o3

o3

Quick answer: o3 is OpenAI's flagship reasoning model, released April 16, 2025. It uses chain-of-thought reasoning with configurable effort levels (low/medium/high) to trade latency for accuracy. It scores 88% on ARC-AGI, 75.8% on LiveCodeBench (high), and 85.9% on Arena Hard v2. Priced at $10 input / $40 output per 1M tokens with a 200K-token context window.

At a Glance

Where o3 leads

• 88% ARC-AGI — strong abstract visual reasoning
• 85.9% Arena Hard v2 — top-tier instruction following and general capability
• 75.8% LiveCodeBench (high effort) — excellent competitive programming performance
• 20.32% Humanity's Last Exam — frontier-class expert knowledge
• Configurable reasoning effort: spend more compute for harder tasks
• Full multimodal vision support

Where it lags

• High cost at $10/$40 per 1M tokens — 9× o4-mini's price
• 200K context window (vs 1M+ for Gemini 2.5 Pro)
• Closed API model with no open weights
• High-effort setting significantly increases latency and token cost

Best for: Frontier-difficulty reasoning, scientific analysis, competitive programming, and tasks where accuracy matters more than cost or speed.

What o3 Is

o3 is OpenAI's main reasoning model, part of the "o-series" that applies chain-of-thought reasoning before producing a final answer. Unlike the GPT series, o-series models trade raw speed for accuracy: they internally generate reasoning tokens before responding, with the depth of reasoning controlled by the effort level setting (low, medium, or high).

The model represents a significant shift from GPT-4o: rather than optimising for fast, fluent generation, o3 prioritises correctness on hard multi-step problems. This makes it substantially stronger than GPT-4o on tasks involving mathematics, science, and complex coding, while being slower and more expensive per response.

o3 was released alongside o4-mini — a compact, cost-efficient reasoning variant. The two models share the same reasoning paradigm but differ significantly in capability ceiling and price. o3 is the appropriate choice when task difficulty is high and the cost premium is justified by the outcome quality.

Specifications

Pricing

Reasoning tokens (internal chain-of-thought) are billed as output tokens. Cached input receives a 75% discount. Pricing per OpenAI pricing page — verify current rates before production use.

Context Window

o3 has a 200,000-token context window — roughly 150 pages of text in a single request. This supports large codebases, long documents, and multi-turn conversations but is substantially smaller than long-context models like Gemini 2.5 Pro (1M tokens).

Public Benchmark Scores

Scores above are from Benchgen evaluations or attributed third-party sources. Figures depend on harness, effort setting, and tools — see the source for methodology.

o3 vs Alternatives

o3's strongest case over o4-mini: higher ARC-AGI score and a larger reasoning ceiling on frontier-difficulty tasks. o4-mini closes the gap on LiveCodeBench (74.2% vs 75.8%) at 9× lower cost, making it the default for most coding tasks.

How o3 Performs on Real Agent Tasks

o3's SHADE-Arena score of 1.6 overall success (a safety evaluation for sabotage detection) reflects the benchmark's design — low scores indicate the model doesn't engage in sabotage behaviour, which is the expected result for a well-aligned model. Its Arena Hard v2 score of 85.9% and Humanity's Last Exam score of 20.32% position o3 near the frontier of models available at its April 2025 launch.

For agent builders, o3's high-effort mode is the primary differentiator: on tasks where a single correct answer has outsized value (debugging a subtle race condition, solving a novel algorithm problem, reviewing a complex legal clause), the reasoning depth of o3-high can outperform faster models despite the cost premium.

Use o3 via API

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY")

response = client.chat.completions.create(
    model="o3",
    messages=[{"role": "user", "content": "Solve this algorithmic problem step by step..."}],
    reasoning_effort="high",
)
print(response.choices[0].message.content)

Related Models & Benchmarks

• Compare: o4-mini · GPT-5.6 Sol · Gemini 2.5 Pro
• Benchmarks: ARC-AGI · LiveCodeBench · Arena Hard v2 · Humanity's Last Exam

Frequently Asked Questions

Specs and scores sourced from OpenAI's official o3 announcement (April 16, 2025) and Benchgen evaluations; third-party benchmark scores attributed inline. Pricing cited to the OpenAI pricing page. Last updated 2026-07-23.