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GLaM dense 8B

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GLaM dense 8B is a closed-weights language model from Google, released December 2021. Its published size is 8.7B parameters. Licence: proprietary.

Catalogue identity notes

Google's GLaM 8B dense baseline model (Du et al., arXiv:2112.06905, Table 4: 8.7B total / 8.7B 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 ↗

57.4±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 8B, 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 lab48.1% ✎▄··▄···▄·█▄█ beats 4/7hard · avg 35%▇▇▇▇▇▇ 15%sharp · at its level
Natural Questions (closed book) lab9.0% ✎▄·▄··▄·█▄·▄▄ beats 3/7very hard · avg 7.2%▇▇▇▇ 10%sharp
ARC Challenge lab42.7% ✎▂··▂·····▄·█ beats 4/7very hard · avg 35%▇▇▇ 8.2%sharp
WinoGrande lab70.1% ✎▃··▃·····▃▅█ beats 5/7hard · avg 61%▇▇▇ 7.6%sharp · at its level
HellaSwag lab72.2% ✎▃··▃···▃·▃▃█ beats 4/7medium · avg 62%▇▇▇ 6.5%sharp · at its level
SQuAD 2.0 (prompted, F1) lab55.7% ✎██····██████ beats 4/7medium · avg 47%▇▇▇ 6.3%sharp · at its level
WebQuestions (closed book) lab7.7% ✎▄····▄·▄█▄▄▄ beats 3/7very hard · avg 6.7%▇▇ 6.1%sharp
RACE-high lab45.0% ✎▃▃······▅·▃█ beats 5/7hard · avg 39%▇▇ 4.0%typical
21 more tests
testresultvs the others in its rundifficultycountswhy
COPA lab86.0% ✎▄·▄···▄·▄█·█ beats 4/7easy · avg 83%▇ 3.7%at its level
BoolQ lab73.6% ✎█···▃··▃▃·▃▃ beats 5/7hard · avg 65%▇ 3.4%sharp · at its level · noisy
Winograd Schema Challenge (WSC273) lab81.6% ✎█·····▄▄▄▄▄▄ beats 4/7medium · avg 79%▇ 3.1%at its level
Story Cloze Test lab79.5% ✎▄·▄····▄▄▄▄█ beats 5/7medium · avg 75%▇ 3.0%at its level
SuperGLUE WSC (prompted) lab80.7% ✎█·····▄·▄█▄▄ beats 4/7medium · avg 77%▇ 3.0%at its level
RACE-middle lab63.0% ✎▄▄····▄▄·▄█▄ beats 5/7medium · avg 56%▇ 2.8%at its level
ARC Easy lab67.9% ✎▄··▄···▄·█▄█ beats 5/7medium · avg 63%▇ 2.4%at its level
CoQA lab68.7% ✎▃▃·····█▃▃·▃ beats 5/7medium · avg 64%▇ 2.2%at its level
DROP lab17.0% ✎▄·▄██▄·····▄ beats 5/7hard · avg 16%▇ 2.1%sharp · noisy
OpenBookQA lab49.8% ✎▄·▄····█·█·█ beats 4/7hard · avg 46%▇ 1.8%at its level
PIQA lab78.2% ✎▃···▃··▃·▃█▃ beats 4/7medium · avg 75%▇ 1.7%at its level
LAMBADA (OpenAI version), last-word accuracy lab69.3% ✎▄▄·····▄·█▄█ beats 6/7medium · avg 60%▇ 1.7%at its level
ReCoRD (SuperGLUE, prompted) lab89.2% ✎▄·▄·····▄▄██ beats 5/7very easy · avg 86%▇ 1.7%typical
RTE (prompted) lab63.8% ✎█▄··▄····▄▄█ beats 6/7very hard · avg 55%▇ 1.6%sharp · noisy
ANLI Round 3 (prompted) lab34.9% ✎▄·▄▄██·····▄ beats 6/7very hard · avg 35%▇ 0.8%sharp · too hard for it · noisy
QuAC (prompted, F1) lab30.7% ✎█···▄·█▄··▄▄ beats 2/7hard · avg 32%▇ 0.7%blunt · at its level
ANLI Round 1 (prompted) lab34.7% ✎█·▄▄·▄··▄··█ beats 4/7very hard · avg 35%▇ 0.3%too hard for it · noisy
ANLI Round 2 (prompted) lab34.8% ✎█▄·····█·▄·█ beats 3/7very hard · avg 35%▇ 0.1%blunt · too hard for it · noisy
MultiRC (SuperGLUE, prompted, F1a) lab39.0% ✎▃···▅▃█····▃ beats 0/7medium · avg 56%▇ 0.1%blunt · at its level · noisy
CommitmentBank (prompted) lab33.9% ✎█▄▄█·▄·····▄ beats 0/7medium · avg 43%▇ 0.0%blunt · at its level · noisy
WiC (prompted) lab44.0% ✎█▄·······▂·▂ beats 0/7very 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= 57.4

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