[whatmodel.sh](../../README.md) / LaMDA 68B (pre-trained) # LaMDA 68B (pre-trained) Google · released 2022-01 · 68B params · proprietary **score 26.6** ±0.8 · comparison only: no current run option verified · from 9 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 LaMDA 68B (pre-trained) among every model on that task or test, worst to best. 
task  vs the others          rank  counts  tests
math  ▁▁●▆██▅█▃▁▁▁   #479 of 530*    9.1%      1
math · #479 of 530 · 9.1% of its score Beats [Yi 34B Chat](yi-34b-chat.md), [Command R (03-2024)](command-r-03-2024.md), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md) here, though they rank higher overall. Trails [GPT-Neo 125M](gpt-neo-125m.md), [GPT-2 XL (1.5B)](gpt-2-xl.md), [GPT-Neo 1.3B](gpt-neo-1-3b.md) here, though they rank lower overall. 
test           result  vs the others in its run  difficulty      counts  why
GSM8K (maker)    8.2%  █·▅▃▃●·▃·▃▃▅ beats 7/12   easy · avg 67%    9.1%  sharp · too hard for it
other tests (8) 
test                                result  vs the others in its run  difficulty           counts  why
MAWPS (CoT-paper union) (maker)      30.6%  ▅▃█···●·▃▃▃▅ beats 7/12   very easy · avg 94%     37%  sharp · at its level
SVAMP (maker)                        18.8%  ▃▅█···●·▃▃▃▅ beats 7/12   easy · avg 82%          18%  sharp
ASDiv (maker)                        23.1%  ▅▃▃▅··●▃·▃▃█ beats 6/12   easy · avg 83%          17%  sharp
CommonsenseQA (maker)                37.0%  █······●·▁▁▃ beats 7/12   easy · avg 88%         6.8%  sharp
BIG-bench Date Understanding (mak…   15.5%  ▃·▅·▅▃●·▃▃·█ beats 7/12   medium · avg 52%       4.1%  typical
BIG-bench Sports Understanding (m…   55.2%  █·····●·▁·▄▁ beats 7/12   medium · avg 81%       3.6%  sharp
AQuA-RAT (maker)                     20.2%  ●▁·▁▁·····▂▁ beats 0/12   medium · avg 48%       1.8%  sharp · too hard for it
StrategyQA (maker)                   59.6%  █·····▅▃▃●▃█ beats 8/12   medium · avg 70%       1.5%  typical
## 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
Chain-of-Thought Prompting Elicits Reas…        9  100% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 26.6
Licences: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models ([CC BY 4.0](../../licenses/paper-2201.11903v6-CC-BY-4-0.txt)) ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/lamda-68b) · [JSON](https://whatmodel.sh/api?format=json&all=1)