🧠 Reasoning Models • reasoning_effort • Hidden CoT Tokens • Cost & Speed Parity
DeepSeek-R1 vs OpenAI o1 / o3-mini Calibrator
Reasoning models spend invisible tokens before writing their first response word. Calibrate hyperparameters across DeepSeek-R1 and OpenAI o1/o3-mini: temperature constraints, thinking token overhead, and API cost budgets.
Task Complexity & Inputs
Input Prompt Length 1,200 tokens
Expected Visible Output Length 800 tokens
Side-by-Side Architectural Execution
🧠 DeepSeek-R1 671B MoE
Mandatory Temperature: 0.6
Thinking Tokens: ~6,500
Estimated Time: 14.2s
Estimated Turn Cost: $0.016
Optimal for mathematical proofs & open-weight local serving via Ollama/vLLM.
🚀 OpenAI o1 Frontier Tier
Mandatory Temperature: 1.0 (Fixed)
Thinking Tokens: ~6,000
Estimated Time: 18.5s
Estimated Turn Cost: $0.428
Strict structured outputs & complex coding with reasoning_effort parameter.
Cost Multiple: DeepSeek-R1 is ~26.7x cheaper per turn