whatmodel.sh / Grok 2 (2024-08-13)
Grok 2 (2024-08-13)
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Grok 2 (2024-08-13) is a closed-weights language model from SpaceXAI (xAI), released August 2024.
Catalogue identity notes
Comparison-only. Arena entry 'grok-2-2024-08-13' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'grok-2-2024-08-13' as 'Grok-2-08-13' (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. xAI later published open weights (xai-org/grok-2, 'a model trained and used at xAI in 2024') without tying them to this dated snapshot, so no run option is given.
$ how it compares
The LLMs ranked either side of Grok 2 (2024-08-13), out of 1132 with a score, then more from SpaceXAI (xAI).
| # | model | score | where | ||
|---|---|---|---|---|---|
| 392 | 56±1 | api $0.20/M +local | |||
| 393 | 56±1 | api $0.10/M +local | |||
| 394 | 56±3 | comparison only | |||
| 395 | 56±3 | comparison only | |||
| 396 | 56±3 | comparison only | |||
| more from SpaceXAI (xAI) | |||||
| 30 | 77±1 | api $3.00/M | |||
| 42 | 75±1 | api $3.00/M | |||
| 47 | 75±1 | api $3.00/M | |||
$ what it's good at
chat & writing▁▁▁▃▅▅▆█▆▆▆▂ #241 of 579 * · 60% of its score · 1 test
Beats Seed-OSS 36B Instruct, Nemotron-Cascade-14B-Thinking, Qwen3-VL 32B Instruct here, though they rank higher overall. Trails LFM2.5 1.2B Instruct, LFM2.5 1.2B Thinking, Gemma 3 12B here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LMArena (text) | 1335.6 ✎ | ▁▁▁▂▃▃▃▆▆▇█▃ beats 173/379 | 60% |
other tests1 not in a task · 37% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LiveBench 2024-08-31 | 46.6% ✎ | ▄█·▄·▄▄·▄··▄ beats 2/7 | 37% |
$ 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
| source | results | share of the score |
|---|---|---|
| 1 | 60% | |
| 1 | 37% | |
| prior · pull toward the middle | 3% | |
| weighted average | = 55.7 |
$ improve this page
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