GoldieBench Blog · 6 min read
Grok Bot Email + Which AI Models To Pair With Your Bot's Inbox, With Real GoldieBench Scores
Grok Bot has no model picker, so here is which benchmarked models to use for routine briefs, digest templates and long mail reviews, with every score linked.

Grok Bot email runs on whatever model Grok Bot picks, so the GoldieBench scores that matter are for the models you use around the inbox: to write routine briefs, draft reply templates and review long mail exports.
The Grok Bot FAQ is direct about it: Grok Bot picks the model for each task, and there is no model picker.
So the useful question is not which model reads your Bot's mail, but which model you pair with that inbox.
Every score below is quoted from a live GoldieBench page I read on 1 November 2026, with the page linked.
If you want my team to help plan the setup, book a free SEO strategy session.
The Bot inbox in one paragraph
Each account claims one address at mail.grokbot.com, and per the email docs your Bots can receive, read, search, send, draft and forward from it.
Plus aliases such as name+receipts@ land in the same inbox, and a routine can fire when mail arrives from anyone, from set addresses or from a whole domain.
Routine mail waits as a draft until you press Send.
The changelog dates the address to v0.70.2 on 11 October 2026 and forwarding to the v0.75.5 release on 23 October.
What GoldieBench measures, and what it does not
The methodology page sets out the method: one fixed prompt, one shot, one self-contained HTML file, scored 0 to 10.
The Grok deep dive lists what the bench does not measure: multi-turn agent work, long-context recall and API latency.
Reading an inbox all day is multi-turn agent work.
So treat these scores as a signal of how well a model turns one clear instruction into a finished output.
That maps onto writing a routine brief or a digest template, not onto running the inbox itself.
The scores worth knowing for email work
| Model | GoldieBench average | Board rank | Pages category | Page read |
|---|---|---|---|---|
| Fusion | 8.59 /10, 47 scored tasks | #1 of 26 | not quoted | /benchmarks/fusion |
| Hermes MoA | 8.17 /10, 47 scored tasks | #3 of 26 | 8.0 (field 7.7) | /benchmarks/moa |
| Grok | 8.09 /10, 43 scored tasks | #8 of 26 | 8.3 (field 7.7) | /benchmarks/grok |
| Kimi K3 | 7.89 /10, 50 scored tasks | #11 of 26 | 7.9 (field 7.7) | /benchmarks/kimik3 |
The models index also lists Claude Opus 5 at 8.27 and GPT-5.6 Sol at 8.16.
The Pages category covers three tasks on each of these pages, so read it as a small sample.
The index card for Grok shows 47 tasks while its benchmark page counts 43 scored tasks; I quote the 43 with the 8.09 from that page.
Grok: the strongest single-model pick for written output
Grok averages 8.09 across 43 scored tasks, with 5 golds, 1 silver and 1 bronze.
Its category rows read Games 8.3, Pages 8.3, Sims 8.0 and Visuals 7.7.
Its landing page build scored 9.0 and took the task gold, and the judge called it a genuinely premium keynote page built from one sentence.
Its benchmark page lists a context of 256,000 tokens.
If you want one model outside Grok Bot to draft the digest layout or reply templates your Bot will follow, that Pages row is the relevant number.
Hermes MoA and Fusion: panels top the board
Fusion leads the whole board at 8.59, with 21 golds out of 47 scored tasks.
Hermes MoA averages 8.17 and ranks third, with Pages and Games at 8.0 and Sims and Visuals at 8.4.
Both are multi-model panels rather than single models, according to their cards on the models index.
The Grok page shows Grok trailing Fusion 2-31 with 10 ties, and trailing Hermes MoA 14-27 with 2 ties.
For a routine brief that has to be right the first time, a panel is the safer writer.
Grok 4.7: why the newer name is not the better pick here
Grok 4.7 averages 7.15 across 20 scored tasks and ranks #18 of 26 on its benchmark page.
All 20 of those tasks are games, and its Pages row shows 0 tasks.
That leaves no GoldieBench evidence for written page or template work from Grok 4.7.
For email templates, the Grok record with a Pages score is the one to lean on.
Kimi K3 for big mail exports
Kimi K3 scores 7.89 over 50 scored tasks and ranks 11th.
Its benchmark page lists a context of 1,048,576 tokens.
Long-context recall is outside what the one-shot bench measures, so that figure is a spec, not a result.
Where it helps is reading a long digest history or a pile of forwarded reports in one pass while you tune filters.
On head-to-head records, the Grok page shows Grok trailing Kimi K3 18-25.
The pairing I would run
| Email job | Pair with | Score behind it |
|---|---|---|
| Writing the routine brief and sender filters | Fusion or Hermes MoA | 8.59 and 8.17 averages |
| Designing the morning digest and reply templates | Grok | Pages 8.3, landing page 9.0 |
| Reviewing a long export of forwarded mail | Kimi K3 | 7.89 average, 1,048,576-token context |
| Reading and sorting the inbox itself | Grok Bot's own choice | No model picker |
The last row is fixed by Grok Bot, and that is fine: the safety rules sit in the product, not the model.
Treat the other three rows as a starting point and swap a model out if its drafts need heavy editing.
A brief that needs three rewrites costs more of your time than one that lands first go, whatever the leaderboard says.
Keep the winning brief as a saved skill, so the Bot reuses the method instead of the model you wrote it with.
Recheck the board each month, because the pages show a data refresh date and the ranks move as new models land.
The rules no model can change
Grok Bot treats what other people write as information, never as orders.
An instruction typed into an incoming message is never read as your sign-off.
Messages caught by the spam or virus check are stored stripped of text and files, and they cannot trigger anything.
Each email tops out at 50 recipients and ten attachments, capped at 10 MB apiece and 25 MB overall.
Those limits hold whichever model Grok Bot routes the task to.
A strong standing rule from the Bots docs is "Always ask before you send an email."
Wire it into a routine
The routines guide lists an email to your Bot's own address as an event trigger, next to Slack, webhooks and GitHub.
Routines cannot filter by subject, so write the brief around aliases and sender domains.
My Grok Bot routines model pairing guide covers schedules and the hourly caps.
Grok Bot is part of the Premium, Super and Ultra plans and not Plus, per the X subscription page.
For the Agent OS, coaching and model comparisons, join the AI Profit Boardroom.
Every build on this bench came out of the Agent OS members install, so get the same stack if you want to run the pairings yourself.
Also On Our Network
- Agent Operatorsthe six-step setup from approval card to a sender-filtered email routine
- AI Income Deskthree small business use cases and the five risks to check first
- AI Tool Verdicta nine-criteria scorecard that rates the Bot inbox 7.5 out of 10
- agentos.guidefive Agent OS prompts that name, alias, brief and review the Bot inbox
Let Grok Bot run the inbox, pair a panel model for briefs and Grok for templates, and if you want a plan for your workflows, book a free SEO strategy session to map your Grok Bot email setup.
FAQ
Can I choose the model Grok Bot uses for email?
No. Grok Bot picks the model for each task and has no model picker, so pairing happens in the tools around it.
Which model scores best for writing digest templates?
Among the pages read, Grok has the highest Pages row at 8.3, and its landing page build scored 9.0 with the task gold.
Does GoldieBench test inbox or agent work?
No. It scores one-shot single-file builds and does not measure multi-turn agent work, long-context recall or latency.
Why pair Kimi K3 with Grok Bot email?
Its benchmark page lists a 1,048,576-token context, which suits reading long mail exports, though the bench itself does not score recall.


