OpenAI o3 and o3-mini: The Next Generation of Reasoning Models

ARC-AGI: The Benchmark That Stumped AI

The ARC-AGI benchmark tests abstract reasoning using visual pattern puzzles that humans solve easily but AI has historically failed at. Prior to o3, the best models scored around 32% (o1) and 34% (Claude 3.5).

o3 achieved 87.5% on the semi-private ARC-AGI evaluation - a jump so dramatic it made headlines and prompted the ARC Prize team to call it "a genuine breakthrough."

Benchmark Scores

The SWE-Bench score of 71.7% is particularly significant - it means o3 can resolve nearly 3 in 4 real GitHub issues autonomously.

Compute-Optimal Inference Scaling

Unlike standard models where inference cost is fixed, o3 uses test-time compute scaling: you can tell it how much "thinking budget" to spend. More thinking = higher accuracy = higher cost.

o3-mini offers three compute tiers:

• Low: Fast, cheap, similar to o1-mini
• Medium: Balanced quality/cost (recommended default)
• High: Maximum accuracy, higher latency and cost

from openai import OpenAI

client = OpenAI()

# o3-mini with medium reasoning effort
response = client.chat.completions.create(
    model="o3-mini",
    reasoning_effort="medium",
    messages=[
        {"role": "user", "content": "Solve: if 3x + 7 = 22, what is x^2 + 2x?"}
    ]
)
print(response.choices[0].message.content)

When to Use o3 vs o1

Use o3/o3-mini for:

• Mathematical proofs and competition-level problems
• Complex multi-step coding tasks (algorithm design, debugging)
• Scientific reasoning and STEM research assistance
• Tasks where accuracy matters more than cost

Use o1 for:

• Tasks where o3's performance gains don't justify the cost premium
• Existing pipelines already tuned for o1 behavior
• When you need faster time-to-first-token

API Access

o3 and o3-mini are available through the OpenAI API. Access for o3 (full) is currently prioritized for Tier 4-5 API users. o3-mini has broader availability.

# Full o3  -  highest capability, highest cost
response = client.chat.completions.create(
    model="o3",
    messages=[{"role": "user", "content": "Design a Byzantine fault-tolerant consensus algorithm."}],
    max_completion_tokens=8000,
)

Cost Considerations

o3 is significantly more expensive than GPT-4o. For most production applications, o3-mini at medium reasoning effort hits the best accuracy/cost balance. Run a cost analysis before deploying o3 at scale - the quality gains are real, but so is the bill.

Summary

o3 represents a step-change in AI reasoning capability, particularly for math, science, and complex coding. o3-mini with configurable compute tiers makes this capability accessible at reasonable cost. Track benchmark results and access details at the ARC Prize blog.