GPT-6 Model Guide: Instructions and Workflow Checks
OpenAI published a GPT-6 model guide on October 2 for GPT-6 Astra, GPT-6.1 Sol and GPT-6 Luna. Use it to review permissions, completion evidence and long-running work.
The GPT-6 model guide published by OpenAI on October 2 brings production checks, instructions and long-running work into one practical review. For a team already using GPT-6, the useful next step is to examine one workflow: what the agent may change, what evidence must come back, and which decisions should stop it. Our review worksheet below turns that guidance into a bounded exercise for an existing project.
What is new in the GPT-6 model guide
The October 2 official guide connects model choice, instruction design and tool coordination. It discusses GPT-6 Astra, GPT-6.1 Sol and GPT-6 Luna; it is operational guidance, not another model launch or a new price announcement. For the already released Sol update, see our GPT-6.1 Sol profile.
One useful connection is between the assignment and its completion evidence. A model menu answers which system to use. A working assignment also tells the agent which result matters and where its authority ends. Review those together before changing several settings at once.
Replace broad permission rules with one clear boundary
OpenAI’s earlier September 11 instruction-design article, linked from the new guide, recommends task-specific document routing, concise skill descriptions and explicit permission for safe workflows. Those recommendations predate October 2; the new guide brings them into a family-wide operating checklist.
Here is our proposed exercise. Choose a local bug-fix task with disposable test data. Write down the permitted directory, the behavior to repair and the command that reproduces it. State that the agent may inspect relevant files, make the scoped patch and repeat affected local tests. Put production deployment, customer-data access and changes to the feature contract behind a review boundary.
That boundary lets the agent proceed with the requested work without turning every test invocation into a new approval request. It also gives a reviewer a concrete stopping point. A failing local assertion is work to investigate; a proposed change to what the feature promises is a decision to bring back.
Compare the task prompt, repository instructions and relevant skill. If one permits the local repair while another requires approval for every edit, resolve the contradiction. Keep instructions that protect the work, and make their scope explicit. Our code-change handoff prompt supplies a reusable reporting structure; adapt the handoff to your task, and state the permitted actions separately.
Define a completion packet before the agent starts
Use a small completion packet for that same exercise: the original failing input, the expected behavior, the patch, the observed test result and any remaining check. This packet is our suggested review format, not an OpenAI benchmark.
For a pagination bug, the evidence might show that a request beyond the last page returned the wrong records, then show the corrected empty result under the agreed contract. Include nearby boundaries such as zero results and invalid page sizes. Ask for actual commands and exit statuses rather than a general statement that the code was tested.
If the task includes a visible interface, add an interaction check. Our frontend QA example contains a deliberately broken fixture where clearing a search input does not restore the result cards. A screenshot can show the empty input while missing that state failure. An interaction assertion should check the cards after Clear is activated. That existing example illustrates the review method; it is not a new measurement of the October guide.
An agent can finish the patch while a release decision remains pending. Name both states in the handoff so the next person knows whether to review code, repeat a test or approve a deployment.
Keep updates separate from dependent work
The new guide explains that mid-turn steering queues revised instructions without reversing completed actions. It also describes asynchronous tools and GPT-6.1 Sol’s beta multi-agent support. Independent work can proceed while a tool runs, but a dependent step must wait for its result.
Apply this to the bug-fix exercise. A documentation review can run alongside a local test if each has its own inputs. The release decision depends on the completed test result, so it cannot use the fact that the command started as its pass condition.
When requirements change, make the update specific. For example: keep the existing page-size limits, preserve the public function signature, and repair the out-of-range response. If an earlier step already changed the signature, inspect that diff and restore the agreed contract as a separate action. A correction in the conversation is not evidence that the files now match it.
Keep parallel tasks from making overlapping edits. Assign one owner to shared files, collect the other task’s findings, and integrate them after the relevant checks finish. This is our coordination recommendation for the exercise, not a claim that every client exposes the API’s orchestration features.
Measure the whole workflow before tuning it
The API deployment checklist recommends representative evaluations and cost per successful task. It separates model selection from reasoning effort and lists supported effort levels. GPT-6 Astra and GPT-6.1 Sol do not support none; tool calling for those models requires Responses.
For a repeatable comparison, preserve a baseline with the same task inputs and acceptance check. Record the model ID, effort, completion time and whether the packet met the contract. When using the API, also record reported input, output, reasoning and cache-write tokens where available. Keep failed attempts in the total: a cheap request that needs several retries may not produce a cheap completed job.
Change one setting for the next pass and compare the same checks. If correctness worsens, investigate the failed case before moving on to a different prompt and model. Use our prompt caching calculator for a separate cost estimate; measured workflow results still need their own records.
The October 2 guide gives builders a reason to align instructions, permissions and evidence before expanding an agent’s responsibilities. Start with one existing workflow, make its stopping rules and completion packet clear, and use the observed result to decide what to improve next.