whatmodel.sh / ERNIE 5.0 Preview (1022)

ERNIE 5.0 Preview (1022)

+ contribute
Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

ERNIE 5.0 Preview (1022) is a closed-weights language model from Baidu, released November 2025.

Catalogue identity notes

Comparison-only. Arena entry 'ernie-5.0-preview-1022' (text). Released 2025-11: Baidu ERNIE blog naming ERNIE-5.0-Preview-1022; preview public from 2025-11-13. Dated ERNIE 5.0 preview snapshot publicly named by Baidu; no Qianfan id carries the 1022 suffix.

67.2±3.3comparison 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 ERNIE 5.0 Preview (1022), out of 1132 with a score, then more from Baidu.

#modelscorewhereRAMreleased
142Gemini 2.5 Pro Google67±1api $3.44/M2025-06
143Claude Opus 4 Anthropic67±1comparison only2025-05
144ERNIE 5.0 Preview (1022) Baidu67±3comparison only2025-11
145ERNIE 5.0 Preview (1220) Baidu67±4comparison only2026-01
146K-EXAONE 2.0 (750B-A37B) LG AI Research67±1local~791GB2026-07
more from Baidu
68ERNIE 5.1 Baidu73±3comparison only2026-05
93ERNIE 5.0 Preview (1203) Baidu71±3comparison only2025-12
129ERNIE 5.0 (0110) Baidu68±3comparison only2026-01

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▅▆█▆▆▆▂ #112 of 579 * · 95% of its score · 1 test

Beats LongCat 2.0 (1.6T-A48B), Inkling Small (266B-A12B), Qwen3.5 122B-A10B here, though they rank higher overall. Trails Gemma 4 12B, Gemma 4 26B-A4B, Mistral Medium 3.5 (128B) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1418.7 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 270/379avg 1268▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 95%blunt

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · LMArena leaderboard dataset 95%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 95%CC BY 4.0
prior · pull toward the middle▇ 5%
weighted average= 67.2

$ 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