Grok 4.6

Made by xAI, released 12 Aug 2026. Epoch AI lists its access as “API access”. No Top tier or Strong results yet among the models listed here.

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In short

What it does well

No Top tier or Strong ratings on the four main tests yet.

Where it falls short

  • Coding: Middle (56th of 77 listed models)
  • Knowledge: Middle (23rd of 43 listed models)
  • Maths: Middle (24th of 43 listed models)
  • Writing: Middle (52nd of 77 listed models)

What we don't know yet

  • Fixing real software bugs: Not enough data

The four ratings

  • Top tier
  • Strong
  • Middle
  • Behind the leaders
  • Not enough data

Ratings compare only the 77 models listed here, in quarters, best first. “Not enough data” means no result yet. It is never a low score.

Writing
MiddleHow often people preferred its answers to writing prompts. 1,426 on Arena's scale · Source for Grok 4.6 writing score (lmarena)
Coding
MiddleHow often people preferred its answers to coding questions. 1,464 on Arena's scale · Source for Grok 4.6 coding score (lmarena)
Maths
MiddleHow many very hard maths problems it solved. 66.0% · Source for Grok 4.6 maths score (epoch)
Knowledge
MiddleHow many short factual questions it answered correctly. 48.9% · Source for Grok 4.6 knowledge score (epoch)

Evidence completeness: Broad evidence, 12 of 13 tests have a result.

Where it is relatively stronger or weaker

This compares each kind of request with the model's own overall position among the 77 listed models, using Arena's votes. It is about performance on different languages, kinds of request and topics, not about political or social bias.

Relatively stronger

Relatively weaker

No category stands out as clearly weaker than its overall position.

Show every category we can see
Position in each Arena category
CategoryPositionArena scoreVotesSource
Expert-level prompts
Kind of request
49th of 771,466
95% range 1,456 to 1,476
3,835Source for Expert-level prompts
Hard prompts
Kind of request
64th of 771,447
95% range 1,442 to 1,453
21,843Source for Hard prompts
Long prompts
Kind of request
46th of 771,456
95% range 1,450 to 1,462
15,506Source for Long prompts
Longer conversations
Kind of request
69th of 771,425
95% range 1,416 to 1,434
5,117Source for Longer conversations
Chinese
Language
64th of 761,473
95% range 1,461 to 1,484
2,761Source for Chinese
English prompts
Language
68th of 771,434
95% range 1,428 to 1,440
13,532Source for English prompts
French
Language
58th of 721,452
95% range 1,432 to 1,471
1,047Source for French
German
Language
50th of 551,416
95% range 1,390 to 1,441
578Source for German
Japanese
Language
48th of 541,380
95% range 1,354 to 1,406
611Source for Japanese
Korean
Language
48th of 541,391
95% range 1,366 to 1,415
637Source for Korean
Non-English prompts
Language
67th of 771,419
95% range 1,413 to 1,424
20,289Source for Non-English prompts
Russian
Language
67th of 771,419
95% range 1,409 to 1,428
4,317Source for Russian
Spanish
Language
64th of 701,416
95% range 1,397 to 1,435
989Source for Spanish
Entertainment, sport and media
Topic
52nd of 771,420
95% range 1,413 to 1,428
8,833Source for Entertainment, sport and media
Law and government
Topic
67th of 761,428
95% range 1,416 to 1,440
2,863Source for Law and government
Medicine and health
Topic
65th of 751,421
95% range 1,408 to 1,434
2,349Source for Medicine and health
Science
Topic
68th of 771,437
95% range 1,428 to 1,445
5,589Source for Science
Software and IT
Topic
65th of 771,455
95% range 1,449 to 1,461
12,579Source for Software and IT

Bias: what we can and can't see

Our sources do not measure political lean, stereotyping, flattery (agreeing with you too readily) or safety behaviour, so we cannot say how any model does on those. What we can show is above: where a model is relatively stronger or weaker across languages, kinds of request and topics. Also, people voting in Arena can favour answer style, such as length and formatting, and our numbers do not adjust for that. See what AI bias means and what these tests can tell you.

Best-supported use cases

This model is not among the options we recommend for any of the five tasks. That reflects the rules and the models listed, not a verdict on the model.

Where an alternative might be more suitable

What it is

Maker
xAI
Release date (Epoch AI)
12 Aug 2026
Access, as listed by Epoch AI
API access
Licence, as listed by Arena
Proprietary
Maker's country (Epoch AI)
United States of America
Last observation in our data
28 Oct 2026
Data last changed
29 Oct 2026, 14:36 UTC

We have not verified an official product page for this model, so we do not link to one.

How you can use it

Proprietary. The model's files are not published, so it is generally available only through its maker or partners the maker chooses.

How you can reach it (an app, a website, a developer API or a third-party tool) and what that costs are not covered by our benchmark data. Where we could verify prices from the maker's own pages, they are shown under Price. Epoch AI lists access as “API access”. See models, apps and harnesses and how AI pricing works.

Price

Price not independently verified

See Grok (checked 29 Oct 2026). Page opened and read. It says Grok is 'free to try on the web and in the apps' and that SuperGrok adds higher limits. It states no prices or model names. The separate plans page opens as a sign-up pop-up showing introductory offers and no model names, so we record nothing else.

We never infer subscription prices from API prices. New to “tokens” and “per million”? See how AI pricing works.

Good to remember

  • Benchmarks are samples of tasks. None proves performance in every situation.
  • Arena scores come from people choosing between answers. That measures preference, not correctness.
  • Ratings are relative to the 77 models listed here. A different set would change them.
  • Sources test models at different settings. Each result shows the setting used, and we do not adjust it.

The evidence, for the curious

Show all 13 tests with settings and sources
All published results for Grok 4.6. Ratings compare only the 77 models on this site.
TestResultRatingSetting usedSource identifierObservedLinks
Arena text: overall preference
Arena 'text' leaderboard (Bradley-Terry rating, no style control)
1,429
Arena score
95% range 1,424 to 1,433, 33,824 votes
Behind the leaders effort: High, bradley-terry rating Grok 4.6 (High)
lmarena · text/latest
28 Oct 2026 Source for Grok 4.6 Arena text: overall preference score (lmarena)
Arena text: creative writing
Arena 'text' leaderboard, category creative_writing
1,426
Arena score
95% range 1,418 to 1,434, 7,492 votes
Middle effort: High, bradley-terry rating Grok 4.6 (High)
lmarena · text/latest
28 Oct 2026 Source for Grok 4.6 Arena text: creative writing score (lmarena)
Arena text: coding
Arena 'text' leaderboard, category coding
1,464
Arena score
95% range 1,457 to 1,472, 8,394 votes
Middle effort: High, bradley-terry rating Grok 4.6 (High)
lmarena · text/latest
28 Oct 2026 Source for Grok 4.6 Arena text: coding score (lmarena)
Arena text: maths
Arena 'text' leaderboard, category math
1,415
Arena score
95% range 1,400 to 1,430, 1,672 votes
Behind the leaders effort: High, bradley-terry rating Grok 4.6 (High)
lmarena · text/latest
28 Oct 2026 Source for Grok 4.6 Arena text: maths score (lmarena)
Arena text: following instructions
Arena 'text' leaderboard, category instruction_following
1,431
Arena score
95% range 1,424 to 1,437, 11,988 votes
Middle effort: High, bradley-terry rating Grok 4.6 (High)
lmarena · text/latest
28 Oct 2026 Source for Grok 4.6 Arena text: following instructions score (lmarena)
Arena text: business and management
Arena 'text' leaderboard, category industry_business_and_management_and_financial_operations
1,416
Arena score
95% range 1,408 to 1,424, 6,205 votes
Behind the leaders effort: High, bradley-terry rating Grok 4.6 (High)
lmarena · text/latest
28 Oct 2026 Source for Grok 4.6 Arena text: business and management score (lmarena)
Arena WebDev: building web apps
Arena 'webdev' leaderboard (Code Arena), category overall
1,616
Arena score
95% range 1,609 to 1,623, 11,966 votes
Strong effort: High, bradley-terry rating Grok 4.6 (High)
lmarena · webdev/latest
28 Oct 2026 Source for Grok 4.6 Arena WebDev: building web apps score (lmarena)
FrontierMath Tiers 1-3 (v2)
FrontierMath Tiers 1-3 v2 (private set), benchmark released 2026-06-12
66.0%
share of problems solved
standard error ±2.8 points
Middle effort: xhigh, best score across scorers grok-4.6_xhigh
epoch · frontiermath_tiers_1_3_v2.csv
14 Aug 2026 Source for Grok 4.6 FrontierMath Tiers 1-3 (v2) score (epoch) · Run details for FrontierMath Tiers 1-3 (v2)
OTIS Mock AIME 2024-2025
OTIS Mock AIME 2024-2025, benchmark released 2024-12-19
99.2%
share of problems solved
standard error ±0.5 points
Top tier effort: xhigh, best score across scorers grok-4.6_xhigh
epoch · otis_mock_aime_2024_2025.csv
14 Aug 2026 Source for Grok 4.6 OTIS Mock AIME 2024-2025 score (epoch)
No public run log in the data file.
GPQA Diamond
GPQA Diamond, benchmark released 2023-11-20
93.2%
share of questions correct
standard error ±1.5 points
Strong effort: xhigh, best score across scorers grok-4.6_xhigh
epoch · gpqa_diamond.csv
14 Aug 2026 Source for Grok 4.6 GPQA Diamond score (epoch)
No public run log in the data file.
SimpleQA Verified
SimpleQA Verified, benchmark released 2025-09-09
48.9%
share of questions correct
standard error ±1.6 points
Middle effort: xhigh, best score across scorers grok-4.6_xhigh
epoch · simpleqa_verified.csv
27 Aug 2026 Source for Grok 4.6 SimpleQA Verified score (epoch) · Run details for SimpleQA Verified
SWE-bench Verified (Epoch runs)Not enough data
FrontierCode (reported by Cognition)
FrontierCode, benchmark released 2026-06-08, aggregation Mean@5
48.0%
mean score over 5 attempts
no uncertainty figure published
Not rated (results use different tools) effort: high, tool: grok-build, mean@5 grok-4.6_unknown
epoch · frontiercode_external.csv
not provided Source for Grok 4.6 FrontierCode (reported by Cognition) score (epoch) · Run details for FrontierCode (reported by Cognition)

Missing evidence

Not enough data for these tests:

  • SWE-bench Verified (Epoch runs)

Missing results are not low scores and do not count against the model.

Where a source gives no public run log, we cannot show the run behind the score. See data sources.

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