CHAPTER 3.6b · FLEXIBLE INFERENCE SCALING

Thinking Budget Allocator

Qwen3 Framework: Set your thinking budget and see how response quality scales with invested token budget depending on task type. Accuracy curves for math, code, and creative tasks.

Thinking Budget Allocator: Qwen3's approach — direct token budget instead of abstract effort level. Define how many tokens the model can "think" before responding.

📖 Learning Context ▼
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Learning Objectives

  • Understand token-based thinking budget
  • Read task-specific scaling curves
  • Find optimal budget for different tasks
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Context: Where are we?

Step 4/5 Reasoning & Test-Time Compute

Complements Effort Parameter with the more direct token budget approach.

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Why it matters

Math needs ~2000 thinking tokens for optimal results, creative tasks only ~500. With token budget control, costs can be reduced by 60-80%.

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Key Takeaways

  • Direct Control: Exact token count instead of abstract level
  • Task-dependent: Math > Code > Creative in token requirements
  • Plateau: Beyond a certain budget, no more improvement
1,000
Tokens
Estimated Cost
$0.02
Math Accuracy
72%
Code Accuracy
68%
Creative Quality
65%
Accuracy vs. Thinking Budget
Math Problems (Steep)
Code Generation (Moderate)
Creative Writing (Flat)
💡 Thinking Budget Framework (Qwen3)

• 100-500 Tokens: Easy tasks, quick responses
• 500-1500 Tokens: Medium problems, good balance
• 1500-3000 Tokens: Complex tasks, deep reasoning
• 3000-5000 Tokens: Very difficult problems, maximum depth
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Task-dependent Curves
Different task types benefit differently: Math needs a lot of budget, Creative Writing only a little.
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Optimal Budget
Find the perfect budget for your use case: More budget = higher quality, but also higher costs.
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Unified Framework
Thinking and Non-Thinking work together. Budget affects the internal reasoning depth.
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ROI Analysis
With our calculator, find the sweet spot between quality and cost efficiency.