o3: OpenAI's Reasoning Specialist
o3 is OpenAI's advanced reasoning model, using extended chain-of-thought thinking to tackle complex mathematics, coding, and scientific problems that challenge conventional language models.
Specifications
At a glance
Parameters
Undisclosed
Context Window
200,000 tokens
Training Data Cutoff
March 2025
Release Date
April 2025
Licence
Commercial (Proprietary)
Pricing (Input)
$2.00 per 1M tokens
Pricing (Output)
$8.00 per 1M tokens
Overview
About o3
o3 is OpenAI's dedicated reasoning model, the successor to o1 and o1-pro. Rather than generating responses immediately, o3 engages in extended internal chain-of-thought reasoning — spending more compute 'thinking' before producing a final answer. This approach delivers breakthrough performance on tasks requiring deep logical deduction, multi-step mathematics, complex coding, and scientific reasoning. The model achieved notable results on challenging benchmarks including ARC-AGI, competition-level mathematics, and PhD-level science questions. o3 can adjust its reasoning effort using a 'thinking budget', allowing users to trade off between quality and latency/cost depending on the task difficulty. While o3 is not a replacement for general-purpose models like GPT-4.1 (it is slower and more expensive for routine tasks), it is the model to reach for when a problem genuinely requires deep reasoning. OpenAI also released o3-mini, a more affordable variant that retains strong reasoning at lower cost and latency.
Strengths
Capabilities
- Extended chain-of-thought reasoning for complex problems
- Exceptional performance on mathematics and formal logic
- Strong scientific reasoning across physics, chemistry, and biology
- Advanced coding and algorithm design capabilities
- Adjustable thinking budget to balance quality vs cost
- 200K context window for processing long problems
- o3-mini variant for cost-effective reasoning
Considerations
Limitations
- Significantly higher latency due to extended reasoning process
- More expensive per-task than general-purpose models for simple queries
- Chain-of-thought tokens consume additional output budget
- Overkill for straightforward tasks — use GPT-4.1 instead
- Proprietary model with no self-hosting option
Best For
Ideal use cases
- Competition-level mathematics and theorem proving
- Complex software engineering and algorithm design
- Scientific research requiring multi-step deduction
- Graduate-level academic problem solving
- High-stakes analytical tasks where accuracy justifies additional cost
Pricing
Input: $2.00/1M tokens, Output: $8.00/1M tokens. Internal reasoning tokens billed at output rate. o3-mini: $1.10/1M input, $4.40/1M output. Available via OpenAI API and Azure.
FAQ
Frequently asked questions
GPT-4.1 is a general-purpose model optimised for instruction following and broad capabilities. o3 is a reasoning specialist that uses extended thinking to solve hard problems. Use GPT-4.1 for most tasks and o3 when you specifically need deep reasoning on complex problems.
Both are reasoning models using chain-of-thought. o3 tends to lead on the hardest benchmarks. DeepSeek R1 is open-source and significantly cheaper. For most reasoning tasks, R1 provides strong results at lower cost; o3 is better for the most challenging problems.
o3-mini is a smaller, more affordable reasoning model that retains much of o3's reasoning capability at lower cost and latency. It offers adjustable reasoning effort levels, making it practical for production use cases that need reasoning but cannot afford o3's full compute.
o3 uses 'thinking tokens' — internal chain-of-thought reasoning steps generated before the final answer. This additional computation dramatically improves accuracy on hard problems but adds latency. Simple questions can be answered faster by GPT-4.1.
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