[whatmodel.sh](../../README.md) / cont-PaLM 62B (PaLM 62B trained to 1.3T tokens) # cont-PaLM 62B (PaLM 62B trained to 1.3T tokens) Google · released 2022-10 · 62.5B params · proprietary **score 43.0** ±1.0 · comparison only: no current run option verified · from 15 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 cont-PaLM 62B (PaLM 62B trained to 1.3T tokens) among every model on that task or test, worst to best.}

task    vs the others          rank  counts  tests
coding  █·█······●·█       #2 of 4*     14%      2
math    ▁▁▃▆█●▅█▃▁▁▁   #299 of 530*     23%      2

coding · #2 of 4 · 14% of its score

test               result  vs the others in its run   difficulty        counts  why
HumanEval (maker)   23.7%  ▁▁▁▁▂●▆▆█▅▂▁ beats 34/205  medium · avg 46%     10%  sharp · at its level
MBPP (maker)        31.2%  ▁▁▁●▅█▆▅▄▁▂▁ beats 8/86    medium · avg 50%    4.5%  at its level · small run

math · #299 of 530 · 23% of its score

Beats [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md), [Command A (03-2025)](command-a.md), [GPT-4o (2024-05-13)](gpt-4o-2024-05-13.md) here, though they rank higher overall. Trails [Gemma 7B (base)](gemma-7b-base.md), [CodeGemma 7B (v1.0)](codegemma-7b.md), [Phi-2 (2.7B)](phi-2.md) here, though they rank lower overall.

test           result  vs the others in its run   difficulty        counts  why
MGSM (maker)    32.0%  ▆·▂·▄▂▂▂▂●▄▆ beats 11/18   medium · avg 48%     13%  sharp · at its level
GSM8K (maker)   48.7%  ▂▂▂▂▃▄▅●██▆▄ beats 84/206  easy · avg 69%       10%  sharp · at its level · small run

other tests (10)

test                                result  vs the others in its run    difficulty        counts  why
MMLU (maker)                         61.2%  ▁···▁··▃▁▅●█ beats 10/18    easy · avg 74%       22%  at its level · several runs
BIG-Bench Hard (maker)               41.7%  ▂▂▂▂·▅·●▂█▃▃ beats 8/18     medium · avg 60%     13%  sharp · at its level
TyDiQA-GoldP (1-shot, EM, 8-langu…   45.7%  ▂··▂·▂▄▄·●██ beats 10/18    medium · avg 61%    5.7%  at its level
TriviaQA (maker)                     71.8%  ▁▁▂▂▄▄▃▆▅█●▂ beats 71/80    easy · avg 81%      4.4%  at its level
Natural Questions (closed book) (…   21.8%  ▂▁▁▁▂▄▄▃▅●▅▁ beats 45/68    hard · avg 32%      4.4%  typical
WinoGrande (maker)                   77.0%  ▁▁▁▁▂▁▁▅▇█●▁ beats 118/131  easy · avg 85%      4.3%  at its level
BoolQ (maker)                        83.9%  ▁····▁▂█▆▅●▄ beats 87/103   easy · avg 89%      3.2%  at its level
HellaSwag (maker)                    80.6%  ▁▁▁▂▂▂▄▆█●▄▂ beats 112/135  easy · avg 88%      2.9%  typical
LAMBADA (OpenAI version), last-wo…   76.3%  ▁·····▁▁▃▃█● beats 81/86    easy · avg 85%      1.3%  blunt
PIQA (maker)                         81.4%  ▁▁▁▂▂▃▅█●▃▁▁ beats 105/123  easy · avg 86%      1.1%  blunt · 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
PaLM: Scaling Language Modeling with Pa…       11   52% ▇▇▇▇▇▇
Scaling Instruction-Finetuned Language …        4   48% ▇▇▇▇▇▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 43.0

_{Licences: PaLM: Scaling Language Modeling with Pathways ([CC BY 4.0](../../licenses/paper-2204.02311v5-CC-BY-4-0.txt)) · 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/cont-palm-62b) · [JSON](https://whatmodel.sh/api?format=json&all=1)