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Hunyuan-T1 (20250711)

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Hunyuan-T1 (20250711) is a closed-weights language model from Tencent, released July 2025.

Catalogue identity notes

Comparison-only. Arena entry 'hunyuan-t1-20250711' (text). Released 2025-07: Tencent Cloud Hunyuan version changelog (document 1729/97765) / date in the API id. Exact dated Tencent Cloud API snapshot hunyuan-t1-20250711 (T1 reasoning model on the TurboS base). Retired.

63.6±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 Hunyuan-T1 (20250711), out of 1132 with a score, then more from Tencent.

#modelscorewhereRAMreleased
214Kimi K2 Instruct (0905) Moonshot AI64±2local~298GB2025-09
215Qwen3-VL 235B-A22B Instruct Alibaba64±1local~134GB2025-09
216Hunyuan-T1 (20250711) Tencent64±3comparison only2025-07
217Nova 2 Pro (Preview) Amazon64±2api $3.78/M2025-11
218Grok 3 Mini Beta SpaceXAI (xAI)64±1comparison only2025-04
more from Tencent
16Hy4 preview (770B-A49B) Tencent79±1api $1.25/M +local~248GB2026-08
57Hunyuan Hy3 (295B-A21B) Tencent74±1api $0.23/M +local~96GB2026-07
139Hy3 preview (295B-A21B) Tencent68±1local~629GB2026-04

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▅▆█▆▆▆▂ #161 of 579 * · 95% of its score · 1 test

Beats LongCat 2.0 (1.6T-A48B), MiMo-V2-Flash (309B-A15B), MiniMax-M2.1 here, though they rank higher overall. Trails LFM2.5 2.6B, Nemotron-Cascade-8B, DeepSeek V3 0324 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1387.2 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 227/379avg 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= 63.6

$ 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