[whatmodel.sh](../../README.md) / DeepSeek LLM 7B Chat # DeepSeek LLM 7B Chat DeepSeek · released 2023-11 · 7B params · deepseek **score 36.5** ±1.6 · #614 of 732 · from 8 benchmark results across 1 source · † no independently run results verified Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks DeepSeek LLM 7B Chat among every model on that task or test, worst to best.}

task    vs the others          rank  counts  tests
coding  ▁▁▁▃●▇█▇▅▄▃▁   #413 of 469*     26%      2
math    ●··········█       #2 of 2*     29%      2

coding · #413 of 469 · 26% of its score

Beats [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Qwen3.5 2B](qwen3-5-2b.md), [DeepSeek R1 Distill Llama 8B](deepseek-r1-distill-llama-8b.md) here, though they rank higher overall.

test               result  vs the others in its run   difficulty      counts  why
HumanEval (maker)   45.1%  ▁▁▁▁▂▂▆●█▅▂▁ beats 80/205  easy · avg 81%     17%  sharp · at its level · small run
MBPP (maker)        39.0%  ▁▁▁▄●█▆▅▄▁▂▁ beats 17/86   easy · avg 68%    8.7%  at its level · small run

math · #2 of 2 · 29% of its score

test           result  vs the others in its run    difficulty           counts  why
GSM8K (maker)   62.6%  ▂▂▂▂▃▄▅▇●█▆▄ beats 104/206  very easy · avg 89%     19%  sharp · at its level · small run
MATH (maker)    14.7%  ▃▅▂▆●▃▅▅█▃▃▂ beats 12/30    medium · avg 45%        10%  small run

other tests (4)

test                    result  vs the others in its run   difficulty        counts  why
BIG-Bench Hard (maker)   43.1%  ▁▁▁▁▂▄●▆█▃▃▁ beats 43/155  medium · avg 71%     16%  sharp · small run
MMLU (maker)             49.7%  ▁▁▁▁▁▂▂●██▆▄ beats 50/214  easy · avg 76%       13%  at its level · small run
C-Eval (maker)           44.7%  ▁···▁▁●▁▂▂▃█ beats 4/33    medium · avg 72%    7.9%  at its level · small run
CMMLU (maker)            51.2%  ●·▅····█▃▅█▅ beats 0/14    easy · avg 74%      7.1%  at its level · 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 vendor-reported benchmark scor…        8   99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 36.5

## How to run it ### Ollama (recommended) 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run DeepSeek LLM 7B Chat (Q4_0, needs ~4 GB, from [ollama.com](https://ollama.com/library/deepseek-llm/tags)) ```sh ollama run deepseek-llm:7b ``` [interactive version](https://whatmodel.sh/model/deepseek-llm-7b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)