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Modern AI models can do more than blurt the first answer that comes to mind. The best ones quietly work through the problem first — drafting, checking, revising — before producing a final reply. That hidden draft is called reasoning (or “extended thinking”, “chain of thought”). Pria gives you a single slider, the Reasoning Effort picker, to control how much of that thinking you want.

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What “reasoning” means

When a thinking model receives your question, it can spend extra time generating an internal monologue before it starts writing the reply:
  • “Let me re-read the question.”
  • “There are two ways to interpret this — I’ll consider both.”
  • “That step looks wrong, let me redo it.”
You only see the polished final answer, but the reasoning step is what makes the difference between a snappy guess and a carefully argued response. Pria can also surface the live thinking stream so you can watch the model work — useful for transparency, debugging tricky problems, and learning how the model approaches a task.
Not every model supports reasoning. In the model catalog, the Features icon marks the models that do.

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The 5 effort levels

None

No thinking. The model answers immediately from its first read. Fast and cheap. Good for casual chat, quick lookups, and voice conversations where latency matters.

Low

A brief inner pass — usually a few seconds extra. Catches obvious mistakes without slowing things down much.

Medium

A solid amount of thinking. The model checks its work, considers alternatives, and reorders its plan if needed. A sensible default for most knowledge work.

High

Deep thinking. Several seconds to a minute of internal reasoning. Use for nuanced analysis, complex multi-step problems, and long-form writing where quality matters more than speed.

Max

Maximum effort. The model uses the largest reasoning budget the provider allows. Slow and credit-heavy, but produces the highest-quality output for genuinely hard problems.
Reasoning Effort is set alongside the model, not per message: whoever manages the Twin picks it under Settings → Instance → Conversation → Reasoning Effort (just below the Conversation Model), and an assistant can pin its own effort level. Members of a Twin use the level it’s set to. The dropdown’s first option is None (Disable thinking); the rest are Low, Medium, High, and Max.
The Instance Settings lightbox on the Conversation tab, showing Conversation Model, Max Tokens, and a Reasoning Effort dropdown set to None (Disable thinking), above moderation and history toggles.

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Per-model differences

Every AI provider implements thinking slightly differently. Pria abstracts the differences behind the same 5-level scale, but here’s what’s happening underneath:
Claude’s “extended thinking” mode reserves a token budget for an internal reasoning block. Low/Medium/High/Max map to progressively larger budgets. The thinking tokens are billed but not shown in the final reply (unless you turn on the display toggle).
OpenAI reasoning models accept a reasoning_effort parameter directly. None / Low / Medium / High / Max pass through to the API. Newer GPT-5 generations also surface short “thinking summary” snippets which Pria can render live.
Gemini 3.x models use thinkingLevel (low/medium/high/max), older 2.5-pro models use thinkingBudget (a token count, minimum 128). Pria translates the unified scale into whichever knob the model supports.
Only grok-3-mini accepts an explicit reasoning effort. Grok 4 reasons automatically and ignores the slider — Max and Medium produce the same answer.
Models served via AWS Bedrock (Claude on Bedrock, Nova, Llama) inherit the underlying model’s reasoning support and Pria’s scale maps onto each one’s native parameter.
Mistral and most older non-reasoning models don’t accept the slider at all — the picker is greyed out and the model answers in its normal one-shot mode.
You don’t need to know which knob a model uses — pick the level that matches the depth you want and Pria handles the translation.

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The thinking display toggle

By default the reasoning trace can appear above each answer in a 💡 lightbulb block. Whether it does is a per-Twin choice, controlled by whoever manages the Digital Twin under Settings → Instance → Personalization → Display Details.
The Display Details section of Instance Settings showing Display Tools Details, Display Tool Execution, Display Thinking Details, and Display Thinking Execution toggles, each enabled.

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Reading the four toggles

The screenshot shows four switches, because the same two-way split applies to the model’s tool activity as well as its thinking. They form a simple grid:
Details — persisted, above each answerExecution — live, while the response streams
🔧 ToolsDisplay Tools Details — tool/agent calls (RAG, web search, MCP, …) shown in a wrench block above the answer and saved on History recordsDisplay Tool Execution — live ⚙️ Running… / 🔧 Ran ✓ indicators as the model works; not saved
💡 ThinkingDisplay Thinking Details — the reasoning trace shown in a lightbulb block above the answer and saved on History recordsDisplay Thinking Execution — the reasoning trace shown live as it streams; not saved
Two questions decide which toggle you want:
  • Tools or Thinking — what are you watching? 🔧 Tools is the model acting — calling retrieval, web search, or an MCP connector. 💡 Thinking is the model reasoning — its internal chain-of-thought.
  • Details or Execution — when do you see it? Details is the persisted block that stays above the answer (and is written to saved History). Execution is the live view that appears only while the reply streams and then disappears. Persistence always follows the matching Details toggle — so you can watch the live trace without keeping it, or keep the saved block without the live view.
When thinking display is on, the next reply renders a collapsible 💡 block above the final answer — expand it to read the model’s work, or leave it folded. Because these are per-Twin settings, an administrator can keep thinking visible on a research twin and hidden on a customer-facing one.

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Reading thinking blocks during a stream

While a reasoning model is thinking, you’ll see:
  • A growing “thinking” panel above the reply area, streaming in real time.
  • A spinner / waveform indicating the model is still planning.
  • The final answer appears below once thinking finishes and the model begins composing the reply.
The thinking block is collapsible after the answer arrives — click the header to fold or unfold it. Long thinking sessions are scrollable inside the panel, so they don’t push the answer off-screen.
Thinking content is informative, not authoritative. The final answer is what the model wants to commit to. Treat the thinking block as “showing the work”, not as a second answer to react to.

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Cost & speed tradeoffs

LevelTypical extra latencyCredit overheadWhen it pays off
None00Greetings, quick facts, voice
Low1–5s~10–20%Light QA, drafting
Medium5–15s~30–60%Default knowledge work
High15–60s2–4×Analysis, long planning
MaxUp to several minutes5–10×Hard, high-stakes problems
Numbers are rough — they vary by model, question complexity, and provider. Watch the credit indicator on each reply to see the real cost.

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When to use Max

Reach for Max when the answer’s quality matters more than time or cost:
  • Deep research questions that require synthesising many sources.
  • Long, multi-step plans you’ll act on (project plans, architecture proposals).
  • Nuanced analysis where small mistakes have big downstream cost.
  • Hard puzzles, derivations, proofs, or ambiguous specifications.
  • Final-draft writing on important material.
You’ll usually pair Max with a frontier model (Claude Opus, GPT-5, Gemini 3 Pro, Grok 4) and a Knowledge Mode that gives the model rich context.

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When to use None or Low

Drop to None or Low when speed and economy matter:
  • Voice mode (long thinking pauses break the conversation’s natural rhythm).
  • Quick factual lookups (“what’s the capital of…”).
  • Casual chitchat, small talk, formatting tweaks to existing text.
  • Bulk drafting where you’ll edit afterwards anyway.
  • Repetitive workflows where the answer is well within the model’s first-pass capability.
Setting effort to None on a reasoning-required problem doesn’t break the model — it just produces a shallower, more error-prone answer. If a reply looks wrong, bump the slider and re-ask.