o3 (reasoning)
Pure reasoning engine for complex analysis and scientific work.
via OpenAI →
What is this model?
o3 is OpenAI's second-generation reasoning model (after o1) and builds on chain-of-thought architecture where the model "thinks aloud" before answering. For complex questions where GPT-4o gives a fast surface answer, o3 can "think" for minutes and thereby produce deeper, more accurate answers. Especially strong on mathematics, scientific reasoning and complex coding tasks.
Strengths
Strengths: top scores on reasoning benchmarks (AIME, GPQA, FrontierMath), excellent step-by-step problem solving, strong in legal analysis and scientific literature, 200K context window. For "difficult questions you don't want to get wrong" this is often the right pick within our stack — Claude Opus 4 has comparable depth but at ~5× higher cost.
Best suited for
- Research, deep dives and analyses
- Complex reasoning and multi-step tasks
- Legal analysis and contract review
How ZelixAI uses this model
We position o3 as the "deep thinking" bot within ZelixAI: for research questions, contract analysis, complex technical escalations and anything where GPT-4o would give a too fast/superficial answer. Latency is a tradeoff — expect 5-30 seconds per heavy request. Not suited for real-time chat; route via GPT-4o mini first and only escalate complex questions.
Real-world examples within ZelixAI
Real example: a law firm uses o3 to analyse contract clauses against case law — the model "thinks" 30-60 seconds per clause and gives a substantiated risk classification. A construction consultancy uses o3 to interpret building regulations (Bbl, Bbk, NEN standards) for specific project questions. An R&D department has o3 summarise scientific articles and formulate hypotheses.
Limitations and caveats
Limitations: higher latency (5-30 sec for complex questions) — not for real-time interaction. Higher cost than GPT-4o mini, roughly equal to GPT-4o. US cloud provider — not for strict EU data residency. Not suited for multimodal tasks (o3 is text-only); use GPT-4o instead. Reasoning models can "overthink" and answer simple questions unnecessarily complex.
Technical specifications
| Provider | OpenAI |
| Context window | 200K tokens |
| Throughput | 15–40 tokens/s (Average) |
| Cost tier | Mid-range |
| Tool / function-calling | yes |
| Data residency | United States (cloud provider) |