o4-mini-deep-research

o4-mini-deep-research is a advanced-tier language model from OpenAI. o4-mini-deep-research is a proprietary model available exclusively through OpenAI's API. It is a reasoning model that uses extended chain-of-thought (CoT) processing before producing a final answer — significantly more capable on complex math, science, and multi-step logic tasks, but slower and more expensive per query than standard models. The o4 series is OpenAI's latest reasoning model family, combining strong reasoning capability with multimodal input support and improved efficiency.

o4-mini-deep-research is priced at $2/1M input tokens and $8/1M output tokens. Output tokens are 4× more expensive than input tokens — this ratio reflects the sequential nature of autoregressive generation, which requires a separate forward pass per output token versus parallel processing of all input tokens simultaneously. At a typical usage pattern of 1,000 input tokens and 500 output tokens per call, o4-mini-deep-research costs approximately $6.00 per 1,000 API calls. OpenAI does not publish a prompt caching rate for this model, so repeated long contexts are billed at the standard $2/1M input rate.

Its 200K-token context window is in the top tier for production models, enabling full-document analysis, long multi-turn conversations, and large codebase understanding without chunking. Median latency (P50 time-to-first-token) is approximately 30000ms — better suited to batch or async workloads where latency is less critical. Throughput is approximately 40 tokens/second — on the slower side; plan for longer generation times on lengthy outputs. Maximum output length is 100K tokens — sufficient for long-form content generation, detailed code files, and extended reports.

o4-mini-deep-research is best suited to research, reasoning, cost. Supported capabilities include vision inputs (images alongside text), streaming token output for real-time UIs. Vision support enables document analysis, screenshot understanding, diagram interpretation, and visual question answering — images are converted to tokens at the standard input rate based on resolution. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how o4-mini-deep-research compares to similar models across these dimensions.

o4-mini-deep-research sits in the advanced tier — strong general-purpose models balancing capability and cost, suitable for most production applications. Unlike open-weights alternatives, o4-mini-deep-research is only available via OpenAI's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing o4-mini-deep-research to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (83/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.