Next-generation reasoning model optimized for mathematical rigor and complex software engineering
| Scenario | Input Tokens | Output Tokens | Uncached Cost | With Prompt Caching |
|---|---|---|---|---|
| Short Chat Query | 1,000 | 500 | $0.00600 | $0.00500 |
| Document Summarization | 10,000 | 2,000 | $0.0360 | $0.0260 |
| Codebase & Context Analysis | 100,000 | 20,000 | $0.3600 | $0.2600 |
| Batch Corpus Processing | 1,000,000 | 100,000 | $2.80 | $1.80 |
Ideal for production workloads demanding reasoning capabilities, deep context depth (200k tokens), and reliability from OpenAI. Excellent when predictable tokenomics and prompt caching support are paramount.
If your use-case requires sub-second streaming latency or ultra-high frequency classification at micro-cent pricing, consider lighter budget options such as Gemini Flash-Lite or Claude Haiku. For deep formal logic, consider dedicated reasoning models like o3.
For OpenAI o3, 1 million input tokens costs $2.00, while 1 million output tokens costs $8.00. If using prompt caching, repetitive input prefixes are discounted to $1.00 per million.
OpenAI o3 features a maximum context window of 200,000 tokens (~150,000 words), with a maximum output limit of 100,000 tokens per completion.
OpenAI o3 utilizes the OpenAI o200k_base (200k vocabulary). Token counting on TokenMath runs client-side to ensure maximum privacy.
Yes. OpenAI o3 supports prompt caching with a cached input rate of $1.00/1M (saving up to 50% on repeated input context).