Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Dadaalka Sababaynta iyo Kediska Miisaaniyadda Fekerka

Wuxuu tijaabiyaa sida dadaalka sababaynta iyo miisaaniyada fekerku u shaqeeyaan, sida calaamadaha macquulka ah ee qarsoon loo dallaco, iyo sababta fekerka badan u leeyahay soo celinta soo celinta.

Wadooyinka haga ee la xidhiidhaDedaalka Sababaynta iyo Miisaaniyadda Fekerka
Su'aal 1 ee 8

What do reasoning effort and thinking budget settings control?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. What do reasoning effort and thinking budget settings control?
    • The size of the model's training data
    • How much internal step-by-step reasoning the model does before answering
    • The number of languages the model supports
    • How many users can call the model at once
  2. How are hidden reasoning tokens generally billed?
    • They are free because you cannot see them
    • Sida calaamadihii gelinta
    • As output tokens
    • Sida lacag siman bishii
  3. For Anthropic extended thinking, which rule about budget_tokens is stated in the guide?
    • It must be at least 1,024 and smaller than max_tokens
    • It must equal max_tokens exactly
    • It has no minimum
    • It must be larger than the context window
  4. A model is given a large thinking budget for an easy question. What typically happens?
    • It always uses the full budget
    • Way diiday inay ka jawaabto
    • It may use only part of the budget
    • It switches to a different model
  5. Where do larger reasoning settings help the most?
    • Simple factual lookups
    • Problems that genuinely need several steps, like hard math or debugging
    • Greeting messages
    • Very short translations
  6. What is the recommended way to choose an effort level?
    • Always use the highest setting
    • Always use the lowest setting
    • Test real tasks at several levels and pick the lowest that meets your quality bar
    • Pick a level at random for each request
  7. Why might a reasoning model return a truncated or empty answer?
    • The output limit was too small to cover both reasoning and the answer
    • The prompt was too short
    • Reasoning models cannot answer questions
    • The thinking budget was set to its minimum
  8. When using Anthropic extended thinking with tools across multiple turns, what should you do with thinking blocks?
    • Delete them to save tokens
    • Rewrite them in your own words
    • Pass them back unchanged so the model can continue its reasoning
    • Convert them into a system prompt