{"id":119847,"date":"2026-10-07T10:55:48","date_gmt":"2026-10-07T05:25:48","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/openai-dots-vs-gpt-6-1-sol\/"},"modified":"2026-10-07T10:55:50","modified_gmt":"2026-10-07T05:25:50","slug":"openai-dots-vs-gpt-6-1-sol","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/openai-dots-vs-gpt-6-1-sol\/","title":{"rendered":"OpenAI Dots vs GPT-6.1 Sol: Understanding the New Era of AI Agents"},"content":{"rendered":"\n

OpenAI<\/a>'slatest AI releases signal a shift from conversational AI toward systems that can reason, use tools, operate software, and work toward goals with less step-by-step supervision<\/strong>.<\/p>\n\n\n\n

Two announcements from OpenAI's September 2026 DevDay make this shift particularly clear: Dots<\/strong>, a new category of always-on AI agents<\/a>, and GPT-6.1 Sol<\/strong>, a lower-cost model designed for complex coding, computer use, and professional workflows.<\/p>\n\n\n\n

At first glance, comparing OpenAI Dots vs GPT-6.1 Sol might seem straightforward. Both are designed around more capable AI agents and autonomous workflows. But they operate at different layers of the AI stack.<\/p>\n\n\n\n

Dots is an agent experience. GPT-6.1 Sol is a model.<\/strong><\/p>\n\n\n\n

Understanding that distinction helps explain where AI agents are heading\u2014and why the next generation of AI systems will be less about generating an answer and more about taking action to accomplish a goal<\/strong>.<\/p>\n\n\n\n

What Are OpenAI Dots?<\/strong><\/h2>\n\n\n\n

OpenAI Dots<\/a> are persistent, always-on AI agents designed to work toward user goals 24\/7<\/strong>. Unlike a traditional chatbot that primarily responds to individual prompts, Dots are designed to continue working on tasks and projects after the initial instruction, making them better suited to ongoing, multi-step work.<\/p>\n\n\n

\"\"<\/figure>\n\n\n\n

Each Dot has its own cloud computer<\/strong>, allowing it to interact with digital environments and work across connected applications. OpenAI also says Dots can learn from user feedback over time<\/strong>, helping them adapt their work to a user's preferences and requirements.<\/p>\n\n\n\n

Dots can connect to more than 4,000 applications<\/strong> through OpenAI's plugin ecosystem. This gives them access to the tools and services needed to carry out tasks rather than simply generating a response in a chat window.<\/p>\n\n\n\n

For example<\/strong>, instead of asking an AI to create a one-time research summary, a user could give a Dot an ongoing objective such as tracking a project, gathering information from multiple applications, and organizing the results. <\/p>\n\n\n\n

The Dot can continue working toward that objective 24\/7<\/strong>, reducing the need for users to repeatedly provide instructions or manually coordinate each step.<\/p>\n\n\n\n

Dots are available through ChatGPT<\/strong> and can also be accessed through workplace platforms such as Slack and Microsoft Teams<\/strong>, allowing them to operate within the tools where users already work.<\/p>\n\n\n\n

For example<\/strong>, instead of asking an AI to summarize several project documents, a user could give a Dot a broader objective such as:<\/p>\n\n\n\n

\"Prepare the weekly product update and identify anything that needs my attention<\/strong>.\"<\/p>\n\n\n\n

The agent can potentially gather information, work across connected applications, organize findings, and return a completed result.<\/p>\n\n\n\n

The important innovation is therefore not simply better text generation. It is persistent task execution<\/strong>.<\/p>\n\n\n\n

What Is GPT-6.1 Sol?<\/strong><\/h2>\n\n\n\n

GPT-6.1 Sol<\/a> is a new model in OpenAI's GPT-6 family<\/a> designed to provide near-Astra performance for complex work at substantially lower cost<\/strong>. OpenAI positions it specifically for coding, computer use, and professional workflows.<\/p>\n\n\n\n

Its API pricing is:<\/strong><\/p>\n\n\n\n

Metric<\/strong><\/td>GPT-6.1 Sol<\/strong><\/td><\/tr>
Input<\/td>$2 \/ 1M tokens<\/strong><\/td><\/tr>
Cached input<\/td>$0.10 \/ 1M tokens<\/strong><\/td><\/tr>
Output<\/td>$10 \/ 1M tokens<\/strong><\/td><\/tr>
Context window<\/td>1.05M tokens<\/strong><\/td><\/tr>
Maximum output<\/td>128K tokens<\/strong><\/td><\/tr>
Reasoning effort<\/td>Low, Medium, High, XHigh, Max<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

OpenAI reports that GPT-6.1 Sol can approach GPT-6 Astra's performance on several demanding workloads while costing substantially less. <\/p>\n\n\n\n

For example<\/strong>, OpenAI says it matches Astra on DeepSWE v1.1 at roughly one-fifth the cost and comes within 2.1 percentage points of Astra on the tested OSWorld 2.0 computer-use setting at maximum reasoning effort.<\/p>\n\n\n\n

That makes Sol particularly interesting for developers building high-volume AI agents<\/strong>, where model quality matters but inference economics can determine whether an agent is practical to run continuously.<\/p>\n\n\n\n

OpenAI Dots vs GPT-6.1 Sol: What Is the Difference?<\/strong><\/h2>\n\n\n\n

The biggest difference is where each technology sits in the AI stack<\/strong>.<\/p>\n\n\n\n

Feature<\/strong><\/td>OpenAI Dots<\/strong><\/td>GPT-6.1 Sol<\/strong><\/td><\/tr>
What it is<\/td>AI agent\/product<\/td>AI model<\/td><\/tr>
Primary purpose<\/td>Perform ongoing work for users<\/td>Power complex AI workloads<\/td><\/tr>
Always-on behavior<\/td>Yes<\/td>Depends on the application<\/td><\/tr>
Cloud computer<\/td>Yes<\/td>Supports computer-use tools<\/td><\/tr>
Tool use<\/td>Built into the agent experience<\/td>Available through API tools<\/td><\/tr>
App integrations<\/td>4,000+ apps through ecosystem<\/td>Developers integrate tools themselves<\/td><\/tr>
Persistent goal-oriented work<\/td>Core feature<\/td>Can enable it when used in an agent<\/td><\/tr>
Coding<\/td>Can use coding capabilities\/tools<\/td>Strong coding focus<\/td><\/tr>
Computer use<\/td>Agent capability<\/td>Native tool support<\/td><\/tr>
Custom development<\/td>More managed<\/td>Highly developer-oriented<\/td><\/tr>
Best suited for<\/td>Users and organizations wanting managed agents<\/td>Developers building AI applications and agents<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

The simplest way to think about the relationship is:<\/p>\n\n\n\n

Dots = the agent that performs the work<\/strong><\/p>\n\n\n\n

GPT-6.1 Sol = the intelligence developers can use to build systems that perform the work<\/strong><\/p>\n\n\n\n

There is therefore some overlap in capabilities, but they are not substitutes in the traditional sense.<\/p>\n\n\n\n

How Do Dots and GPT-6.1 Sol Approach AI Agents Differently?<\/strong><\/h2>\n\n\n\n

The difference becomes clearer when looking at the workflow.<\/p>\n\n\n\n

Dots: Goal First<\/strong><\/h3>\n\n\n\n

Dots is designed around a user's objective.<\/p>\n\n\n\n

The user can provide a goal, preferences, and feedback. The agent can then work across connected tools and continue making progress.<\/p>\n\n\n\n

For example:<\/strong><\/p>\n\n\n\n

Goal \u2192 Research \u2192 Gather information \u2192 Work across apps \u2192 Organize results \u2192 Return outcome<\/strong><\/p>\n\n\n\n

This is closer to delegating work to a digital employee.<\/p>\n\n\n\n

OpenAI describes Dots as agents that can work toward goals 24\/7 and learn from feedback over time.<\/p>\n\n\n\n

GPT-6.1 Sol: Capability First<\/strong><\/h3>\n\n\n\n

GPT-6.1 Sol is designed for developers who want to build these kinds of workflows.<\/p>\n\n\n\n

A developer can combine Sol with:<\/p>\n\n\n\n