[whatmodel.sh](../../README.md) / Flan-PaLM 540B # Flan-PaLM 540B Google · released 2022-10 · 540B params · proprietary **score 50.9** ±1.2 · comparison only: no current run option verified · from 7 benchmark results across 2 sources · † 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 Flan-PaLM 540B among every model on that task or test, worst to best.}

task  vs the others          rank  counts  tests
math  ▁▁▃▆██●█▃▁▁▁   #201 of 530*     23%      1

math · #201 of 530 · 23% of its score

Beats [MiniMax-M2.1](minimax-m2-1.md), [Mistral Medium 3.5 (128B)](mistral-medium-3-5.md), [GPT-4.1](gpt-4-1.md) here, though they rank higher overall. Trails [OpenThinker3 7B](openthinker3-7b.md) here, though they rank lower overall.

test          result  vs the others in its run  difficulty        counts  why
MGSM (maker)   57.0%  ▆·▂·▄▂▂▂▂█▄● beats 17/18  medium · avg 48%     23%  sharp · at its level

other tests (6)

test                                result  vs the others in its run  difficulty        counts  why
BIG-Bench Hard (maker)               57.9%  ▂▂▂▂·▅·▃▂█▃● beats 17/18  medium · avg 60%     22%  sharp · at its level
MMLU (maker)                         73.5%  ▁···▁··▃▁▅▅● beats 17/18  easy · avg 74%       21%  at its level
MedQA USMLE 4-option (few-shot pr…   60.3%  ▄······▄▄▄·● beats 5/5    medium · avg 58%     12%  at its level
MedMCQA (few-shot prompted) (make…   56.5%  ▄·····▄··█·● beats 5/5    medium · avg 56%     10%  at its level
TyDiQA-GoldP (1-shot, EM, 8-langu…   67.8%  ▂··▂·▂▄▄·██● beats 17/18  medium · avg 61%    7.7%  at its level
PubMedQA (few-shot prompted) (mak…   79.0%  ▄·······▄▄▄● beats 5/5    medium · avg 75%    2.8%  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
Scaling Instruction-Finetuned Language …        4   74% ▇▇▇▇▇▇▇▇▇
Large Language Models Encode Clinical K…        3   25% ▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 50.9

_{Licences: Scaling Instruction-Finetuned Language Models ([CC BY 4.0](../../licenses/paper-2210.11416v5-CC-BY-4.0.txt)) · Large Language Models Encode Clinical Knowledge ([CC BY 4.0](../../licenses/paper-2212.13138v1-CC-BY-4-0.txt))} ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/flan-palm-540b) · [JSON](https://whatmodel.sh/api?format=json&all=1)