[whatmodel.sh](../../README.md) / Trinity-Large-Thinking # Trinity-Large-Thinking Arcee AI · released 2026-04 · 398B params (13B active) · openmdw-1.1 **score 62.2** ±1.5 · #178 of 732 · from 5 benchmark results across 2 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-Thinking among every model on that task or test, worst to best.}

task            vs the others            rank  counts  tests
agent tasks                    not ranked yet       —      1
coding          ▁▁▁▃▅▇█●▅▄▃▁     #178 of 469*     44%      1
chat & writing  ▁▁▁▃▆▄▆█●▇▇▃     #147 of 402*     25%      1
math            █··········●         #1 of 2*    3.0%      1

agent tasks · 0.0% of its score

test                      result  vs the others in its run  difficulty            counts  why
τ²-bench Telecom (maker)   94.7%  ▁·▅█▆▅▂▃●▂▃▁ beats 74/89  very hard · avg 8.8%       —  not counted yet: no other model in the …

coding · #178 of 469 · 44% of its score

Beats [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Qwen3-VL 235B-A22B Instruct](qwen3-vl-235b-a22b-instruct.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 8B Reasoning 2512](ministral-3-8b-reasoning.md), [Phi-4-reasoning 14B](phi-4-reasoning.md) here, though they rank lower overall.

test                       result  vs the others in its run  difficulty  counts  why
Arena WebDev (Code Arena)  1238.0  ▁▁●▂▅█▃▆▅▅▁▂ beats 9/129  avg 981        44%  sharp

chat & writing · #147 of 402 · 25% of its score

Beats [INTELLECT-3](intellect-3.md), [MiniMax-M2](minimax-m2.md), [Nemotron 3 Super 120B-A12B](nemotron-3-super-120b-a12b.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [LFM2.5 2.6B](lfm2-5-2-6b.md) here, though they rank lower overall.

test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1367.5  ▁▁▁▂▃▃▃▆●▇█▃ beats 211/385  avg 1260       25%  blunt

math · #1 of 2 · 3.0% of its score

test               result  vs the others in its run    difficulty        counts  why
AIME 2025 (maker)   96.3%  ▁·▁▁▁▂▅▆█▇▅● beats 132/133  medium · avg 36%    3.0%  sharp · at its level · noisy · small run

other tests (2)

test                  result  vs the others in its run    difficulty        counts  why
GPQA Diamond (maker)   76.3%  ▁▁▁▁▂▄▅▅●██▄ beats 185/322  hard · avg 43%       19%  sharp · at its level · small run
MMLU-Pro (maker)       83.4%  ▁▁·▁▁▁▃▂▄▅●▁ beats 156/188  medium · avg 59%    8.5%  small run

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md)

source                                    results  share of the score
LMArena leaderboard dataset                     2   69% ▇▇▇▇▇▇▇▇
Arcee AI vendor-reported benchmark scor…        3   30% ▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 62.2

_{Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))}

Not counted (1)

source                  test              score  why
Arcee AI vendor-repor…  τ²-bench Telecom  94.7%  too few models have it yet

## How to run it ### Through an API api (Arcee AI) · $0.39/M blended · $0.25 in / $0.8 out per 1M tokens · [docs.arcee.ai](https://docs.arcee.ai/arcee-language-models/trinity-large-thinking.md) _{[price source](https://docs.arcee.ai/get-started/pricing.md): USD per 1M tokens, Arcee Platform text-model pricing table, model trinity-large-thinking: input $0.25, output $0.80; cached input $0.06 excluded. Currency: USD. Arcee official pricing docs, observed 2026-10-07. · [context source](https://huggingface.co/arcee-ai/Trinity-Large-Thinking/blob/dc6a99b6b74202880e31491a445a94884c97890c/README.md): Context length stated on the official model card.} ### 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-Thinking (Q4_K_M, needs ~241.85 GB, from [huggingface.co/arcee-ai](https://huggingface.co/arcee-ai/Trinity-Large-Thinking-GGUF/tree/352ed5f50f0148466e596950c29aecb51bed4cb3)) ```sh llama cli -hf arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M ``` Other versions: IQ2_M (~133.01 GB) `llama cli -hf arcee-ai/Trinity-Large-Thinking-GGUF:IQ2_M` ### 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-Thinking (Q4_K_M, needs ~241.85 GB, from [huggingface.co/arcee-ai](https://huggingface.co/arcee-ai/Trinity-Large-Thinking-GGUF/tree/352ed5f50f0148466e596950c29aecb51bed4cb3)) ```sh lms get https://huggingface.co/arcee-ai/Trinity-Large-Thinking-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: IQ2_M (~133.01 GB) `lms get https://huggingface.co/arcee-ai/Trinity-Large-Thinking-GGUF@IQ2_M -y` Model repository and documentation: [huggingface.co/arcee-ai](https://huggingface.co/arcee-ai/Trinity-Large-Thinking) [interactive version](https://whatmodel.sh/model/trinity-large-thinking) · [JSON](https://whatmodel.sh/api?format=json&all=1)