OpenAI • LLM
The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learning to reason...
Context Window
200K tokens
Input Price/1M
$15.00
Output Price/1M
$60.00
Parameters
—
Speed
127 tok/s
Latency (TTFT)
14.5s
Max Output
100K tokens
o1 results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Terminal-Bench Hard | 13.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LiveCodeBench | 68.0 | 100.0 | — |
| SciCode | 36.0 | 100.0 | — |
| AA Coding Index | 20.5 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU-Pro | 84.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 59.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 30.7 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| GPQA Diamond | 75.0 | 100.0 | — |
| IFBench | 70.0 | 100.0 | — |
| HLE | 8.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Tau²-Bench | 63.0 | 100.0 | — |
o1 is an AI model developed by OpenAI, classified as a large language model (LLM). It is a multimodal model, capable of processing text, images, and potentially other media types. As a proprietary model, it is available via OpenAI's cloud API. With a context window of 200K tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.
o1 is usage-based, priced at $15/1M input tokens and $60/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. The premium pricing reflects the model's frontier capabilities, recommended for complex reasoning, analysis, and generation tasks where quality takes priority over cost.
o1 was evaluated on 11 different benchmarks, covering categories like Agentic, Coding, Knowledge, Long Context, overall, Reasoning, Tool Use. Results show solid performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
o1 is suitable for a wide range of AI applications: long document analysis (contracts, legal proceedings, codebases), automation with tool calling (API integration, databases, external systems), image and visual document analysis (OCR, diagrams, screenshots), multimodal processing combining text and images, complex reasoning, math problem solving, and logical analysis, text generation, summarization, translation, and general assistance.
In the 2026 AI model ecosystem, o1 competes directly with similarly capable models. Key competitors include Claude (Anthropic), Gemini (Google), and open source models like Llama (Meta) and Qwen (Alibaba). The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learning to reason...
o1 costs $15/1M input tokens and $60/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
In available benchmarks, o1 scored: Terminal-Bench Hard: 13/100, LiveCodeBench: 68/100, SciCode: 36/100. See the full table above for a detailed comparison.
No, o1 is a proprietary model from OpenAI. It is available via cloud API. For open source alternatives, check our open source model ranking.
o1 excels at complex reasoning and problem solving. With its large context window, it handles long documents, codebases, and extended conversations. It supports tool calling for API integrations and automation.
Last updated: June 01, 2026 • View methodology →