Grok Bot For Coding + Which AI Models To Run Around It: Live GoldieBench Scores And What They Mean For Dev Work

Grok Bot for coding picks its own model for every task, so the useful benchmark question is which models you run next to the Bot, not which one runs inside it.

This page reads the live GoldieBench pages on 8 November 2026 and explains what those scores do and don't tell you for coding work.

Grok Bot facts come from xAI's FAQ and How Grok Bot Works page; every score is quoted from the GoldieBench page I read it on.

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Grok Bot doesn't let you pick a model

The FAQ is direct: there is no model picker, Grok Bot chooses per task, and the choice can differ from one request to the next.

xAI doesn't say which model handles a given request.

So no number below is a Grok Bot score, and the Grok rows shouldn't be read as one.

The bench is useful for the parts of a coding setup where you do choose: the model that writes your briefs and reviews pull requests in your Agent OS, and the model you pick in your own editor.

Where a model choice does exist around the Bot

Bots can hand coding work to Cursor Cloud Agents, and the teams docs say that work runs on separate computers under your existing Cloud Agent controls.

Cursor's Cloud Agents docs say those agents use a curated selection of models and let you set the context window size for supported ones.

The Grok Bot docs don't say which model a delegated run uses, so don't assume you can steer it from the Bot.

Your clean choice points are therefore your Agent OS planner, your PR reviewer and your editor's coding agent.

What GoldieBench actually measures

The methodology page sets the rules: every model gets the same fixed prompt, one shot, and must return one self-contained HTML file that runs in a browser.

Each result gets Julian's own 0 to 10 score, and the board ranks models by their average across scored tasks.

That is a test of building things from scratch: games, simulations, visuals and pages.

It isn't a test of fixing a bug in an existing repo, reading a stack trace or passing a test suite.

Use it as a signal for "can this model ship a working page in one go", which matters for briefs and reports, not as proof of repo-level coding skill.

The board read on 8 November 2026

All seven numbers come from the cards on the all models page.

Fusion is a multi-model panel, not a single model, so compare it with care.

Grok 4.7: the numbers on its page

The Grok 4.7 page gives an average of 7.15/10 over 20 scored tasks, with 0 gold, 2 silver and 3 bronze medals.

The same page lists outside reference numbers it credits to x.ai: CursorBench 4.0 at 46.3% and DeepSWE v1.1 (high effort) at 71.0%.

The page itself labels those as external benchmarks, not GoldieBench medal scores.

Cursor's own Grok 4.7 model page describes it as built for coding and knowledge work, with a 256k standard window and 500k long context.

So if you want a Grok model in your editor, Grok 4.7 is the one whose page talks most about code, even though its one-shot build average sits below the top of the board.

The Grok row and its Pages category

The Grok benchmark breakdown shows 8.09/10 and rank #8 of 26 ranked frontier models.

Its category table puts Grok at 8.3 on Pages across 3 tasks, against a field average of 7.7.

Two of those page builds, Landing Page and Webos Page, scored 9.0 with gold.

For a coding Bot, that category is the closest match to the release trackers and bug dashboards you'll ask for, but remember it's the Grok model on the bench, not the Bot.

Pairing by coding job

• Briefs and PR review in your Agent OS: pick from the top of the board, such as Claude Opus 5 at 8.27 or GPT-5.6 Sol at 8.16, and check its own page first.
• Report and dashboard pages you build yourself: the Grok row's 8.3 Pages average is the most relevant single number here.
• In-editor coding: GoldieBench doesn't test repo work, so use the vendor's coding benchmarks with care; the Grok 4.7 page shows the x.ai figures above.
• The Bot itself: no choice to make; spend the effort on approvals and review instead.

Two examples, step by step

A web agency triaging client bugs

1. The agency's Agent OS, running a top-of-board model, turns each client email into a bug brief.
2. Grok Bot reproduces the bug on staging with a test account and attaches screenshots.
3. For a scoped fix, the Bot hands the change to a Cursor Cloud Agent, which can open a pull request.
4. The Agent OS model reviews the diff against the brief, and a developer merges it by hand.

An online shop wanting a weekly release page

1. The owner asks Grok Bot for a Page summarizing merged pull requests in plain English.
2. Because she can't choose the Bot's model, she judges the page on accuracy against the real commits.
3. If she wants a custom HTML version for the team, she builds it in her Agent OS with a model that scores well on page tasks.

Where the bench pages disagree

A few numbers don't line up across pages, so quote them with their page.

• The Grok card on /models/ says 47 tasks; the Grok breakdown says 43 scored tasks, with data refreshed 2026-10-27.
• The methodology page still lists Grok as unranked, while the board ranks it at 8.09.
• The Claude Fable 5 page headlines 8.10 but its notes also mention an 8.14 average and a 7.72 bare one-shot figure.

When in doubt, use the headline card on the all models page and note the date you read it.

What scores can't tell you about the Bot

No GoldieBench task checks how well a model reproduces a staging bug or reads a pull request.

For that, lean on the Bot's controls: terminal commands show an Allow or Skip card, and the approvals docs recommend explicit limits for production changes and for deleting or overwriting data.

Remember too that the Bots on one account work side by side on a single shared machine, one screen apiece, so any credential stored there is open to every one of them.

The Cursor side of this stack is compared in our Grok Bot vs Cursor model guide.

The model trainings and the Agent OS are inside the AI Profit Boardroom, and my team will happily talk your setup through on a free call.

Also On Our Network

• Agent Operatorsthe seven-step build for a code-triage Bot that ends at a pull request
• AI Income Deskwhether a small firm without developers gets value from a coding Bot
• AI Tool Verdictan eight-criteria scorecard of the Bot as a coding teammate
• agentos.guidefive copy-paste coding prompts and a weekly review rhythm
• aiprofitboardroom.comthe complete Grok Bot for Coding guide on the AI Profit Boardroom blog

Pick your planner and reviewer from the dated board, keep a person on every merge, and join the AI Profit Boardroom to run Grok Bot for coding.