run short pilots that compare model tiers on target metrics, not just subjective impressions.
• Define cost and compliance guardrails that trigger routing decisions or fallbacks when limits are reached.
• Document expected model behaviors and preserve legacy-model access for reproducible outputs.

At QuarkyByte we translate these choices into actionable plans: experiments that surface trade-offs, cost forecasts tied to usage patterns, and governance templates that preserve auditability. Teams that treat model selection strategically will extract more value from GPT-5 while reducing surprises in production.