OpenAI: OpenAI o1
openai/o1
Try OpenAI o1 for free in your browser, and add it to your app for free with Puter.js AI API.
Try it free Add to your app// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "openai/o1"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "openai/o1"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
OpenAI o1 is a reasoning model that thinks before answering using chain-of-thought, excelling at complex science and mathematics tasks. It was the first in OpenAI's "o" series designed for step-by-step logical reasoning.
Context Window 200K
tokens
Max Output 33K
tokens
Input Cost $11.25
per million tokens
Output Cost $45
per million tokens
Input text, image
modalities
Tool Use Yes
Knowledge Cutoff Sep 2023
Release Date Dec 5, 2024
Try OpenAI o1 for free
Try OpenAI o1 instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How OpenAI o1 performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 74.7% |
| Humanity's Last Exam Cross-domain reasoning | 7.0% |
| LiveCodeBench Recent coding problems | 67.9% |
| MATH-500 Competition math | 97.0% |
| AIME 2024 Advanced math exam | 72.3% |
| IFBench Instruction following | 70.3% |
| LCR Long-context reasoning | 65.0% |
| Terminal-Bench Hard Agentic terminal tasks | 12.9% |
| τ²-Bench Tool use / agents | 62.6% |
Scores sourced from Artificial Analysis.
Find other OpenAI models →
GPT-6.1 Sol
GPT-6.1 Sol is OpenAI's mid-tier model in the GPT-6 family, released September 29, 2026 as an update to GPT-6 Sol. It sits below the flagship GPT-6 Astra, takes text and image input, supports tool calling, and has a 1,050,000-token context window with 128,000 max output tokens. Pricing is unchanged from GPT-6 Sol at $2 per million input tokens and $10 per million output tokens, while scores are higher. It reaches 75.2% on DeepSWE 1.1 (68.8% for GPT-6 Sol), 71.4% on OSWorld 2.0 (64.4%), and 32.0% on GDP.pdf (28.0%). Its factual error rate at low reasoning effort fell from 11.4% to 7.7%. In one third-party comparison table, GPT-6 Astra scores 74.8% on DeepSWE and 73.5% on OSWorld at roughly five times the price. GPT-6.1 Sol suits coding agents, computer use, document question answering, and workflow automation where cost per task matters.
ChatGPT-6.1 Sol Pro
GPT-6.1 Sol Pro is OpenAI's higher-effort configuration of GPT-6.1 Sol, the mid-tier model in the GPT-6 family. Based on how OpenAI handled GPT-6 Sol Pro, it is likely the same underlying model with reasoning mode set to pro, though OpenAI's documentation for the 6.1 release does not confirm this. It has a 1,050,000-token context window and 128,000 max output tokens, at $2 per million input tokens and $10 per million output tokens. Supported parameters include reasoning effort, tools, tool choice, structured response formats, seed, and streaming. Sol Pro is intended for complex requests such as agentic coding, computer use, and workflow automation, where answer quality matters more than latency. We found no published benchmarks for the Pro configuration itself.
ChatGPT-6 Sol
GPT-6 Sol is OpenAI's mid-tier model in the GPT-6 family, released September 22, 2026 as a faster, cheaper counterpart to the flagship GPT-6 Astra, built on the same training methods. OpenAI positions it for complex development work that developers run repeatedly, such as building features, reviewing code, debugging, and analyzing data. OpenAI reports Sol scoring 68.8% on the DeepSWE 1.1 coding benchmark, 60.5% on OSWorld 2.0 for computer use at xhigh effort, and 56.4% on Agents' Last Exam, a test of professional work across 55 sub-industries. The company says Sol makes about half as many factual errors as GPT-5.6 Sol, approaching Astra-level reliability at a lower cost. At $2 per million input tokens and $10 per million output tokens, half of GPT-5.6 Sol's rate, Sol matches Claude Sonnet 5's standard pricing, though no published benchmark compares the two models directly.
Frequently Asked Questions
You can access OpenAI o1 by OpenAI through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
OpenAI o1 is free to try with a Puter account. Every account includes a free AI allowance, and you can chat with it in the playground on this page. You can upgrade your account anytime for a larger allowance.
OpenAI o1 is free to integrate using the Puter.js AI API. With the User-Pays Model, you can add AI to your app for $0, since users cover their own AI usage through their Puter account.
| Price per 1M tokens | |
|---|---|
| Input | $11.25 |
| Output | $45 |
OpenAI o1 was created by OpenAI and released on Dec 5, 2024.
OpenAI o1 supports a context window of 200K tokens. For reference, that is roughly equivalent to 400 pages of text.
OpenAI o1 can generate up to 33K tokens in a single response.
OpenAI o1 has a knowledge cutoff date of Sep 2023. This means the model was trained on data available up to that date.
OpenAI o1 accepts the following input types: text, image. It produces: text.
Yes, OpenAI o1 supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
OpenAI o1 scores 15.2 on the Artificial Analysis Intelligence Index, outperforming 57% of tracked models. On coding, it scores 39.7 (outperforms 46% of models).
Yes — the OpenAI o1 API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Add OpenAI o1 to your app for free
Developers can integrate OpenAI o1 for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.