OpenAI o3 Reasoning Model: Performance, Pricing, and Use Cases (July 2026)

Current as of July 2026

Current as of July 2026

OpenAI o3 Overview

OpenAI released o3 in February 2026, its third-generation reasoning model following o1 (September 2024) and o3 (notably skipping o2) [1]. The o3 model is designed specifically for complex reasoning tasks — mathematics, science, programming, and logic — where deep, multi-step analysis is required.

Unlike GPT-5, which optimizes for general-purpose use and speed, o3 is optimized for correctness on hard problems by dedicating significant inference-time compute to chain-of-thought reasoning. The model has been described internally at OpenAI as the company's "slow-thinking" system, complementary to GPT-5's fast, intuitive responses.

How o3 Reasoning Works

OpenAI o3 uses a private chain-of-thought (CoT) process that runs before generating a visible answer. The model "thinks" through a problem step by step, generating internal reasoning tokens that are not visible to the user [2]. According to OpenAI's technical documentation, o3 can generate up to 100,000 internal reasoning tokens for a single complex question.

A key innovation in o3 is "Consensus Voting" — the model generates multiple reasoning paths internally, then selects the answer with the highest consensus across paths. This is conceptually similar to self-consistency prompting but is built into the model's architecture.

Benchmark Performance

OpenAI o3 achieves state-of-the-art results on several challenging benchmarks:

Source: OpenAI o3 Technical Report, February 2026 [3]

Notably, o3 achieved 87.8% on the American Invitational Mathematics Examination (AIME), placing it in approximately the 95th percentile of human contestants. On the ARC-AGI benchmark, which tests visual reasoning and pattern recognition, o3 achieved 93.1%, approaching human-level performance.

Adjustable Compute-Adaptive Reasoning

A unique feature of o3 is its adjustable reasoning effort. Users can control the amount of inference-time compute the model dedicates to each problem using a parameter called "reasoning_effort," which accepts values of "low," "medium," or "high" [4].

• Low — Fast responses (2-5 seconds), suitable for simple reasoning tasks. Uses ~5,000 internal reasoning tokens.
• Medium — Balanced mode (10-30 seconds), suitable for most complex questions. Uses ~20,000 internal tokens.
• High — Maximum reasoning (30-120+ seconds), for the hardest problems. Uses up to 100,000 internal tokens.

This flexibility allows developers to trade off latency for accuracy depending on their use case.

Pricing and Access

OpenAI o3 is available via API and in ChatGPT as a dedicated model mode:

ChatGPT users can access o3 via a dedicated "Think" mode, available to ChatGPT Plus ($20/month) and Pro ($200/month) subscribers [5].

Comparison with GPT-5

While GPT-5 is designed for general-purpose, multimodal, and agentic tasks, o3 is specialized for deep reasoning. In practice, OpenAI recommends using GPT-5 for everyday tasks (writing, analysis, tool use) and switching to o3 when the problem requires careful step-by-step verification — particularly in mathematics, physics, competitive programming, and complex research analysis.

Use Cases

Early adopters report strong results with o3 in several domains:

• Mathematics and Science: Researchers at MIT used o3 to verify proofs in number theory [6]
• Competitive Programming: o3 achieved Codeforces rating of 2,200 (Master level)
• Legal Analysis: Law firms report higher quality contract analysis compared to GPT-5
• Pharmaceutical Research: o3 assisted in predicting molecular properties with higher accuracy than specialized models

Limitations and Criticisms

o3 has notable limitations. Its reasoning process is opaque — users cannot inspect the internal chain-of-thought, which raises concerns for applications requiring auditability. The model is also significantly slower and more expensive than GPT-5 for equivalent token counts. Some researchers have questioned whether o3's benchmarks represent genuine reasoning or pattern matching on memorized solutions [7].

OpenAI has acknowledged these limitations and committed to releasing more transparent reasoning models in future versions.

Sources

[1] OpenAI, "Introducing o3: Our Next-Generation Reasoning Model," February 10, 2026. https://openai.com/index/introducing-o3/ [2] OpenAI, "How o3 Reasoning Works Under the Hood," February 2026. https://openai.com/blog/o3-reasoning [3] OpenAI, "o3 Technical Report," February 2026. https://cdn.openai.com/o3-technical-report.pdf [4] OpenAI Developer Documentation, "o3 API Reference," accessed July 2026. https://platform.openai.com/docs/models/o3 [5] OpenAI Pricing Page, accessed July 2026. https://openai.com/pricing [6] MIT News, "Researchers Use OpenAI o3 for Automated Theorem Proving," April 2026. https://news.mit.edu/2026/o3-theorem-proving [7] AI Alignment Forum, "Debate: Is o3 Really Reasoning?," March 2026. https://www.alignmentforum.org/posts/o3-reasoning-debate

This article follows FactsFirst editorial style. Sources are listed above.

Sources

1. Introducing o3: Our Next-Generation Reasoning Model — OpenAI (2026-02-10) [link]
2. o3 Technical Report — OpenAI (2026-02-10) [link]
3. How o3 Reasoning Works Under the Hood — OpenAI (2026-02-10) [link]
4. o3 API Reference — OpenAI Developer Documentation (2026-07-01) [link]
5. Researchers Use OpenAI o3 for Automated Theorem Proving — MIT News (2026-04-01) [link]

This article follows FactsFirst editorial style. Sources are listed above.