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ERNIE 5.1

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ERNIE 5.1 is a closed-weights language model from Baidu, released May 2026.

Catalogue identity notes

Comparison-only. Arena entry 'ernie-5.1' (baidu, Proprietary), first listed 2026-05-06 in text_style_control. Release date not taken from an approved source.

Model information sources

Release date ↗

72.8±2.8comparison only: no current run option verifiedfrom 2 benchmark results across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of ERNIE 5.1, out of 1132 with a score, then more from Baidu.

#modelscorewhereRAMreleased
66Seed2.0 Lite ByteDance Seed73±4comparison only
67Kimi K2.6 Moonshot AI73±1api $1.71/M +local~317GB2026-04
68ERNIE 5.1 Baidu73±3comparison only2026-05
69GPT-5 Pro OpenAI73±1api $41.25/M2025-10
70LongCat 2.0 (1.6T-A48B) Meituan73±2api $1.30/M +local~2155GB2026-06
more from Baidu
93ERNIE 5.0 Preview (1203) Baidu71±3comparison only2025-12
129ERNIE 5.0 (0110) Baidu68±3comparison only2026-01
144ERNIE 5.0 Preview (1022) Baidu67±3comparison only2025-11

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▅▆█▆▆▆▂ #41 of 579 * · 69% of its score · 1 test

Beats GPT-6 Sol, GPT-5.6 Terra, MiMo-V2.6-Flash (309B-A15B) here, though they rank higher overall. Trails MiMo-V2.5-Pro (1T-A42B), Grok 4.20 (0309 reasoning), Claude Opus 4.5 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1467.6 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 340/379avg 1268▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 69%blunt
web search▂▁▂▇▅█▃▄▁▂▁▁ #40 of 96 * · 28% of its score · 1 test

Beats Gemini 3.1 Pro (Preview), GPT-5.3-Codex, Qwen3.7 Max here, though they rank higher overall. Trails GPT-4o mini (2024-07-18), Mistral Small 3.1 24B (2503), Llama 4 Scout (109B-A17B) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena search systems (style controlled)1193.4 ✎▁·▁▁▂▂▂█▅▂▁▁ beats 16/30avg 1055▇▇▇▇▇▇▇▇▇▇▇ 28%blunt

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · LMArena leaderboard dataset 97%

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 dataset2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 97%CC BY 4.0
prior · pull toward the middle▇ 3%
weighted average= 72.8

$ 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