On September 29, 2026, OpenAI ran its annual DevDay at an information density that bordered on overload. By the official account (from the OpenAI DevDay 2026 recap), the event carried "more than 20 major announcements"; counting the recap item by item, we get 25. The same day, OpenAI confirmed that ChatGPT now serves 1.2 billion weekly users. The list ran long, from collaborative slides to sixteen Sign in with ChatGPT partners sharing quota across products, but only two items truly justify rearranging your workflow overnight: a resident agent called Dots is officially on the job, and a new model called GPT-6.1 Sol delivers near-flagship intelligence at one fifth of the flagship price.
This piece unpacks the news in that order: what Dots actually is and who can use it; the full GPT-6.1 Sol pricing table plus the most persuasive numbers from the official benchmarks; then the Ultrafast speed tier, the four-piece Codex cloud loop, Computer Use in the Agents API, and the AWS partnership. Every pricing and benchmark figure below comes from the official recap, and paraphrased claims are labeled as such.
Dots: the agent that lives in your account
The official definition of Dots is surprisingly restrained: a "highly capable, always-on agent" that understands what matters to you, works on your behalf continuously, and takes over important work to give you your time back. The valuable phrase here is not "highly capable." It is "always-on." OpenAI is moving the agent from a place you visit, a chat window you open when you need something, to a resident that sits in your account and keeps watch. The product shape changes, and so should your expectations: this is less a tool you summon and more a colleague who is online by default.
Availability draws two lines. Pro and Business Premium users in eligible markets get Dots directly. Enterprise, Edu, and Healthcare workspaces can enable it as a beta, but only through a workspace admin, and it is off by default. In other words, consumers and high-end business tiers go first, while large organizations get a built-in observation period.
One paraphrase needs untangling. Coverage after the event described Dots as tracking long-running tasks, proactively reporting back, and autonomously operating software. That triple comes from third-party summaries (QbitAI and AI tool aggregators), not from the recap's own wording. The "autonomously operating software" part maps more closely to Computer Use in the Agents API, which the recap presents separately. Conflating second-hand summaries with the official record is the most common distortion in launch-cycle coverage, so it is worth drawing that line explicitly.
It is worth thinking one step further about how Dots relates to what came before. Over the past year we have grown used to two agent shapes: the assistant in a chat box that acts only when you speak, and the developer-side coding agent that runs a task in the background and reports when done. Dots' "resident" model is a third shape. It does not wait for a prompt; it is designed to keep learning your preferences and workload and to pick work up on its own. The technical precondition is the maturing of the Agents API and Computer Use; the commercial precondition is a subscription tier strong enough to carry a premium benefit. Pro and Business Premium satisfy both. As for how much work Dots actually takes over and how often it stumbles, the recap offers no quantitative claims, so we will not invent any. Real usage will tell.
GPT-6.1 Sol: near-Astra intelligence at a fifth of the price
GPT-6.1 Sol is a major upgrade to GPT-6 Sol. Per the official recap, it strengthens agentic coding, Computer Use, and professional work performance, with intelligence described as "near-Astra." Astra is the flagship of the GPT-6 family (we reviewed it in depth, see After GPT-6 Astra: How the Top Flagships Really Compare), while Sol has always held the value position; we covered its original launch pricing separately (see GPT-6 Sol and Luna Halve Prices). This article covers only what 6.1 changes relative to them. The point of this upgrade is not to top the charts but to compress flagship-level intelligence into a price that survives large-scale daily use.
Here is the pricing table, per million tokens, from the official recap:
| Model | Input | Cached input | Output |
|---|---|---|---|
| GPT-6.1 Sol | $2 | $0.10 | $10 |
| GPT-6 Astra | $10 | $1 | $50 |
| GPT-6 Luna | $0.10 | $0.01 | $0.50 |
Standard input and output both land at one fifth of Astra's rates, which is where the "costs a fifth" framing comes from. The more interesting column is cached input. At $0.10 it undercuts Sol's own standard input by 95 percent and GPT-6 Sol's cache by 50 percent. For agentic workloads, which recycle huge contexts and call tools repeatedly, cache hit rate effectively determines the bill, so this number may matter more than the headline fifth. To see how these rates sit in the broader market, our October 2026 coding-model price roundup is a good companion read.
There is one availability line that is easy to miss. GPT-6.1 Sol is available now to all ChatGPT Plus, Pro, Business, Enterprise, and Edu users, in ChatGPT Work and Codex, with gpt-6.1-sol as the API model name. But it is not yet usable in Chat. To put 6.1 Sol to work, you have to place it in a workflow rather than a conversation. The restriction itself signals how OpenAI positions this model: it is built for agent scenarios first.
Four benchmarks worth your attention
The official recap includes a wall of benchmark data. Rather than repeating it, here are the four results that carry the most decision value (all official figures).
First, average cost per task on Terminal-Bench Science 0.1: GPT-6.1 Sol at $5.47, Opus 5.5 at $23.21, and Astra at $23.80, a reduction of more than 75 percent. Note that Astra remains the top scorer among tested models on this benchmark at 68.1 percent. So this is not a "flagship dethroned" story; it is a "score near the flagship, bill cut to under a quarter" story.
Second, the OSWorld 2.0 offline set (v2026.08.08): at maximum reasoning effort, 7 percentage points above GPT-6 Sol at less than half the cost; 2.1 points below Astra at roughly one seventh of the per-task cost. OSWorld measures computer operation, the home turf of agent scenarios, and that gap structure says more than any single score.
Third, AutomationBench 1.0.6, which spans 47 tools: at medium reasoning effort, 2.2 points above Opus 5.5 at about a third of the cost, and 4.8 points above GPT-6 Sol under the same settings. Tool-dense automation is exactly where the cache lever bites hardest.
Fourth, DeepSWE v1.1: parity with Astra at roughly one fifth of the cost, plus 6.4 points above GPT-6 Sol's best result. Stack on the GDP.pdf result, where it outscores Opus 5.5 including fallbacks at less than half the per-task cost, and all four datasets point the same way.
Two more numbers deserve their own note. On factuality, the factual error rate at low reasoning effort drops from 11.4 percent to 7.7 percent, roughly a one third reduction, and across settings the gap to Astra's error rate stays within 1.9 points. On safety disclosure, in the broken-search-tool test at maximum reasoning effort, the share of cases where the model failed to tell the user the tool was broken: GPT-6.1 Sol at 2.1 percent, GPT-6 Sol at 4.9 percent, Astra at 1.5 percent, and Luna at 28.7 percent. Luna's figure is startling at a glance and a reminder that the cheapest tiers still lag badly on honesty. For business-critical work, do not read the price table alone.
Read together, the pattern is clear. 6.1 Sol is not chasing every leaderboard. It packages "performance near the flagship" and "a bill that is a fraction of the flagship" in one offering. For teams running batches of agent tasks, that combination often beats a higher raw score, because the same budget buys several times more experiments. Of course, these are official figures; third-party reproduction and real-world bill verification will take time, as with any vendor's launch numbers.
Ultrafast, the Codex loop, and agents on AWS
On speed, the Ultrafast tier pushes up to 8x faster generation inside Codex at 300 tokens per second, and up to 6x via the API. GPT-6 Astra Ultrafast is available today across the API, ChatGPT Work, and Codex for Pro 500 and Enterprise; GPT-6.1 Sol Ultrafast is listed as coming soon. For anyone who writes code, 300 tokens per second is a change in interactive feel, not just a billing line.
Codex, meanwhile, completed a four-piece cloud loop. Codex in the cloud: your environment reachable from cloud, phone, or any device, with reusable dev environments and team-shared settings plus approved permissions, for Plus, Pro, Business, Healthcare, Education, and Enterprise. Codex CLI refresh: voice kickoff, an /agents view for tracking multiple tasks at once, prompt editing, session resume, and worktrees, on every plan. Code Review: read summaries and diffs on desktop, ask questions on GitHub PRs and GitLab MRs, with an automatic review mode that completes a first pass in the cloud while you are away, also on every plan. Codex Security Cloud: on-demand or scheduled scans of entire GitHub repositories, continuous checks on new commits, investigation and deduplication with fixes staged in the cloud, powered by the Daybreak Blue model, for Pro, Business, Enterprise, and Edu. We previously looked at how Codex functions as a working agent (see OpenAI's Data Shows Agents Became the Productivity Engine in Six Months); these four pieces fill in the remaining gaps: a cloud environment, a review channel, and a security sentry.
The Agents API update is about exporting Codex's capabilities. It adds Computer Use support for building agents that interact with software to complete tasks. Codex's multi-agent capability, tool search, tool calling, and context compression all enter the API, with OpenAI running the underlying infrastructure; access is via the API, and inside Codex and ChatGPT Work for Pro 500 and Enterprise. The AWS partnership deserves equal attention: Bedrock Managed Agents, powered by OpenAI, runs the Agents API's core capabilities natively on AWS with integration into AWS resources, enabling OpenAI agents that live entirely inside AWS. Agent runtimes no longer sit only in OpenAI's own data centers.
More cards in the deck
Beyond the two headlines, a few more cards deserve mention. The Pro 500 tier is officially priced at 25 times the ChatGPT Plus quota, and combined with Ultrafast and heavy Agents API usage it is clearly aimed at intensive agent users. Sign in with ChatGPT now shares quota across 16 partners, including Cognition's Devin, Notion, Vercel, T3, OpenClaw, and Dactyl; login state and quota are starting to flow across products, turning the ChatGPT account into an identity and quota layer. The OpenAI Marketplace opened with 32 launch partners, laying the groundwork for an app distribution story.
On privacy, one announcement is easy to overlook: Private Intelligence, built on zero data retention and Private Safety Processing, with a Private Inference preview this autumn. When enterprises wire agents into internal workflows, the concern is rarely capability; it is where the data goes. How fast this piece of the puzzle completes is worth tracking.
The year of the resident agent
Close the recap and the trend is hard to miss: models are getting cheaper (Sol at a fifth), agents are moving in (Dots), and runtimes are decentralizing (onto AWS). The intersection of those three lines is the new category you might call the resident agent. Manus, in the same window, pushed further with Cue: a standalone mobile and desktop app where each agent gets its own email address, its own phone number, its own wallet, and its own computer, can pay within a budget you set, and is currently free by invitation. We have previously covered the inflection point for computer-operating agents; what is happening now is that vendors are packaging these models as colleagues who live in your account.
There is also a wry detail. On the very same day as DevDay, an open-source project with no connection to OpenAI claimed the "dots" name first: the repository feder-cr/dots, a web agent that ships with its own browser, MIT licensed, with a README that states plainly it is not affiliated with OpenAI. Within about three days it had collected more than 2,400 stars. The official Dots is a hosted resident agent inside a subscription; this open-source dots is the self-hosted route where you can swap models at will. We will cover the comparison and a self-hosting walkthrough separately.
For regular users, my judgment is this: the most worthwhile experiment in the second half of 2026 is not switching to yet another smarter chat model. It is choosing one resident agent, moving it into your daily routine, handing it a few genuinely repetitive jobs, watching your data feeds, triaging mail, guarding CI, and then judging whether it has earned your trust. The model price war has reached the point where cheap intelligence is abundant. What is scarce is a resident that actually saves you time.
Join the discussion
Dots, Cue, or the open-source dots: which resident agent would you invite into your daily routine? Tell us in the comments.