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GLaM dense 137B

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GLaM dense 137B is a closed-weights language model from Google, released December 2021. Its published size is 137B parameters. Licence: proprietary.

Catalogue identity notes

Google's GLaM 137B dense baseline model (Du et al., arXiv:2112.06905, Table 4: 137B total / 137B activated parameters), trained on the GLaM dataset. Research size from the same paper. Never released. Not the same checkpoint as LaMDA-PT even where sizes match. Comparison-only.

Model information sources

Release date ↗

63.2±1.3comparison only: no current run option verifiedfrom 29 benchmark results across 1 source

Score within the ANLI Round 1 (prompted) comparison set only.

$ how it compares

Scored LLMs released closest to GLaM dense 137B, out of 1182 with a score, then more from Google.

#modelscorewhereRAMreleased
1034LaMDA 137B (pre-trained, LaMDA-PT) Google34±1never released2022-01
1035GPT-3 text-davinci-001 OpenAI34±1comparison only2022-01
1106LaMDA 68B (pre-trained) Google27±1never released2022-01
1156LaMDA 8B (pre-trained) Google12±2never released2022-01
1158GPT-3 text-curie-001 OpenAI12±1comparison only2022-01
more from Google
11Gemini 4 Argon Google80±1api $4.00/M2026-09
26Gemini 3.8 Flash Google76±1api $1.50/M2026-09
34Gemini 3.7 Flash Google75±1api $1.50/M2026-08

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$ what it's good at

other tests29 not in a task · 99% of its score
testresultvs the others in its rundifficultycountswhy
TriviaQA lab64.0% ✎▄··▄···▄·█▄█ beats 6/7hard · avg 35%▇▇▇▇▇ 13%sharp · at its level
Natural Questions (closed book) lab17.3% ✎▄·▄··▄·█▄·▄▄ beats 6/7very hard · avg 7.2%▇▇▇▇▇ 12%sharp
ARC Challenge lab47.2% ✎▂··▂·····▄·█ beats 6/7very hard · avg 35%▇▇▇▇ 9.3%sharp · at its level
WinoGrande lab71.5% ✎▃··▃·····▃▅█ beats 6/7hard · avg 61%▇▇▇ 7.9%sharp · at its level
WebQuestions (closed book) lab13.8% ✎▄····▄·▄█▄▄▄ beats 6/7very hard · avg 6.7%▇▇▇ 7.2%sharp
SQuAD 2.0 (prompted, F1) lab65.5% ✎██····██████ beats 6/7medium · avg 47%▇▇ 5.7%sharp · at its level
HellaSwag lab76.9% ✎▃··▃···▃·▃▃█ beats 7/7medium · avg 62%▇▇ 5.7%sharp · at its level
RACE-high lab47.2% ✎▃▃······▅·▃█ beats 7/7hard · avg 39%▇▇ 4.2%at its level
21 more tests
testresultvs the others in its rundifficultycountswhy
BoolQ lab78.0% ✎█···▃··▃▃·▃▃ beats 6/7hard · avg 65%▇ 3.3%sharp · at its level · noisy
COPA lab90.0% ✎▄·▄···▄·▄█·█ beats 6/7easy · avg 83%▇ 3.1%typical
Winograd Schema Challenge (WSC273) lab84.3% ✎█·····▄▄▄▄▄▄ beats 6/7medium · avg 79%▇ 2.8%at its level
Story Cloze Test lab81.1% ✎▄·▄····▄▄▄▄█ beats 6/7medium · avg 75%▇ 2.8%at its level
SuperGLUE WSC (prompted) lab82.8% ✎█·····▄·▄█▄▄ beats 6/7medium · avg 77%▇ 2.7%at its level
RACE-middle lab67.8% ✎▄▄····▄▄·▄█▄ beats 7/7medium · avg 56%▇ 2.6%at its level
DROP lab21.8% ✎▄·▄██▄·····▄ beats 6/7hard · avg 16%▇ 2.3%sharp · at its level · noisy
ARC Easy lab71.7% ✎▄··▄···▄·█▄█ beats 7/7medium · avg 63%▇ 2.2%at its level
CoQA lab72.1% ✎▃▃·····█▃▃·▃ beats 6/7medium · avg 64%▇ 1.9%at its level
RTE (prompted) lab50.5% ✎█▄··▄····▄▄█ beats 2/7very hard · avg 55%▇ 1.8%sharp · noisy
OpenBookQA lab52.0% ✎▄·▄····█·█·█ beats 6/7hard · avg 46%▇ 1.8%at its level
PIQA lab78.5% ✎▃···▃··▃·▃█▃ beats 5/7medium · avg 75%▇ 1.6%at its level
LAMBADA (OpenAI version), last-word accuracy lab70.9% ✎▄▄·····▄·█▄█ beats 7/7medium · avg 60%▇ 1.6%at its level
ReCoRD (SuperGLUE, prompted) lab90.3% ✎▄·▄·····▄▄██ beats 6/7very easy · avg 86%▇ 1.4%typical
ANLI Round 3 (prompted) lab34.6% ✎▄·▄▄██·····▄ beats 3/7very hard · avg 35%▇ 1.1%sharp · too hard for it · noisy
QuAC (prompted, F1) lab38.3% ✎█···▄·█▄··▄▄ beats 6/7hard · avg 32%▇ 0.7%blunt · at its level
ANLI Round 1 (prompted) lab39.4% ✎█·▄▄·▄··▄··█ beats 7/7very hard · avg 35%▇ 0.3%too hard for it · noisy
ANLI Round 2 (prompted) lab35.7% ✎█▄·····█·▄·█ beats 5/7very hard · avg 35%▇ 0.1%blunt · too hard for it · noisy
MultiRC (SuperGLUE, prompted, F1a) lab54.8% ✎▃···▅▃█····▃ beats 3/7medium · avg 56%▇ 0.0%blunt · at its level · noisy
CommitmentBank (prompted) lab42.9% ✎█▄▄█·▄·····▄ beats 4/7medium · avg 43%▇ 0.0%blunt · at its level · noisy
WiC (prompted) lab50.6% ✎█▄·······▃·▂ beats 6/7very hard · avg 50%▇ 0.0%blunt · too hard for it · noisy

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · GLaM: Efficient Scaling of La… 99%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The 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's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts — authors' own runs · lab-reported29CC BY 4.0
prior · pull toward the middle
weighted average= 63.2

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