[whatmodel.sh](../../README.md) / Yi-Large Preview # Yi-Large Preview 01.AI · released 2024-05 · proprietary **score 53.5** ±0.8 · 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 Preview among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
coding          ▁▁▁▃▄▆●▇▅▄▃▁   #313 of 539*     17%      1
chat & writing  ▁▁▁▃▅▄▇●▇▇▇▃   #249 of 474*     31%      1
math            ▁▁▃●██▅█▃▁▁▁   #434 of 530*    5.8%      1
coding · #313 of 539 · 17% 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)   60.4%  ▁▂▆▃●█▆▃▂·▁▁ beats 21/47  medium · avg 57%     17%  sharp · at its level
chat & writing · #249 of 474 · 31% of its score Beats [Gemma 2 9B IT SimPO](gemma-2-9b-simpo.md), [Granite 4.2 8B](granite-4-2-8b.md), [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [Granite 4.0 H Micro 3B](granite-4-0-h-micro.md), [Gemma 3n E4B](gemma-3n-e4b.md) here, though they rank lower overall. 
test                            result  vs the others in its run    difficulty        counts  why
AlpacaEval 2 length-controlled   51.9%  ▁▂▂▃▆▆█▆▄●▁▁ beats 109/122  medium · avg 35%     31%  sharp · at its level
math · #434 of 530 · 5.8% of its score Trails [OPT-2.7B](opt-2-7b.md), [Gemma 2B (base)](gemma-2b-base.md), [Stable Code 3B](stable-code-3b.md) here, though they rank lower overall. 
test                         result  vs the others in its run  difficulty           counts  why
GSM8K (ZeroEval, zero-shot)   82.6%  ▁▄·▁▄█●▅▅███ beats 14/43  very easy · avg 87%    5.8%  sharp
other tests (3) 
test                                          result  vs the others in its run  difficulty           counts  why
MMLU-Redux (ZeroEval, 2,778 quest…             82.2%  ▁·▁▁▄▄▄▅●▄▂▁ beats 36/53  easy · avg 75%          26%  at its level
ZebraLogic (ZeroEval, 1,000 puzzl…             18.9%  ▁▁▃▆●█▁▁·▁▁▁ beats 27/57  hard · avg 15%          12%  sharp
WildBench v2 WB-Score (task macro)  55.2946 WB-Score  ▁·▁▁·▃█▄▇▆▅● beats 48/52  avg 43.019 WB-Score    7.5%  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   61% ▇▇▇▇▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards        1   31% ▇▇▇▇
WildBench v2 leaderboard data              1    7% ▇
prior: pull toward the middle                   0% ▇
weighted average                              = 53.5
Licences: WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · 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-preview) · [JSON](https://whatmodel.sh/api?format=json&all=1)