whatmodel.sh / Grok Code Fast 1

Grok Code Fast 1

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Grok Code Fast 1 is a closed-weights language model from SpaceXAI (xAI), released August 2025.

Catalogue identity notes

Comparison-only. Arena entry 'grok-code-fast-1' (webdev). Released 2025-08: xAI 'Grok Code Fast 1' post dated Aug 28, 2025. xAI retired grok-code-fast-1 on May 15, 2026; the slug is now an alias of Grok Build 0.1 (grok-build-0.1), a different model, so this entry is not mapped to grok-build-0-1.

58.2±2.5comparison only: no current run option verifiedfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Grok Code Fast 1, out of 1132 with a score, then more from SpaceXAI (xAI).

#modelscorewhereRAMreleased
328Arch-Agent-32B Katanemo58±5local~21GB2025-06
329Olmo 3.1 32B Think Ai258±1local~21GB2025-12
330Grok Code Fast 1 SpaceXAI (xAI)58±3comparison only2025-08
331DeepSeek R1 Distill Llama 70B DeepSeek58±1api $0.80/M +local~30GB2025-01
332Jamba 1.6 Large AI21 Labs58±11local~839GB2025-02
more from SpaceXAI (xAI)
30Grok 4.6 SpaceXAI (xAI)77±1api $3.00/M2026-08
42Grok 4.5 SpaceXAI (xAI)75±1api $3.00/M2026-07
47Grok 4.7 SpaceXAI (xAI)75±1api $3.00/M2026-09

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▅▇█▇▅▄▂▁ #243 of 549 * · 97% of its score · 1 test

Beats Qwen3.5 4B, Qwen3-VL 32B Instruct, Falcon-H1R 7B here, though they rank higher overall. Trails QwQ-32B-Preview, Ministral 3 3B Reasoning 2512, DeepSeek-Coder-V2 Instruct (0724) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena WebDev (Code Arena)1167.0 ✎▁▃▄▃▅█▄▇▆▄▁▁ beats 2/119avg 981▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 97%sharp

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · LMArena leaderboard dataset 97%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The 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's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
LMArena leaderboard dataset1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 97%CC BY 4.0
prior · pull toward the middle▇ 3%
weighted average= 58.2

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json