o1

o1 is a frontier-tier language model from OpenAI. o1 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 o1 series is OpenAI's reasoning model family, using extended internal chain-of-thought to solve problems that require careful multi-step reasoning.

o1 is priced at $15/1M input tokens and $60/1M 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, o1 costs approximately $45.00 per 1,000 API calls. Prompt caching reduces repeated input tokens to $7.5/1M — a 50% discount that compounds significantly for applications with long shared system prompts, multi-turn conversations, or document Q&A where the same context is reused across many queries.

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 12000ms — better suited to batch or async workloads where latency is less critical. Throughput is approximately 35 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.

o1 is best suited to reasoning, coding, math. Supported capabilities include vision inputs (images alongside text), function calling / tool use for agentic workflows, 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. Function calling support makes it suitable for building AI agents, structured data extraction pipelines, and any workflow requiring the model to invoke external APIs or return typed JSON. 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 o1 compares to similar models across these dimensions.

o1 sits in the frontier tier — the highest-capability models available, used when quality is the primary constraint and cost is secondary. Unlike open-weights alternatives, o1 is only available via OpenAI's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing o1 to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (86/100) 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.