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GLaM dense 1.7B

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GLaM dense 1.7B is a closed-weights language model from Google, released December 2021. Its published size is 1.7B parameters. Licence: proprietary.

Catalogue identity notes

Google's GLaM 1.7B dense baseline model (Du et al., arXiv:2112.06905, Table 4: 1.7B total / 1.7B 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 ↗

47.7±1.4comparison 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 1.7B, 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

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests29 not in a task · 99% of its score
testresultvs the others in its rundifficultycountswhy
TriviaQA lab27.0% ✎▄··▄···▄·█▄█ beats 2/7hard · avg 35%▇▇▇▇▇▇ 15%sharp · at its level
HellaSwag lab60.6% ✎▃··▃···▃·▃▃█ beats 2/7medium · avg 62%▇▇▇ 7.8%sharp · at its level
SQuAD 2.0 (prompted, F1) lab44.9% ✎██····██████ beats 2/7medium · avg 47%▇▇▇ 7.4%sharp · at its level
Natural Questions (closed book) lab5.6% ✎▄·▄··▄·█▄·▄▄ beats 2/7very hard · avg 7.2%▇▇▇ 6.8%sharp · too hard for it
WinoGrande lab62.6% ✎▃··▃·····▃▅█ beats 2/7hard · avg 61%▇▇▇ 6.5%sharp
ARC Challenge lab35.1% ✎▂··▂·····▄·█ beats 2/7very hard · avg 35%▇▇ 6.2%sharp
COPA lab80.0% ✎▄·▄···▄·▄█·█ beats 2/7easy · avg 83%▇▇ 4.9%at its level
WebQuestions (closed book) lab5.9% ✎▄····▄·▄█▄▄▄ beats 2/7very hard · avg 6.7%▇▇ 4.6%sharp · too hard for it
21 more tests
testresultvs the others in its rundifficultycountswhy
Winograd Schema Challenge (WSC273) lab78.7% ✎█·····▄▄▄▄▄▄ beats 2/7medium · avg 79%▇▇ 3.9%at its level
RACE-high lab40.0% ✎▃▃······▅·▃█ beats 2/7hard · avg 39%▇ 3.7%typical
SuperGLUE WSC (prompted) lab77.2% ✎█·····▄·▄█▄▄ beats 2/7medium · avg 77%▇ 3.6%at its level
Story Cloze Test lab75.1% ✎▄·▄····▄▄▄▄█ beats 2/7medium · avg 75%▇ 3.6%at its level
BoolQ lab56.1% ✎█···▃··▃▃·▃▃ beats 0/7hard · avg 65%▇ 3.4%sharp · at its level · noisy
RACE-middle lab53.6% ✎▄▄····▄▄·▄█▄ beats 2/7medium · avg 56%▇ 3.1%at its level
ARC Easy lab62.2% ✎▄··▄···▄·█▄█ beats 2/7medium · avg 63%▇ 2.9%at its level
CoQA lab66.5% ✎▃▃·····█▃▃·▃ beats 4/7medium · avg 64%▇ 2.8%at its level
ReCoRD (SuperGLUE, prompted) lab86.7% ✎▄·▄·····▄▄██ beats 2/7very easy · avg 86%▇ 2.4%typical
LAMBADA (OpenAI version), last-word accuracy lab60.1% ✎▄▄·····▄·█▄█ beats 2/7medium · avg 60%▇ 2.2%at its level
PIQA lab73.6% ✎▃···▃··▃·▃█▃ beats 2/7medium · avg 75%▇ 2.1%at its level
OpenBookQA lab46.7% ✎▄·▄····█·█·█ beats 3/7hard · avg 46%▇ 1.9%at its level
DROP lab14.0% ✎▄·▄██▄·····▄ beats 3/7hard · avg 16%▇ 1.8%sharp · noisy
RTE (prompted) lab49.1% ✎█▄··▄····▄▄█ beats 0/7very hard · avg 55%▇ 1.1%sharp · too hard for it · noisy
QuAC (prompted, F1) lab33.3% ✎█···▄·█▄··▄▄ beats 4/7hard · avg 32%▇ 0.8%blunt · at its level
ANLI Round 3 (prompted) lab34.1% ✎▄·▄▄██·····▄ beats 2/7very hard · avg 35%▇ 0.6%sharp · too hard for it · noisy
ANLI Round 1 (prompted) lab33.2% ✎█·▄▄·▄··▄··█ beats 0/7very hard · avg 35%▇ 0.2%too hard for it · noisy
MultiRC (SuperGLUE, prompted, F1a) lab53.0% ✎▃···▅▃█····▃ beats 2/7medium · avg 56%▇ 0.1%blunt · at its level · noisy
ANLI Round 2 (prompted) lab33.6% ✎█▄·····█·▄·█ beats 0/7very hard · avg 35%▇ 0.1%blunt · too hard for it · noisy
CommitmentBank (prompted) lab37.5% ✎█▄▄█·▄·····▄ beats 2/7medium · avg 43%▇ 0.0%blunt · at its level · noisy
WiC (prompted) lab50.3% ✎█▄·······▂·▂ beats 4/7very 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= 47.7

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