whatmodel.sh / Yi-Vision

Yi-Vision

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Yi-Vision is a closed-weights language model from 01.AI.

Catalogue identity notes

Comparison-only. Arena entry 'yi-vision' (vision). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'yi-vision' as 'Yi-Vision' (01 AI, Proprietary), linking https://platform.01.ai/docs#models-and-pricing. LMArena labels it 'Yi-Vision' linking platform.01.ai/docs; 01.AI's current China platform lists yi-vision-v2 instead, so no current API is asserted.

42.6±3.9comparison 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 Yi-Vision, out of 1132 with a score, then more from 01.AI.

#modelscorewhereRAMreleased
810Llama 3.2 11B Vision Instruct Meta43±2local~8.8GB2024-09
811Qwen2.5 3B Instruct Alibaba43±1local~3.3GB2024-09
812Yi-Vision 01.AI43±4comparison only
813DeepSeek-Coder 1.3B Instruct DeepSeek43±1local~2.4GB2023-10
814Aya 23 8B Cohere For AI43±3local~6.2GB2024-05
more from 01.AI
404Yi-Lightning 01.AI55±1api price unknown2024-10
506Yi-Large Preview 01.AI52±1comparison only2024-05
547Yi-Large 01.AI51±1comparison only2024-05

see all LLMs · find the ones that fit your machine

$ what it's good at

vision & documents▁▁▃▃▂▃▅▅▇▇█▃ #182 of 199 * · 93% of its score · 1 test

Beats LLaVA-OneVision Qwen2 72B (ov-sft), Gemma 3 4B, CogVLM2 Llama3 Chat 19B here, though they rank higher overall. Trails Qwen3.5 0.8B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena vision (style controlled)1002.4 ✎▁▁▂▃▂▂▄▅▅▆██ beats 12/145avg 1068▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%blunt

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · LMArena leaderboard dataset 93%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%CC BY 4.0
prior · pull toward the middle▇▇ 7%
weighted average= 42.6

$ 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