o3

o3 is OpenAI's most capable reasoning model in the o-series lineup. It uses extended chain-of-thought (CoT) processing — the model "thinks" through problems internally before producing a final answer, spending significantly more compute on reasoning than standard models. This approach delivers state-of-the-art performance on hard science, mathematics, competitive programming, and multi-step logic tasks, with o3 achieving near-human performance on several benchmarks that frontier models like GPT-4o struggle with. It supports vision inputs, function calling, and streaming, with a 200K-token context window.

o3 is priced at $10.00 per million input tokens and $40.00 per million output tokens — among the most expensive models in OpenAI's lineup, reflecting the additional compute consumed by extended reasoning. At 1,000 input + 500 output tokens per call, it costs approximately $0.03 per 1,000 API calls. The actual cost per query is often higher in practice because reasoning models generate more output tokens (the internal thinking process contributes to output token count). Prompt caching is available at $2.50/1M for repeated inputs. For workloads where o3's reasoning capability is genuinely needed, the cost premium is typically justified — but for tasks that GPT-4o handles well, o3 adds cost without proportional quality improvement.

o3's 200K-token context window enables analysis of very large codebases, lengthy research papers, or extended multi-document contexts. Latency is significantly higher than standard models — median TTFT can range from 5 to 60+ seconds depending on the complexity of the reasoning task and the "effort" setting (low/medium/high). Throughput is approximately 30–50 tokens per second for the visible output portion. Maximum output is 100,000 tokens, enabling very long-form generation. The effort parameter (low/medium/high) controls how much reasoning compute the model applies — low effort reduces latency and cost at the expense of reasoning depth, high effort maximizes quality for the hardest problems.

o3 is best suited to tasks where reasoning depth matters more than speed or cost: competitive programming, advanced mathematics, scientific analysis, complex multi-step planning, and any task where GPT-4o produces incorrect or shallow answers. It is not the right choice for high-volume, latency-sensitive, or cost-constrained workloads — for those, GPT-4o or GPT-4o mini are more appropriate. Vision support makes it useful for complex diagram analysis, mathematical figure interpretation, and scientific image understanding. Function calling works but is slower than GPT-4o due to the reasoning overhead.

o3 sits at the top of OpenAI's capability hierarchy, above GPT-4o and alongside o4-mini (which trades some capability for lower cost and latency). Its main competitors are other extended-reasoning models from Anthropic and Google — both of which use similar chain-of-thought approaches. On coding benchmarks, o3 and its closest competitors are closely matched, with task-specific results varying. On mathematics, o3 leads most alternatives. For teams evaluating reasoning models, the key decision is whether the task genuinely requires extended reasoning — many tasks that seem to need it are actually well-served by GPT-4o at a fraction of the cost. Always benchmark with your specific prompts before committing to o3 for production.