What do I need to do before November 12, 2026 in Cursor?

Audit anywhere you pinned an OpenAI model — the model picker, .cursor rules files, cloud agent configs and any CI automation calling Cursor. Replace each pinned GPT model with a tested alternative such as Claude Sonnet 5, Claude Opus 5, Grok 4.6 or Composer, then re-run your evaluation set before the cutoff date.

What replaces GPT-5.6 Sol in Cursor?

Claude Opus 5 at $5/$25 per MTok is the closest quality-tier replacement for hard multi-file work. For cheaper day-to-day agentic coding, Grok 4.6 at $2/$6 and Claude Sonnet 5 at $2/$10 both remain available in Cursor after the cutoff, as does Cursor's own Composer line.

Will my Cursor subscription still work after the cutoff?

Yes. Cursor continues operating normally; only OpenAI-branded models are removed from the model pool. Subscriptions, Composer, cloud agents, Auto routing and the Anthropic, Google and xAI models are unaffected by OpenAI's decision.

How do I test a replacement model properly?

Build a fixed set of 10 to 20 real tasks from your own repository, run them on your current GPT model and each candidate, and score on completion, diff quality, tool-call correctness and token cost. Judge on your codebase rather than public benchmarks, since agentic coding quality varies sharply by repo shape and language.

Quick Answer

How to Prepare for Cursor's OpenAI Cutoff (Nov 12)

Published: August 29, 2026

The Short Answer

OpenAI models disappear from Cursor on November 12, 2026. Cursor itself keeps working. The migration is small if you start now and annoying if you start on November 11.

The work is four steps: audit what you pinned, choose replacements, evaluate on your own code, then fix configs and automation. Budget an afternoon for a solo developer, a day or two for a team with CI integration.

Step 1: Audit Every Place a Model Is Pinned

Model names hide in more places than the picker. Check all of these:

Location	What to look for
Cursor model picker	Manually selected GPT-5.6 Sol / Terra / Luna
`.cursor/rules` and project rules	Instructions naming a specific model
Cloud agent configurations	Pinned model per agent
Team/org policy settings	Admin-enforced model allowlists
CI or scripts calling Cursor	Hard-coded model IDs
Documentation and onboarding docs	”Use GPT-5.6 Sol for reviews” style guidance

A fast grep across your repo catches most of it:

grep -rniE "gpt-5\.6|gpt-5-6|sol|terra|luna" \
  --include="*.json" --include="*.md" --include="*.yml" \
  --include="*.yaml" --include="*.toml" .

Done when: you have a written list of every pinned reference, with an owner for each.

Step 2: Pick Candidate Replacements by Task Class

Do not look for one universal substitute. Match the model to what the pinned model was actually doing.

If you used GPT for…	Best remaining option in Cursor	Cost per MTok
Hard multi-file agentic work	Claude Opus 5	$5 / $25
Everyday coding and edits	Claude Sonnet 5	$2 / $10
Cheap high-volume passes	Grok 4.6	$2 / $6
In-editor completion and fast loops	Composer (first-party)	Plan-included
Long-context repo reasoning	Gemini 3.7 Flash	$0.75 / $3.75

Two notes that change the arithmetic. Gemini 3.7 Flash’s $0.75/$3.75 is introductory through December 31, 2026, reverting to $1.50/$7.50 on January 1, 2027 — do not build a 2027 budget on the promo rate. And Anthropic’s newer tokenizer emits roughly 30% more tokens for the same text than its older models, so Claude’s real cost sits above what the headline rate implies.

Done when: each pinned reference from Step 1 has one primary and one fallback candidate.

Step 3: Evaluate on Your Own Repository

Public benchmarks will not tell you whether a model handles your codebase. Build a small fixed eval instead.

Pick 10-20 real tasks you have already completed — bug fixes, refactors, test additions — where you know what good output looks like.
Run each task on your current GPT model to capture a baseline, while you still can.
Run the same tasks on each candidate model.
Score four things: task completion, diff quality (did it touch only what it should?), tool-call correctness, and total token cost.

Keep the prompts identical across models. The single most common migration mistake is rewriting the prompt for the new model and then concluding the new model is better.

Done when: you have a scored table and a defensible first choice per task class.

Step 4: Roll Out and Verify

Change the configs from your Step 1 list, then confirm nothing silently falls back to a model you did not choose.

Update pinned models in project rules and cloud agent configs.
Update team policy allowlists so nobody is blocked on November 12.
Update onboarding docs — stale docs are how a fixed problem returns in six months.
If you use Auto routing, you likely need no changes at all; Auto simply routes within the smaller pool.

Verification: re-run three tasks from your eval set end-to-end through the changed configuration, not through a manually selected model. That catches the case where a config edit did not take effect.

Done when: a fresh clone of the repo, opened by someone else on your team, produces the intended model without manual selection.

Common Pitfalls

Waiting for Cursor to migrate you. Cursor may add defaults, but it cannot know which model your review pipeline needs. Pinned choices are yours to re-make.

Assuming price parity means quality parity. Grok 4.6 at $2/$6 is dramatically cheaper than Opus 5 at $5/$25. On routine edits that gap is free money; on hard multi-file refactors it is not.

Migrating everything to one model. You just experienced a single-supplier failure. Landing on a single replacement supplier reproduces the exact risk you are cleaning up. Split by task class across at least two vendors.

Forgetting that OpenAI models still exist elsewhere. If a workflow genuinely requires GPT-5.6, the models remain available via the OpenAI API, Codex CLI, GitHub Copilot and Azure. The correct answer is sometimes “run that one workflow outside Cursor” rather than “replace the model.”

The Timeline

Date	What to have done
Now	Step 1 audit complete
Mid-September 2026	Candidates chosen, eval set built
Early October 2026	Evaluations run, primary choices locked
Late October 2026	Configs and docs updated, team notified
November 12, 2026	OpenAI models removed from Cursor

Eleven weeks is generous notice by 2026 standards. The teams that will struggle are the ones treating it as a November problem.