[whatmodel.sh](../../README.md) / Nemotron-4 340B Instruct # Nemotron-4 340B Instruct NVIDIA · released 2024-06 · 340B params · nvidia-open-model-license **score 52.6** ±1.7 · comparison only: no current run option verified · from 4 benchmark results across 4 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Nemotron-4 340B Instruct among every model on that task or test, worst to best.}

task            vs the others          rank  counts  tests
chat & writing  ▁▁▁▃▅▄●█▇▇▇▃   #301 of 474*     59%      2

chat & writing · #301 of 474 · 59% of its score

Beats [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Qwen2.5 Coder 32B Instruct](qwen2-5-coder-32b.md), [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [Granite 4.0 H 1B](granite-4-0-h-1b.md), [Granite 4.0 1B](granite-4-0-1b.md) here, though they rank lower overall.

test                                result  vs the others in its run    difficulty  counts  why
LMArena (text)                      1277.2  ▁▁▁▂▃▃●▆▆▇█▃ beats 110/385  avg 1260       31%  blunt
Polyrating rating (bias-aware re-…  1222.0  ▂▁▂▁▃▇█▆▇▄●▂ beats 88/95    avg 1187       28%  typical

other tests (2)

test                                          result  vs the others in its run    difficulty           counts  why
WildBench v2 WB-Score (task macro)  47.6725 WB-Score  ▁·▁▁·▃█▄▇●▅▆ beats 37/52    avg 43.019 WB-Score     33%  sharp
Open PT-LLM Leaderboard average (…             77.8%  ▃▁▁▁▁▁▁▁▂█●▁ beats 241/276  medium · avg 72%       6.9%  at its level · noisy

## 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
WildBench v2 leaderboard data                   1   33% ▇▇▇▇
LMArena leaderboard dataset                     1   31% ▇▇▇▇
Polyrating: A Cost-Effective and Bias-A…        1   28% ▇▇▇
Open Portuguese LLM Leaderboard                 1    7% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 52.6

_{Licences: WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))} ## How to run it No verified API or local download yet. Model repository and documentation: [huggingface.co/nvidia](https://huggingface.co/nvidia/Nemotron-4-340B-Instruct) [interactive version](https://whatmodel.sh/model/nemotron-4-340b-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)