whatmodel.sh / Hunyuan-Standard (2025-02-10)
Hunyuan-Standard (2025-02-10)
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Hunyuan-Standard (2025-02-10) is a closed-weights language model from Tencent, released February 2025.
Catalogue identity notes
Comparison-only. Arena entry 'hunyuan-standard-2025-02-10' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'hunyuan-standard-2025-02-10' as 'Hunyuan-Standard-2025-02-10' (Tencent, Proprietary), linking https://cloud.tencent.com/document/product/1729/104753. Released 2025-02: Tencent Cloud Hunyuan version changelog (document 1729/97765) / date in the API id. The 2025-02-10 version update of the closed hunyuan-standard API model (Tencent changelog). Retired.
$ how it compares
The LLMs ranked either side of Hunyuan-Standard (2025-02-10), out of 1132 with a score, then more from Tencent.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 420 |  GigaChat 3.5 Ultra Instruct (432B-A28B) | 55±1 | local | ||
| 421 |  Llama 3.1 405B Instruct | 55±1 | api $2.40/M +local | ||
| 422 | Hunyuan-Standard (2025-02-10) | 55±3 | comparison only | ||
| 423 |  PLLuM-12B-nc-chat (2507) | 55±4 | local | ||
| 424 |  EXAONE 3.5 32B Instruct | 55±4 | local | ||
| more from Tencent | |||||
| 16 |  Hy4 preview (770B-A49B) | 79±1 | api $1.25/M +local | ||
| 57 | Hunyuan Hy3 (295B-A21B) | 74±1 | api $0.23/M +local | ||
| 139 | Hy3 preview (295B-A21B) | 68±1 | local | ||
$ what it's good at
chat & writing▁▁▁▃▅▅▆█▆▆▆▂ #290 of 579 * · 95% of its score · 1 test
Beats Granite 4.2 8B, Qwen3-VL 30B-A3B Thinking, Mistral Small 4 (119B-A6.5B) here, though they rank higher overall. Trails LFM2.5 1.2B Instruct, Granite 4.0 H Micro 3B, Gemma 3n E4B here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LMArena (text) | 1311.3 ✎ | ▁▁▁▂▃▃▃▆▆▇█▃ beats 140/379 | 95% |
$ 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
| source | results | share of the score |
|---|---|---|
 LMArena leaderboard dataset | 1 | 95% |
| prior · pull toward the middle | 5% | |
| weighted average | = 54.9 |
$ 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