OpenAI's DevDay 2026 was less about one blockbuster model than about infrastructure. The company spread more than twenty announcements across its developer platform, and the common thread is clear: OpenAI wants agents that run persistently, in the cloud, across applications, at a price low enough to leave running.
The headline model: GPT-6.1 Sol, not Astra
The main model release was GPT-6.1 Sol, an update to GPT-6 Sol tuned for coding, computer use and professional work 3. OpenAI's pitch is economic. It says Sol comes close to its top-tier GPT-6 Astra on several evaluations while costing one-fifth of Astra's standard input and output token prices, with cached input at $0.10 per million tokens 3. The model is available through the API, ChatGPT Work and Codex 3. One commentator summarized it as "a cheaper near-flagship model" 1.
Astra itself received no announced update. OpenAI used GPT-6 Astra only as the benchmark Sol is measured against 3. OpenAI did not say whether a newer Astra is in development or why none was shown. Speculation aside, the practical result is that DevDay's flagship-adjacent offering was a cost play, not a capability leap at the top end.
That choice fits the rest of the event. Agents that run continuously consume a lot of tokens, so the cost per call matters more than leaderboard position. A model that is nearly as good at a fifth of the price makes always-on workflows easier to justify.
Agents API gets computer use
The most consequential developer change may be in the Agents API, which now supports computer use. Applications can operate software through graphical interfaces rather than relying only on structured APIs 3. The API also brings in multi-agent capabilities from Codex, along with tool search, tool calling and context compaction. OpenAI manages the execution infrastructure underneath 3. These features are available through the API and in Codex and ChatGPT Work on selected plans 3.
InfoQ's recap also lists a new Decisions API and expanded plugin capabilities for ChatGPT 3. Taken together, these point toward what one outlet called a shift "beyond individual model interactions toward persistent agent workflows" 2.
Codex moves to the cloud
Codex can now run in cloud environments as well as on local machines, so developers can start remote tasks from other devices 23. The Codex CLI gained voice input and an /agents interface for delegating and monitoring several tasks at once 23. A new code-review workflow analyzes diffs and flags potential problems in GitHub pull requests and GitLab merge requests 3.
The Medium writer saw a change in the developer's role here: once Codex runs in the cloud, the human's job "is increasingly reviewing" 1. That is a fair description of where these tools are heading. Developers become supervisors of delegated work more than authors of every line.
Security gets attention
All three accounts highlight Codex Security Cloud. It scans repositories and new commits, investigates findings, removes duplicates and prepares fixes 123. The Medium piece adds that it can run on demand or on a schedule, keeps working with the user's laptop closed, and is available to Pro, Business, Enterprise and Edu users 1.
That writer also covered Private Intelligence, a set of options for data-sensitive enterprises. One option, Zero Data Retention with Private Safety Processing, lets safety reviews run without OpenAI staff seeing the content. A Private Inference preview built on confidential computing is due this fall 1. Writing from a security background, the author called round-the-clock automated vulnerability fixing "great news for defenders" but warned that it also speeds up attackers 1. That trade-off deserves more attention than it got on stage.
Where the coverage diverges
The accounts largely agree on the facts and differ in tone. InfoQ's recap is a straightforward feature list 3. The Medium piece is more opinionated and focused on security 1. The lavx.hu summary captures some developer skepticism. Attendees praised computer use, cloud Codex and cheaper models, but some questioned whether the releases actually advanced beyond existing agent platforms 2.
The takeaway
DevDay 2026 reads as a consolidation event. OpenAI is not trying to win this round on raw model capability. It is building the plumbing for persistent, delegated, cloud-hosted agents and cutting prices to make heavy use affordable. The open questions are whether that plumbing is meaningfully better than competitors' platforms, and how well autonomous agents with computer access and repository write paths will be secured. OpenAI's security tooling suggests it knows the second question matters. Whether these tools work as promised will only be clear once they are in wide use.
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