[whatmodel.sh](../../README.md) / Yi-Large # Yi-Large 01.AI · released 2024-05 · proprietary **score 52.1** ±0.9 · comparison only: no current run option verified · from 6 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 Yi-Large among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
coding          ▁▁▁▃▄▆●▇▅▄▃▁   #315 of 539*     20%      1
chat & writing  ▁▁▁▃▅▄▇●▇▇▇▃   #272 of 474*     20%      1
math            ▁▁▃●██▅█▃▁▁▁   #450 of 530*    7.7%      1
coding · #315 of 539 · 20% of its score Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [QwQ-32B-Preview](qwq-32b-preview.md), [InternLM2.5 20B Chat](internlm2-5-20b-chat.md) here, though they rank lower overall. 
test                               result  vs the others in its run  difficulty        counts  why
CRUXEval (ZeroEval, 800 problems)   60.3%  ▁▂▆▃●█▆▃▂·▁▁ beats 20/47  medium · avg 57%     20%  sharp · at its level
chat & writing · #272 of 474 · 20% of its score Beats [Granite 4.2 8B](granite-4-2-8b.md), [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Olmo 3.1 32B Think](olmo-3-1-32b-think.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [Gemma 3 4B](gemma-3-4b.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty        counts  why
Arena-Hard (v0.1, GPT-4-1106 judg…   63.7%  ▂▁▁▅█▅▄▃●▅▃▁ beats 43/60  medium · avg 47%     20%  sharp · at its level
math · #450 of 530 · 7.7% of its score Trails [OPT-1.3B](opt-1-3b.md), [OPT-2.7B](opt-2-7b.md), [Gemma 2B (base)](gemma-2b-base.md) here, though they rank lower overall. 
test                         result  vs the others in its run  difficulty           counts  why
GSM8K (ZeroEval, zero-shot)   80.1%  ▁▄·▁▄●▅▅▅███ beats 9/43   very easy · avg 87%    7.7%  sharp
other tests (3) 
test                                          result  vs the others in its run  difficulty           counts  why
MMLU-Redux (ZeroEval, 2,778 quest…             81.2%  ▁·▁▁▄▄▄▅●▄▂▁ beats 31/53  easy · avg 75%          32%  at its level
ZebraLogic (ZeroEval, 1,000 puzzl…             18.8%  ▁▁▃▆●█▁▁·▁▁▁ beats 26/57  hard · avg 15%          12%  sharp
WildBench v2 WB-Score (task macro)  48.9273 WB-Score  ▁·▁▁·▃█▄▇▆●▆ beats 40/52  avg 43.019 WB-Score    8.4%  sharp
## 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
WildEval ZeroEval published results        4   72% ▇▇▇▇▇▇▇▇▇
Arena-Hard-Auto v0.1 leaderboard           1   20% ▇▇
WildBench v2 leaderboard data              1    8% ▇
prior: pull toward the middle                   0% ▇
weighted average                              = 52.1
Licences: WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/yi-large) · [JSON](https://whatmodel.sh/api?format=json&all=1)