whatmodel.sh / LongCat-Flash-Chat-2602-Exp

LongCat-Flash-Chat-2602-Exp

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LongCat-Flash-Chat-2602-Exp is a closed-weights language model from Meituan.

Catalogue identity notes

Comparison-only. Arena entry 'longcat-flash-chat-2602-exp' (text). Experimental snapshot that Meituan served under this exact name on the LongCat API platform (named in Meituan's retirement notice: retired May 29, 2026). No weights; separate from the open LongCat-Flash-Chat.

69.2±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 LongCat-Flash-Chat-2602-Exp, out of 1132 with a score, then more from Meituan.

#modelscorewhereRAMreleased
111Ring-2.6-1T inclusionAI (Ant Group)69±2comparison only2026-05
112A.X K2 (688B-A33B) SK Telecom69±1local~1454GB2026-07
113LongCat-Flash-Chat-2602-Exp Meituan69±3comparison only
114o3 OpenAI69±1api $3.50/M2025-04
115MiMo-V2.5 (310B-A15B) Xiaomi69±1api $0.21/M +local~100GB2026-04
more from Meituan
70LongCat 2.0 (1.6T-A48B) Meituan73±2api $1.30/M +local~2155GB2026-06
128LongCat-Flash-Thinking (560B-A27B) Meituan69±1comparison only2025-09
184LongCat-Flash-Chat (560B-A27B) Meituan66±1local~333GB2025-08

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▅▆█▆▆▆▂ #88 of 579 * · 95% of its score · 1 test

Beats LongCat 2.0 (1.6T-A48B), GPT-5, Mistral Large 4 (public preview) here, though they rank higher overall. Trails Gemma 4 26B-A4B, Nemotron Cascade 2 30B-A3B, Gemma 4 31B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1436.6 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 293/379avg 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= 69.2

$ 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