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Neither model is the proven all-purpose winner in Cursor. Opus 5.5 has the larger documented context limits, while Grok 4.7 has lower listed standard token rates and is treated as a first-party Cursor model on eligible paid plans. Choose based on the task, the access and billing rules of your plan, and results from a controlled trial—not on a universal ranking. Cursor’s current documentation, checked October 25, 2026, lists Opus 5.5 at 300k standard context and up to 1M maximum, and Grok 4.7 at 256k standard and up to 500k maximum. Cursor’s Opus 5.5 page and Grok 4.7 page are the place to verify volatile specifications before making a plan or workflow decision.
What is the practical difference between Opus 5.5 and Grok 4.7 in Cursor?
The clearest documented differences are model category, context limits, and how usage is billed. Cursor identifies Grok 4.7 as a first-party model and Opus 5.5 as a third-party Anthropic model; the two therefore do not necessarily draw on the same plan allowance or incur costs in the same way. Their context limits also differ, but a larger maximum alone does not establish better code or more reliable answers.
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| Comparison | Claude Opus 5.5 | Grok 4.7 |
|---|---|---|
| Cursor model category | Third-party model from Anthropic | First-party Cursor model |
| Model ID listed by Cursor | claude-opus-5-5 |
grok-4.7 |
| Standard context listed by Cursor | 300k | 256k |
| Maximum context listed by Cursor | 1M | Up to 500k |
| Effort levels documented | Not listed on the cited model page | Low, medium, high, and xhigh |
| Published base or standard token rates | $4 per million input tokens and $20 per million output tokens at base rates | $2 per million input tokens and $6 per million output tokens at standard rates |
These are current documentation figures, not fixed guarantees: Cursor’s model catalog, availability, context settings, and pricing can change. The rate comparison is also not a full bill comparison. Cache, Fast, long-context, plan-included usage, and plan-specific billing can alter what a particular task costs. See Cursor’s Models & Pricing documentation for the applicable pricing details.
The numbers do give a narrow apples-to-apples starting point: at the listed base or standard rates, Grok’s input rate is half Opus’s ($2 versus $4 per million tokens), and its output rate is less than a third of Opus’s ($6 versus $20 per million). That arithmetic compares token prices only. It does not account for how many tokens a model uses to finish a task, how much included usage your plan provides, or whether a different tier applies. Treat the rate gap as a reason to check actual usage—not as proof that Grok will cost less for every completed feature or fix.
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How do plan access and billing differ?
Grok 4.7 draws from Cursor’s model usage pool on paid plans, according to Cursor. Opus 5.5 is a third-party model billed at API list rates on current eligible plans; legacy plans can follow different pool rules. Confirm your own plan and usage terms rather than assuming a model’s headline rate is the amount you will pay for every request.
Cursor says model availability depends on the plan. Its available-models documentation distinguishes first-party models from third-party models and notes that eligible plans are required for third-party access. For teams, an administrator’s model controls can also affect whether a model appears. If either option is absent, the likely first checks are plan eligibility and team settings, followed by the current availability page.
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At the published base or standard rates, Grok 4.7’s listed input and output prices are lower than Opus 5.5’s. That is a useful starting point for comparing token charges, but not enough to predict the cost of finishing a task. A model may use a different number of tokens, require more follow-up, or be used under a different allowance or speed tier. Cursor also lists distinct cache, Fast, and long-context rates, so requests using those options should not be costed from the standard figures alone.
To compare your actual spend, record the plan, model, task, settings, and usage shown for each run. Keep input and output usage separate, and note whether caching, a speed option, or long-context pricing applied. This makes the comparison meaningful for your workflow without treating a per-million-token price as a complete estimate.
For example, a team choosing a default should not simply multiply a rough monthly token forecast by the standard rate and call that its budget. First establish whether the plan routes a model through a usage pool or bills it at API list rates; then check the rates for the settings the team expects to use. Compare the result against completed work, including retries and human repair time. Cursor’s pages list separate Fast and cache rates, but the figures can vary by model and usage mode, so consult the current pricing table rather than applying one model’s tier assumptions to the other.
Which model is a better fit for long-context work?
Opus 5.5 has the larger context figures published by Cursor: 300k standard and a 1M maximum, compared with Grok 4.7 at 256k standard and up to 500k maximum. If a task genuinely needs a larger amount of material in one request, those listed limits make Opus 5.5 the more promising starting point. Verify that the desired context setting is available to your plan and check applicable long-context pricing.
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A context limit is a capacity figure, not a quality score. It does not mean every request will automatically include that much of a repository, that the model will use all supplied material correctly, or that the maximum is available in every configuration. For a large codebase, ask a concrete question about the files and behavior that matter, and check whether the answer reflects the relevant code rather than assuming that a larger ceiling solved retrieval or reasoning.
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When the extra context may matter
- You need to reason across several files, a long specification, or a broad change request in one session.
- You want to provide substantial project context without splitting the task into many separate prompts.
- The smaller standard window is not enough for the material you need to include, and the larger setting is actually available and affordable on your plan.
If the task is a focused bug fix or a small, clearly scoped edit, a larger maximum may not provide a practical advantage. Try a representative request at the context setting you expect to use rather than choosing solely by the largest published number.
It also helps to distinguish a model’s listed ceiling from the context your workflow actually uses. Cursor publishes a standard figure and a larger maximum for each of these models; those are not a promise that every session runs at the maximum. If a repository task can be answered with a few relevant files, the larger ceiling may be immaterial. If you need an unusually broad specification or many related files in the same request, the difference may be worth testing—but include the actual context setting and any applicable long-context rate in your comparison notes.
What does Cursor say each model is suited to?
Cursor’s descriptions suggest different emphases, but they are vendor positioning, not an independent head-to-head assessment. Cursor describes Grok 4.7 as a frontier model for coding and knowledge work, with an emphasis on difficult, long-running tasks and self-verification. It describes Opus 5.5 as Anthropic’s latest Opus model and says it performs strongly on CursorBench, frontend and UI work, communication, and subagent coordination. Treat these statements as clues for what to test, not proof that either model will perform better on your project.
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For complex coding and longer agent tasks
Grok 4.7’s documented emphasis on difficult, long-running work and self-verification makes it reasonable to trial on a task that requires sustained investigation or multiple implementation steps. Measure whether it actually completes the work, checks its changes, and avoids unnecessary edits in your repository. The description does not establish a guaranteed success rate or make it the best choice for every agent task.
For frontend and UI work
Cursor specifically highlights Opus 5.5 for frontend and UI work. If that matches your workload, use a real interface task to evaluate it: for example, a change with an existing design system, responsive behavior, and clear acceptance criteria. Judge the rendered or reviewed result and the code changes, not just the fluency of the explanation. Cursor’s statement is its own model-page claim, not an independent comparison against Grok 4.7.
For coordination and communication
Cursor also points to Opus 5.5’s communication and subagent coordination. Those qualities matter when you rely on an agent to explain decisions or divide work, but the cited information does not establish that it will be more concise, more accurate, or more effective for every team. Include those outcomes in your evaluation only if they matter to your workflow.
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For teams that use subagents, define what coordination means in practical terms before the test: for example, whether delegated work comes back in a form that is easy to review and whether the final changes satisfy the same acceptance criteria. The model-page description is a reason to include that workflow in a trial, not a guarantee about the number or quality of agents it will coordinate. Likewise, a model’s effort setting is part of the test setup. Cursor documents low, medium, high, and xhigh for Grok 4.7; the cited Opus page does not list effort levels. Do not assume equivalent controls exist for both models or compare different settings as if the model alone changed.
How can you compare the models fairly in your own project?
Run the same representative task in the same repository and hold the prompt, codebase context, tool permissions, settings, and evaluation criteria constant. A disciplined test is more useful than comparing anecdotes from unrelated projects, since a change in context or tools can affect the result as much as the model choice.
- Choose a task with an observable outcome. Use a real bug, feature, refactor, or interface change. Write down what counts as correct before either model starts—for instance, specific behavior, tests that must pass, and files that should not be changed.
- Record your setup. Note the Cursor plan, date, model, effort or speed setting available, included context, tool permissions, and any team restrictions. Access and usage terms can differ by plan and change over time.
- Give each model the same starting conditions. Use the identical prompt and repository state. Keep the task boundaries, instructions, and permitted tools the same; do not give one model extra hints or retries unless you are deliberately measuring that difference.
- Evaluate the result, not the confidence of the response. Check whether the implementation meets the acceptance criteria, whether relevant tests pass, whether the model made unrelated edits, and whether its explanation matches the actual diff. Record failures and required corrections.
- Track usage and completion effort. Capture token usage and applicable rate tier where available, plus the number of follow-up prompts and time spent reviewing or repairing the work. A low per-token rate is not a saving if the task needs substantially more correction.
- Repeat for tasks you care about. One run is a noisy sample. Repeat with several representative tasks—such as a bug fix, an unfamiliar code path, and a UI change—before changing a team default.
For a team comparison, summarize the test conditions and date alongside the outcomes. Avoid turning one successful run into a general claim about model quality. A result is useful when it answers a local decision—such as which model handles your UI changes more reliably under your plan—not when it pretends to rank every possible use.
A compact scorecard for the trial
Use the same scorecard for both runs, and record observations rather than relying on a single overall impression. At minimum, note whether the task met the acceptance criteria, which required tests passed, whether the diff stayed within scope, and how much repair or review was needed. For work where the model must explain its reasoning or coordinate delegated tasks, rate those outcomes separately instead of allowing a polished explanation to conceal a faulty implementation.
Cost and speed belong on the scorecard too, but compare them carefully. Record token use, rate tier, and follow-up count where Cursor exposes them. Also record completion time and the time a developer spent validating or correcting the result. The model with the lower listed token rate may not be the most economical choice if it takes more retries or creates more review work; the model with the larger context ceiling may not be faster if the task does not need it. These are project-specific findings to observe, not outcomes that can be inferred from the published model pages.
When the two models have different available controls, document that limitation rather than forcing a false equivalence. For instance, if you use one of Grok’s documented effort levels, record which level you selected. Match the closest available speed or effort setting on the other model only if Cursor offers one; otherwise mark the settings as different and interpret the result accordingly. The goal is a useful decision for your workflow, not a laboratory claim broader than the test supports.
Which should you choose for coding in Cursor?
Start with Grok 4.7 if you want to try Cursor’s first-party model under the paid-plan usage pool and your work fits its documented coding and long-running-task positioning. Start with Opus 5.5 when a larger published context ceiling is relevant, when you want to evaluate Cursor’s stated UI-work emphasis, or when its plan and billing terms fit your usage. For either, check the model is available on your account and verify current pricing before settling on a default.
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There is no independently established head-to-head winner in the cited sources. Cursor’s own description of a model and any launch-time CursorBench claim should not be read as an independent benchmark proving superiority over the other option. If quality matters more than convenience, test both on the same work and use the results you can inspect: correctness, test outcomes, review burden, token use, and consistency.
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- Choose by cost structure: account for plan usage, rate tier, caching, and long-context pricing—not just standard token rates.
- Choose by observed task fit: weigh performance on your own coding and interface tasks above vendor descriptions.
- Choose by team policy: confirm administrator controls and eligible access if the model must be available to a team.
If you need a quick default rather than a full evaluation, use the documented differences as a shortlist, not a verdict. Grok’s first-party status and lower listed standard rates may make it a sensible first trial when your plan’s model pool is the priority. Opus’s larger listed context and Cursor’s stated frontend/UI emphasis may make it a sensible first trial for broad-context or interface work. Then verify the choice with the tasks your team actually repeats, because access, cost, and quality are separate questions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you check before relying on these figures?
The comparison reflects Cursor’s documentation accessed October 25, 2026. Its model pages publish the context specifications and listed rates but do not assign those captured figures a publication year. Treat them as current documentation values, not permanent specifications or independently measured results. Recheck the official model and pricing pages if you are making a purchase, configuring a team, or building a cost estimate.
Context ceilings, prices, plan eligibility, and model catalogs are product details that can move independently. In particular, do not assume that a model appearing in documentation is enabled for every account, that its maximum context can be selected under every plan, or that a standard token rate applies to every request. Cursor’s availability and pricing documentation are the authoritative places to check those account-dependent conditions.
When sharing a comparison internally, include the date and plan alongside any price or access claim. The official pages reviewed here do not give the captured context and price figures a publication year, so attaching a named year to them would imply more than the pages establish. A dated note such as “Cursor documentation accessed October 25, 2026” keeps the figures traceable while making clear that they may change.
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Frequently Asked Questions
Can I use Opus 5.5 in Cursor without a paid plan?
Cursor’s available-models documentation says third-party models such as Opus require eligible plans. Since plan names and eligibility can change, check the current availability page while signed in rather than relying on an older plan description.
Why does a model not appear in my Cursor model picker?
The model may not be included in your plan, or a team administrator may have disabled it. Check your account’s plan and ask the workspace administrator to review model controls before assuming there is a technical fault.
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Does a 1M context limit mean Cursor sends my whole codebase to Opus?
No. A model’s published context ceiling describes capacity, not what Cursor includes in a particular request. What is available to the model depends on the request and the context Cursor supplies.
Are Cursor’s descriptions of these models independent benchmark results?
No. Statements on Cursor’s model pages are Cursor’s descriptions, and the cited materials do not establish a reproducible independent head-to-head benchmark. Treat those claims as product guidance and validate them against tasks you can inspect.
Which model is cheaper to use in Cursor?
Grok 4.7’s listed standard rates are lower: $2 per million input tokens and $6 per million output tokens, compared with Opus 5.5’s base rates of $4 and $20. Those figures do not determine the final cost by themselves. Plan usage, cache, Fast and long-context rates, token consumption, and follow-up work can change the comparison.
What effort settings does Cursor list for Grok 4.7?
Cursor lists low, medium, high, and xhigh effort levels for Grok 4.7. The cited Opus 5.5 model page does not list effort levels, so check the current model controls rather than assuming the same settings are available for both.
Does Opus 5.5’s larger context window make it better for every coding task?
No. Its listed context is larger, but a context ceiling is not a quality rating and does not mean every request uses the maximum. The difference matters most when your task needs more material in one request and the relevant setting is available and affordable.
The Bottom Line
Bottom line: Cursor’s current documentation gives Opus 5.5 the larger context figures and Grok 4.7 the lower listed standard token rates, while placing the models under different access and billing rules. Cursor’s model descriptions are useful starting points, not an independent head-to-head verdict. Check your plan and current pricing, then test both on the same representative tasks and compare correctness, review effort, and actual usage.
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