o3 is a reasoning-focused chat model from OpenAI in the company's o-series, designed to produce internal reasoning before answering.
| Provider | Input / 1M | Output / 1M | Cached / 1M |
|---|---|---|---|
| $1.00 | $4.00 | - | |
| $1.00 | $4.00 | $0.250 | |
| $2.00 | $8.00 | $0.500 | |
| $2.00 | $8.00 | $0.500 | |
| $2.00 | $8.00 | $0.500 | |
| $2.00 | $8.00 | $0.500 |
Prices updated daily. Last check: Sep 5, 2026
Input, output, and batch rates, plus alternatives, for one provider at a time.
Benchmarks measured Sep 2026. Scores are independent evaluations, not vendor-reported.
o3 fits workloads where correctness on multi-step problems justifies a slower first response: mathematical derivations, scientific and quantitative analysis, algorithm design, code review and bug diagnosis, and the planning or verification stages of agentic systems where a single reasoning call decides subsequent actions. Its roughly 4.8-second time to first token makes it a poor match for latency-sensitive conversational front ends, autocomplete, or high-volume classification and extraction jobs — those are better served by a lighter, non-reasoning model, with o3 reserved for the harder subset of requests. A common pattern is routing: handle the bulk of traffic with a fast model and escalate only difficult cases to o3.
Pricing for o3 varies by provider and by pricing type (input tokens, output tokens, cached input, and in some cases batch rates). Because reasoning models bill their internal deliberation as output tokens, effective cost per request can be higher than the headline output rate implies. Check the pricing table on this page for current per-provider rates.
Tasks that reward deliberation: multi-step math, scientific and quantitative reasoning, code debugging and review, and planning steps inside agentic workflows. It is less appropriate for latency-sensitive chat interfaces or high-volume, low-complexity jobs.
o3 is an OpenAI o-series reasoning model, so it performs an internal reasoning pass before producing visible output. Artificial Analysis measured its time to first token at about 4,788 ms. Once generation starts, output streams at roughly 138.8 tokens per second.
The main difference is where time and tokens are spent. Non-reasoning chat models begin emitting output almost immediately; o3 deliberates first, which raises time to first token and adds reasoning tokens to the bill, in exchange for stronger handling of problems that require several logical steps.
Our database does not track a confirmed context window for o3. Consult OpenAI's model documentation or the specific provider's API reference before building applications that depend on long inputs.
OpenAI does not distribute o3 weights for self-hosting. Access is through API providers, which are listed with their current rates in the pricing table above.