OpenAI o1

OpenAI o1 is a large language model from OpenAI, first released on 5 December 2024. It is proprietary (closed-weights) and sits in the reasoning, frontier, openai, agents, and enterprise categories of our catalog. Long chain-of-thought reasoning — unbeatable on hard math and code. This page covers OpenAI o1 pricing, benchmarks, API limits, speed, modalities, best use cases, and how it compares with similar models — so you can decide whether it belongs in your stack in 2026.

As a language model, OpenAI o1 is evaluated on reasoning quality, coding ability, latency, context window size, and dollars-per-million-tokens. The context window is 200k tokens (about 150k words), which determines how much prompt, document, and conversation history you can send in one request. At a typical 3:1 input-to-output mix, the blended API price is about $26.25 per 1M tokens. On our intelligence index it ranks #4 of 22 language models we track with a score of 76/100 (strong production-tier).

Teams usually shortlist OpenAI o1 when they need a dependable OpenAI option for production chat, agents, retrieval-augmented generation, or coding copilots. Common fits include research problems, olympiad-level math, and algorithm design. Reviewers consistently call out tops math and gpqa leaderboards and self-checks its work as standout strengths. Trade-offs to weigh include very slow, very expensive, and overkill for simple tasks. The sections below break down pricing tables, benchmark charts, token limits, input/output modalities, and head-to-head comparisons so long-tail queries — from “OpenAI o1 API pricing” to “OpenAI o1 vs GPT-5.5” — are answered on this page.

If you are migrating from an older OpenAI model or switching labs entirely, treat this page as a decision brief: skim the overview stats, confirm API pricing fits your volume, check whether the context window covers your longest documents, then validate quality on a golden set of prompts. Benchmarks and charts help shortlist; your own evals decide. We refresh catalog numbers periodically (last update 2026-06) so figures stay useful through the year.

Public benchmark scores help compare OpenAI o1 with other LLMs on knowledge, graduate-level science, competition math, and coding. Reported figures in our catalog include MMLU 91.8, MMLU Pro 80, GPQA 78, MATH 94.8, and HumanEval 92.4. These are not a substitute for evals on your own prompts, but they are useful for shortlisting.

Our intelligence index (76/100) normalizes those benchmarks into a single strong production-tier score so you can scan the leaderboard quickly. The performance chart below shows each benchmark against the current catalog leader.

OpenAI o1 offers a 200k-token context window — roughly about 150k words of English text. Everything in a single API call counts against that budget: system instructions, chat history, retrieved documents, tool schemas, and the model’s reply. Exceeding the window truncates or errors depending on the provider.

Large windows help with long PDFs, multi-file code reviews, and multi-hour agent traces, but bigger contexts also cost more tokens and can add latency. Prefer retrieval that stuffs only relevant chunks, summarize old turns, and reserve headroom for up to 100k output tokens.

Token limits define how much OpenAI o1 can read and write per request. Total context is capped at 200k tokens. Maximum completion length is 100k tokens — even if context remains, the model stops generating beyond that ceiling unless you continue in a follow-up call. Providers may also enforce organization-level rate limits (RPM/TPM) separate from these per-request caps.

Practical tip: set max_tokens intentionally. Leaving it unbounded wastes budget on verbose answers; setting it too low truncates JSON or code. For structured outputs, prefer schemas/tool calls and keep completions tight.

The charts on this page visualize OpenAI o1 against catalog peers. Benchmark bars show academic scores versus the current leader; the similar-models comparison table plots intelligence, speed, and blended price so you can see trade-offs at a glance. Use them to answer “is OpenAI o1 fast enough?” and “is the quality jump worth the premium?” without opening a spreadsheet.

Best use cases for OpenAI o1 follow from its strengths, price point, and modality support. Match the model to the job: frontier reasoning for hard planning, fast/cheap tiers for classification, image/video/speech specialists for media pipelines.

Based on catalog notes, OpenAI o1 is a particularly strong fit for research problems, olympiad-level math, and algorithm design. Validate with a short bake-off on your real prompts before a full cutover.

Anti-patterns: do not use a frontier-priced model like a generic classifier if a smaller model scores within a point on your eval; do not stuff entire corpora into context when retrieval would be cheaper; and do not skip structured outputs if you plan to parse OpenAI o1 responses in code.