[whatmodel.sh](../../README.md) / DeepSeek V3.1 Terminus # DeepSeek V3.1 Terminus DeepSeek · released 2025-09 · 671B params (37B active) · mit **score 65.9** ±1.3 · #127 of 732 · from 11 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 DeepSeek V3.1 Terminus among every model on that task or test, worst to best. 
task            vs the others         rank  counts  tests
agent tasks     ▁▁▃▆█▄●▅▄▄▂▁   #136 of 313     30%      5
coding          ▁▁▁▃▅▇█▇●▄▃▁   #109 of 469     43%      4
chat & writing  ▁▁▁▃▆▄▆█▆●▇▃   #96 of 402*     17%      1
web search      █········█·●      #1 of 3*     11%      2
agent tasks · #136 of 313 · 30% of its score Beats [o3](o3.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [Gemini 3.1 Flash-Lite](gemini-3-1-flash-lite.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Large](jamba-1-5-large.md), [Devstral 2 (2512)](devstral-2.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty           counts  why
SWE-bench Verified (maker)           68.4%  ▁▁▁▁▁▁▂▄●▆▁▁ beats 75/138  hard · avg 23%         9.3%  sharp · at its level · small run
BrowseComp-ZH (maker)                45.0%  ▁▁▁▁▁▃·▁●▅▄█ beats 10/23   hard · avg 17%         6.4%  at its level · small run
SWE-bench Multilingual (maker)       57.8%  ▁··▁▃▁●██▂▂▁ beats 21/65   hard · avg 23%         6.0%  at its level · small run
BrowseComp (maker)                   38.5%  ▂·▃▁▂▂●▅█▂▂▄ beats 12/46   hard · avg 15%         4.5%  at its level · small run
Terminal-Bench (pre-2.0 release) …   36.7%  ▃·▃···▃▃▅·●█ beats 6/10    very hard · avg 14%    3.8%  at its level · small run
coding · #109 of 469 · 43% of its score Beats [Grok 4.3](grok-4-3.md), [Muse Glimmer 30B](muse-glimmer-30b.md), [Qwen3.5 Plus](qwen3-5-plus.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Nemotron Cascade 2 30B-A3B](nemotron-cascade-2-30b-a3b.md), [MiniMax-M2.1](minimax-m2-1.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty           counts  why
Aider polyglot (225 exercises, tw…   76.1%  ▂▁▁▃▆▃▄▆█▃●▂ beats 63/71   very hard · avg 12%     22%  sharp · small run
SWE-bench Verified (maker)           68.4%  ▁▁▁▁▁▁▂▄●▆▁▁ beats 75/138  hard · avg 23%         9.3%  sharp · at its level · small run
SWE-bench Multilingual (maker)       57.8%  ▁··▁▃▁●██▂▂▁ beats 21/65   hard · avg 23%         6.0%  at its level · small run
LiveCodeBench (version unspecifie…   74.9%  ▁···▁▁▁█▇▃●▁ beats 34/38   medium · avg 42%       4.9%  at its level · small run
chat & writing · #96 of 402 · 17% of its score Beats [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [Inkling Small (266B-A12B)](inkling-small.md), [GPT-5.4 nano](gpt-5-4-nano.md) here, though they rank higher overall. Trails [Gemma 4 12B](gemma-4-12b.md), [Kimi K2 Instruct (0905)](kimi-k2-0905.md), [Gemma 4 26B-A4B](gemma-4-26b-a4b.md) here, though they rank lower overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1416.1  ▁▁▁▂▃▃▃▆▆●█▃ beats 268/385  avg 1260       17%  blunt
web search · #1 of 3 · 11% of its score 
test                   result  vs the others in its run  difficulty      counts  why
BrowseComp-ZH (maker)   45.0%  ▁▁▁▁▁▃·▁●▅▄█ beats 10/23  hard · avg 17%    6.4%  at its level · small run
BrowseComp (maker)      38.5%  ▂·▃▁▂▂●▅█▂▂▄ beats 12/46  hard · avg 15%    4.5%  at its level · small run
other tests (3) 
test                          result  vs the others in its run    difficulty            counts  why
GPQA Diamond (maker)           80.7%  ▁▁▁▁▂▄▅▅█●█▄ beats 207/322  hard · avg 42%           13%  sharp · small run
Humanity's Last Exam (maker)   21.7%  ▁▁▁▄▆▆●██▆▁▃ beats 55/111   very hard · avg 4.7%    6.3%  small run
MMLU-Pro (maker)               85.0%  ▁▁·▁▁▁▃▂▄▅●▁ beats 170/188  medium · avg 58%        6.0%  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
DeepSeek-V3.1-Terminus model card (subm…       10   83% ▇▇▇▇▇▇▇▇▇▇
LMArena leaderboard dataset                     1   17% ▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 65.9
Licences: DeepSeek-V3.1-Terminus model card ([MIT](https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Terminus/blob/19510d6dc61f79dbd925bd51ee8a9081c509a4b6/LICENSE)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) ## 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 DeepSeek V3.1 Terminus (Q4_K_M, needs ~405.4 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF/tree/fe48342e95b8b3ca863189919605651fc2e88f4e)) ```sh llama cli -hf unsloth/DeepSeek-V3.1-Terminus-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf unsloth/DeepSeek-V3.1-Terminus-GGUF:Q4_K_M ``` Other versions: Q8_0 (~713.3 GB) `llama cli -hf unsloth/DeepSeek-V3.1-Terminus-GGUF:Q8_0` · UD-IQ2_XXS (~217.1 GB) `llama cli -hf unsloth/DeepSeek-V3.1-Terminus-GGUF:UD-IQ2_XXS` ### 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 DeepSeek V3.1 Terminus (Q4_K_M, needs ~405.4 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF/tree/fe48342e95b8b3ca863189919605651fc2e88f4e)) ```sh lms get https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: Q8_0 (~713.3 GB) `lms get https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF@Q8_0 -y` · UD-IQ2_XXS (~217.1 GB) `lms get https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF@UD-IQ2_XXS -y` · 4bit (~377.6 GB, Mac only) `lms get https://huggingface.co/mlx-community/DeepSeek-V3.1-Terminus-4bit -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run DeepSeek V3.1 Terminus (Q4_K_M, needs ~403.1 GB, from [ollama.com](https://ollama.com/library/deepseek-v3.1/tags)) ```sh ollama run deepseek-v3.1:671b-terminus-q4_K_M ``` Other versions: Q8_0 (~805.7 GB) `ollama run deepseek-v3.1:671b-terminus-q8_0` ### MLX (Mac) 1. Install MLX (Mac). Apple silicon Macs only, text only. ```sh # with uv uv tool install mlx-lm # or pip install mlx-lm ``` 2. Run DeepSeek V3.1 Terminus (4bit, needs ~377.6 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/DeepSeek-V3.1-Terminus-4bit/tree/90d058816d4a3592924c3650447e2f995f698204)) ```sh mlx_lm.chat --model mlx-community/DeepSeek-V3.1-Terminus-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/DeepSeek-V3.1-Terminus-4bit ``` Model repository and documentation: [huggingface.co/deepseek-ai](https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Terminus) [interactive version](https://whatmodel.sh/model/deepseek-v3-1-terminus) · [JSON](https://whatmodel.sh/api?format=json&all=1)