[whatmodel.sh](../../README.md) / Flan-cont-PaLM 62B # Flan-cont-PaLM 62B Google · released 2022-10 · 62.5B params · proprietary **score 47.0** ±1.4 · comparison only: no current run option verified · from 4 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 Flan-cont-PaLM 62B among every model on that task or test, worst to best. 
task  vs the others          rank  counts  tests
math  ▁▁▃▆█●▅█▃▁▁▁   #257 of 530*     29%      1
math · #257 of 530 · 29% of its score Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md) here, though they rank higher overall. Trails [Phi-2 (2.7B)](phi-2.md), [Granite 8B Code Base (4K)](granite-8b-code-base-4k.md), [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md) here, though they rank lower overall. 
test          result  vs the others in its run  difficulty        counts  why
MGSM (maker)   40.3%  ▆·▂·▄▂▂▂▂█●▆ beats 14/18  medium · avg 48%     29%  sharp · at its level
other tests (3) 
test                                result  vs the others in its run  difficulty        counts  why
MMLU (maker)                         66.1%  ▁···▁··▃▁▅▅● beats 13/18  easy · avg 74%       31%  at its level
BIG-Bench Hard (maker)               51.0%  ▂▂▂▂·▅·▃▂█●▃ beats 15/18  medium · avg 60%     28%  sharp · at its level
TyDiQA-GoldP (1-shot, EM, 8-langu…   62.7%  ▂··▂·▂▄▄·██● beats 16/18  medium · avg 61%     11%  at its level
## 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   99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 47.0
Licences: Scaling Instruction-Finetuned Language Models ([CC BY 4.0](../../licenses/paper-2210.11416v5-CC-BY-4.0.txt)) ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/flan-cont-palm-62b) · [JSON](https://whatmodel.sh/api?format=json&all=1)