Top-Tier Reasoning

o1-preview

o1-preview is OpenAI's advanced reasoning model designed to spend more time thinking through complex problems before responding, excelling at sophisticated tasks in science, coding, and mathematics at a level comparable to PhD students. With its enhanced chain-of-thought reasoning and self-reflection capabilities, it delivers more accurate solutions for deep analytical work without requiring special prompt engineering techniques.

128k Context

Top-Tier Intelligence

Oct '23 Knowledge

Available for Chat, Vision, and File Uploads.

Performance Benchmarks

MMLU

90.8%

HumanEval

92.4%

MATH

85.5%

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Why use o1-preview?

Chain-of-Thought Reasoning

Advanced step-by-step logical deduction for complex problems in science, coding, and math

Enhanced Coding and Math Performance

Superior results on reasoning-intensive tasks like code generation and mathematical problem-solving

Self-Fact-Checking and Reflection

Built-in verification of information accuracy through internal multi-step reasoning and mistake recognition

Capability Examples

Complex Math Reasoning

Solve this IMO problem: Let ABC be a triangle with circumcircle Ω. Let P be the second intersection of lines AB and AC with the lines through C and B parallel to AC and AB, respectively. Prove that the circumcircle of triangle PBC intersects Ω again at the midpoint of arc BC not containing A.

o1-preview generates a detailed chain-of-thought: First, visualize the construction—P is the intersection of parallels forming a parallelogram-like figure. Then, use symmetry and angle chasing: angles at P match due to parallel lines, implying cyclic quadrilaterals. Finally, apply Miquel point theorem to show the intersection is the arc midpoint, confirmed by equal arcs via inscribed angles.

Advanced Coding Challenge

Write a Python function to solve the 'Super Heroes' dynamic programming problem: Given n heroes with power p_i and team limit k, maximize total power without exceeding k, handling tricky edge cases like uneven distribution.

def max_hero_power(p, k): # DP table dp[i][j] = max power using first i heroes up to weight j dp = [(k+1) for _ in range(len(p)+1)] for i in range(1, len(p)+1): for j in range(k+1): dp[i][j] = dp[i-1][j] if j >= p[i-1]: dp[i][j] = max(dp[i][j], dp[i-1][j - p[i-1]] + p[i-1]) return max(dp[-1]) # Reasoning: Standard 0/1 knapsack adapted for heroes' power/weight, optimized to O(nk). Handles edges by initialization.

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