Academy
4. Reasoning Effort: How Depth Scales with Complexity
Learn that for models supporting extended reasoning, OUPI automatically adjusts thinking depth based on detected complexity — no manual setting needed.
When you send a message, OUPI doesn't just pick the right model — it also decides how deeply that model should think. Some models support "extended reasoning," where they work through intermediate steps before answering (useful for calculations, multi-criteria decisions, or complex logic). OUPI automatically adjusts this reasoning effort based on the complexity it detects in your message. A simple rewrite gets no extended reasoning at all: the model answers quickly and cheaply. A multi-step calculation or a decision involving several constraints triggers deeper thinking. You never need to set a slider or toggle; the trade-off between speed, cost, and depth is handled for you on every single message.
This automatic depth scaling is part of the same routing engine that selects the model. When OUPI reads your message, it classifies both the type of task (writing, analysis, code, extraction) and its complexity. That complexity signal does double duty: it helps choose between a frontier, balanced, or fast model, and — if the chosen model supports extended reasoning — it sets how much thinking the model should do before responding. The two decisions work together so that a hard problem gets both a capable model and deep reasoning, while a routine request gets a light model with no unnecessary thinking overhead.
Why does this matter? Reasoning models are powerful but slower and more expensive. Extended thinking consumes more tokens and more credits. If every message triggered maximum reasoning depth, you'd burn through your budget on tasks that don't need it. By scaling effort to complexity, OUPI gives you the depth you need exactly when you need it — and saves speed and credits everywhere else. Frontier models can cost ten to thirty times more per token than fast ones, so pairing the right model with the right reasoning depth keeps costs proportional to actual difficulty.
You can always check what happened: the badge under each answer shows which model was used. If you feel a response lacked depth on a genuinely hard problem, pick a reasoning-specialist model manually (like a dedicated reasoning model) and regenerate — then compare the two answers side by side.
Don't confuse a short answer with shallow thinking. A concise, correct reply to a complex question may still have involved deep reasoning behind the scenes. Judge by quality, not length. If the answer is wrong or superficial, escalate by choosing a frontier or specialist reasoning model manually.
Open a chat and send two messages without selecting a model: first, ask for a one-line synonym ("Rewrite 'happy' in a formal tone"), then ask a multi-step question ("Compare three options for X using cost, speed, and quality criteria"). Check the badge on each answer — notice whether the model changed and whether the harder question took longer, reflecting deeper reasoning.
For models that support extended reasoning, OUPI automatically scales thinking depth to match the complexity of each message. Simple tasks get fast, shallow processing; hard, multi-step problems trigger deeper reasoning. This happens per message, with no manual setting required. The result: you get the right depth of thought at the right cost, every time. Check the model badge to see what was chosen, and override manually only when you have a specific reason.