whatmodel.sh / Hunyuan-Large-Vision
Hunyuan-Large-Vision
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Hunyuan-Large-Vision is a closed-weights language model from Tencent, released May 2025.
Catalogue identity notes
Comparison-only. Arena entry 'hunyuan-large-vision' (text, vision). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'hunyuan-large-vision' as 'Hunyuan-Large-Vision' (Tencent, Proprietary), linking https://cloud.tencent.com/document/product/1729/104753#d6cb04da-053d-4bed-b187-191514feb972. Released 2025-05: Tencent Cloud Hunyuan version changelog (document 1729/97765) / date in the API id. Closed Tencent Cloud vision-language API model hunyuan-large-vision (API launch 2025-05-26). Absent from the current Hunyuan overview, so treated as retired.
$ how it compares
The LLMs ranked either side of Hunyuan-Large-Vision, out of 1132 with a score, then more from Tencent.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 394 | 56±3 | comparison only | |||
| 395 | 56±3 | comparison only | |||
| 396 | 56±3 | comparison only | |||
| 397 | 56±4 | local | |||
| 398 | 56±2 | local | |||
| more from Tencent | |||||
| 16 | 79±1 | api $1.25/M +local | |||
| 57 | 74±1 | api $0.23/M +local | |||
| 139 | 68±1 | local | |||
$ what it's good at
chat & writing▁▁▁▃▅▅▆█▆▆▆▂ #319 of 579 * · 57% of its score · 1 test
Beats Granite 4.2 8B, Mistral Small 4 (119B-A6.5B), Olmo 3.1 32B Think here, though they rank higher overall. Trails Flan-T5 XXL, LFM2.5 1.2B Instruct, Gemma 3 4B here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LMArena (text) | 1294.0 ✎ | ▁▁▁▂▃▃▃▆▆▇█▃ beats 126/379 | 57% |
vision & documents▁▁▃▃▂▃▅▅▇▇█▃ #122 of 199 * · 40% of its score · 1 test
Beats DeepSeek V4 Flash Vision (Exp), GPT-5 nano, Step 3 here, though they rank higher overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Arena vision (style controlled) | 1148.8 ✎ | ▁▁▂▂▂▂▄▅▅▆██ beats 48/145 | 40% |
$ 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
| source | results | share of the score |
|---|---|---|
| 2 | 97% | |
| prior · pull toward the middle | 3% | |
| weighted average | = 55.7 |
$ improve this page
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