[whatmodel.sh](../../README.md) / Trinity-Large-Preview
# Trinity-Large-Preview
Arcee AI · released 2026-01 · 398B params (13B active) · openmdw-1.1
**score 58.8** ±2.0 · #215 of 732 · from 5 benchmark results across 3 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Trinity-Large-Preview among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃▆▄▆█●▇▇▃ #136 of 402* 43% 1
math ●··········█ #2 of 2* 6.0% 1
chat & writing · #136 of 402 · 43% of its score
Beats [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [o3-mini](o3-mini.md), [K-EXAONE (236B-A23B)](k-exaone-236b-a23b.md) here, though they rank higher overall. Trails [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [LFM2.5 2.6B](lfm2-5-2-6b.md), [Nemotron-Cascade-8B](nemotron-cascade-8b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1378.5 ▁▁▁▂▃▃▃▆●▇█▃ beats 220/385 avg 1260 43% blunt
math · #2 of 2 · 6.0% of its score
test result vs the others in its run difficulty counts why
AIME 2025 (maker) 24.0% ▁·▁▁▁●▅▆█▇▅▁ beats 14/133 medium · avg 36% 6.0% sharp · at its level · noisy · small run
other tests (3)
test result vs the others in its run difficulty counts why
GPQA Diamond (maker) 63.3% ▁▁▁▁▂▄▅●███▄ beats 123/322 hard · avg 43% 33% sharp · at its level · small run
MMLU-Pro (maker) 75.2% ▁▁·▁▁▁▃▂▄●█▁ beats 103/188 medium · avg 59% 16% at its level · small run
Vectara hallucination leaderboard… 93.1% ▁▂▁▂▅█▂●▄▃▁▁ beats 71/98 very easy · avg 90% 0.0% blunt
source results share of the score
Arcee AI vendor-reported benchmark scor… 3 55% ▇▇▇▇▇▇▇
LMArena leaderboard dataset 1 43% ▇▇▇▇▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 2% ▇
weighted average = 58.8
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Trinity-Large-Preview (ud-q4_k_xl, needs ~247.33 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/Trinity-Large-Preview-GGUF/tree/c11bb8d23b76fe5c05199afac0f37010f76c7d5c))
```sh
llama cli -hf unsloth/Trinity-Large-Preview-GGUF:UD-Q4_K_XL
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf unsloth/Trinity-Large-Preview-GGUF:UD-Q4_K_XL
```
Other versions: ud-q2_k_xl (~145.84 GB) `llama cli -hf unsloth/Trinity-Large-Preview-GGUF:UD-Q2_K_XL`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Trinity-Large-Preview (ud-q4_k_xl, needs ~247.33 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/Trinity-Large-Preview-GGUF/tree/c11bb8d23b76fe5c05199afac0f37010f76c7d5c))
```sh
lms get https://huggingface.co/unsloth/Trinity-Large-Preview-GGUF@UD-Q4_K_XL -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: ud-q2_k_xl (~145.84 GB) `lms get https://huggingface.co/unsloth/Trinity-Large-Preview-GGUF@UD-Q2_K_XL -y`
Model repository and documentation: [huggingface.co/arcee-ai](https://huggingface.co/arcee-ai/Trinity-Large-Preview)
[interactive version](https://whatmodel.sh/model/trinity-large-preview) · [JSON](https://whatmodel.sh/api?format=json&all=1)