whatmodel.sh / Grok 2 mini (2024-08-13)

Grok 2 mini (2024-08-13)

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Grok 2 mini (2024-08-13) is a closed-weights language model from SpaceXAI (xAI), released August 2024.

Catalogue identity notes

Comparison-only. Arena entry 'grok-2-mini-2024-08-13' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'grok-2-mini-2024-08-13' as 'Grok-2-Mini-08-13' (xAI, Proprietary), linking https://x.ai/blog/grok-2. Released 2024-08: xAI 'Grok-2 Beta Release' post dated Aug 13, 2024. Not on xAI's current model list.

53.6±2.6comparison only: no current run option verifiedfrom 2 benchmark results across 2 sources* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Grok 2 mini (2024-08-13), out of 1132 with a score, then more from SpaceXAI (xAI).

#modelscorewhereRAMreleased
463Claude 3.5 Haiku Anthropic54±1comparison only2024-10
464Llama 3.1 Tulu 3 70B Ai254±1local~43GB2024-11
465Grok 2 mini (2024-08-13) SpaceXAI (xAI)54±3comparison only2024-08
466DeepSeek-V2.5 DeepSeek54±1local~151GB2024-09
467DeepSeek-V2 Chat (0628) DeepSeek54±1local~66GB2024-07
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

chat & writing▁▁▁▃▅▅▆█▆▆▆▂ #299 of 579 * · 60% of its score · 1 test

Beats Granite 4.2 8B, Mistral Small 4 (119B-A6.5B), Gemma 2 9B IT SimPO here, though they rank higher overall. Trails LFM2.5 1.2B Instruct, Granite 4.0 H Micro 3B, Gemma 3n E4B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1308.4 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 138/379avg 1268▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 60%blunt
other tests1 not in a task · 37% of its score
testresultvs the others in its rundifficultycountswhy
LiveBench 2024-08-3145.5% ✎▄█·▄·▄▄·▄··▄ beats 1/7medium · avg 42%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 37%at its level

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · LMArena leaderboard dataset 60%, LiveBench model judgments (HF… 37%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 60%CC BY 4.0
LiveBench model judgments (HF livebench/model_judgment, 2024-10-22 revision) with the LiveBench question datasets1▇▇▇▇▇▇▇▇▇▇▇ 37%Apache-2.0 (stated in the LiveBench datasheet)
prior · pull toward the middle▇ 3%
weighted average= 53.6

$ 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