[whatmodel.sh](../../README.md) / Grok 4.7 # Grok 4.7 SpaceXAI (xAI) · released 2026-09 · closed weights **score 73.1** ±0.7 · #50 of 732 · from 13 benchmark results across 3 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Grok 4.7 among every model on that task or test, worst to best. 
task                vs the others         rank  counts  tests
agent tasks         ▁▁▃▆█▄▅▅▄●▂▁   #23 of 313*     14%      2
coding              ▁▁▁▃▅▇█▇▅▄●▁   #19 of 469*    9.0%      1
chat & writing      ▁▁▁▃▆▄▆█▆▇●▃   #65 of 402*    5.0%      1
math                ▁▁▃▅█▆▄█●▂▁▁    #53 of 431     43%      3
vision & documents  ▁▁▂▂▁▃▄▂▆●█▄   #47 of 153*    3.4%      1
agent tasks · #23 of 313 · 14% of its score Beats [GPT-5.5](gpt-5-5.md), [Hy4 preview (770B-A49B)](hy4-preview.md), [GPT-5.6 Terra](gpt-5-6-terra.md) here, though they rank higher overall. 
test                                    result  vs the others in its run  difficulty            counts  why
Arena Agent (October 2026 aggrega…  0.0239 IPS  ▁▁▁▃▃▂●▁▂▂▁▁ beats 37/59  avg -0.379 IPS           13%  sharp
KlinikeBench strict pass@1 (333 i…       22.2%  ▁▁·▁▁▁▁▅▃█●█ beats 21/27  very hard · avg 2.4%    1.3%  typical
coding · #19 of 469 · 9.0% of its score Beats [GPT-5.6 Sol](gpt-5-6-sol.md), [Claude Fable 5](claude-fable-5.md), [GPT-5.5](gpt-5-5.md) here, though they rank higher overall. 
test                       result  vs the others in its run    difficulty  counts  why
Arena WebDev (Code Arena)  1637.5  ▁▁▃▂▅█▃▆▅●▁▂ beats 111/129  avg 981       9.0%  sharp
chat & writing · #65 of 402 · 5.0% of its score Beats [Claude Haiku 5.5](claude-haiku-5-5.md) here, though they rank higher overall. Trails [Nemotron Cascade 2 30B-A3B](nemotron-cascade-2-30b-a3b.md), [Gemma 4 31B](gemma-4-31b.md), [Gemini 2.5 Pro](gemini-2-5-pro.md) here, though they rank lower overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1441.2  ▁▁▁▂▃▃▃▆▆▇●▃ beats 304/385  avg 1260      5.0%  blunt
math · #53 of 431 · 43% of its score Trails [A.X K2 (688B-A33B)](ax-k2.md), [DeepSeek V4 Flash (Preview, Apr 2026)](deepseek-v4-flash-preview.md), [GPT-5](gpt-5.md) here, though they rank lower overall. 
test                       result  vs the others in its run    difficulty            counts  why
FrontierMath tiers 1–3 v2   53.0%  ▂··▁▁▃▄▄●▆▄▃ beats 37/86    very hard · avg 1.2%     23%  sharp · at its level
FrontierMath tier 4 v2      17.1%  ▁▂▁▂●▅▂▃▂▁▁▁ beats 16/67    very hard · avg 0.1%     19%  sharp · at its level
OTIS mock AIME 2024–2025    98.1%  ▃▃▄▃▃▃▇▇█▃●▅ beats 173/197  very hard · avg 7.2%    0.9%  sharp · maxed out · noisy
vision & documents · #47 of 153 · 3.4% of its score Beats [MiMo-V2.6-Pro](mimo-v2-6-pro.md), [MiMo-V2.6-Flash (309B-A15B)](mimo-v2-6-flash.md), [GPT-6 Luna](gpt-6-luna.md) here, though they rank higher overall. Trails [Qwen3.6 Flash](qwen3-6-flash.md), [Gemini 3.5 Flash-Lite](gemini-3-5-flash-lite.md), [Kimi K2.5](kimi-k2-5.md) here, though they rank lower overall. 
test                             result  vs the others in its run    difficulty  counts  why
Arena vision (style controlled)  1249.8  ▁▁▂▂▂▂▃▄▅▅●▇ beats 105/152  avg 1061      3.4%  blunt
other tests (5) 
test                          result  vs the others in its run    difficulty            counts  why
Furniture Assembly (Epoch)     20.8%  ●▆▆▆▆▂▆▂▂▂▂▅ beats 1/32     very hard · avg 1.2%    8.0%  sharp · at its level
Mystery Game Puzzles (Epoch)   29.0%  █··▂▃▇▄●▂▄▂▁ beats 46/77    very hard · avg 9.4%    6.9%  sharp · noisy
Chess Puzzles (Epoch)          38.0%  █▁▁▁▁▂▃▃●▂▁▁ beats 115/137  very hard · avg 5.8%    4.3%  sharp · at its level · noisy
GPQA Diamond                   92.7%  ▁▁▁▁▂▄▆▄▅▇█● beats 192/211  hard · avg 39%          3.9%  sharp
SimpleQA Verified              56.0%  ▁▁▃▂▃▃▆▆█●▂▅ beats 62/81    very hard · avg 8.0%    2.0%  at its level
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
Epoch AI                                        8   68% ▇▇▇▇▇▇▇▇
LMArena leaderboard dataset                     4   30% ▇▇▇▇
KlinikeBench: Evaluating Language Model…        1    1% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 73.1
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · KlinikeBench: Evaluating Language Models Beyond Diagnostic Accuracy ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) ## How to run it ### Through an API api (xAI) · $3.00/M blended · $2 in / $6 out per 1M tokens · [docs.x.ai](https://docs.x.ai/docs/models) [price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/xai/models/grok-4.7.toml): Standard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/xai/grok-4.7.toml) [interactive version](https://whatmodel.sh/model/grok-4-7) · [JSON](https://whatmodel.sh/api?format=json&all=1)