Grok 4.6 Review and Effort Settings in Cursor

Grok 4.6 is available in Cursor, Grok Build, and the API. Standard API pricing starts at $2 per million input tokens and $6 per million output tokens. The Fast variant costs twice as much. SpaceXAI positions the model for long-running agents, coding, and ambitious visual projects. This Grok 4.6 review focuses on the practical choice of reasoning effort, speed tier, and acceptance workflow inside Cursor.

The 4.6 upgrade targets sustained work

SpaceXAI says Grok 4.6 received a longer supplemental training run covering general coding, knowledge work, kernel optimization, web development, and computer-aided design. The release highlights projects that begin with a broad product idea, become a working first version, and continue through several rounds of testing and revision.

Vendor benchmarks show Grok 4.6 High at 69.9 percent on CursorBench 3.2, up from 66.7 percent for 4.5. Its 65.9 percent DeepSWE 1.1 result remains below the GPT-5.6 Sol and Fable 5 results presented on the same page. Benchmark harnesses and settings vary, so these figures describe the intended strength rather than a pass rate in your repository.

Public discussion adds two practical details. Some users report fewer mistakes after moving to high or xhigh effort. Others are frustrated when Cursor's automatic model selection switches to Grok unexpectedly. Both concerns show how much the host configuration affects the experience. Pin the model and effort before comparing it with another option.

Fast buys latency rather than deeper reasoning

Standard pricing starts at $2 input and $6 output per million tokens. Fast costs twice as much and targets lower latency. It is separate from low, medium, high, and xhigh reasoning effort.

Increase effort first when code review or planning needs more reasoning. Consider Fast only when waiting time becomes the constraint and the standard model already passes quality checks. Treating Fast as a more intelligent tier can double the price without increasing acceptance.

Grok 4.6 is also available through OpenRouter, Vercel, Cloudflare, and other partners. Providers may apply different prices, limits, and context behavior. Keep the provider and host fixed during a benchmark so token and timing results remain comparable.

Fix the boundaries in Cursor

Open Cursor's agent settings and pin Grok 4.6 or configure the default to use the last selected model. Start at medium. A small targeted edit can use low, while cross-directory work or review can move to high. Reserve xhigh for difficult, high-value work with a strict acceptance bar.

Every task should name the files the model may change, protected areas, and required commands. Ask for test evidence, then inspect git diff. Grok can produce a substantial visual first pass, but an attractive first version can still omit existing behavior, responsive details, or data boundaries.

Separate planning from execution. Have the model list the files and validation steps first, then authorize the implementation. Break a large feature into independently testable stages. Grok 4.6 is designed to continue working, so a precise stopping condition prevents it from completing unrelated improvements.

Compare price in one repository

Choose five completed tasks that cover a bug fix, an API change, and an interactive page. Run them with standard medium, standard high, and fast high. Record first-pass test success, out-of-scope changes, total tokens, waiting time, and final cost.

When standard high already passes once, Fast offers only lower waiting time. Multiply the minutes saved by the team's time cost and compare the result with the doubled model charge. If medium and high differ only in review tasks, route high to review rather than using it throughout development.

Community reputation changes with the Cursor version, automatic routing, and personal rules. A repository baseline is stronger evidence. Use the same prompt, commit, and test environment across all runs.

Verdict

Grok 4.6 combines attractive token pricing with strong long-workflow ambitions. It is a useful candidate for indie developers building a working product from an initial idea. Start with standard medium and raise effort for difficult work. Buy Fast only when latency has a measured cost. Pin the Cursor model, constrain scope, and verify with tests and git diff to turn cheap tokens into a low cost per completed task.