whatmodel.sh / GPT-3 Large (760M, Brown et al. 2020 paper model)

GPT-3 Large (760M, Brown et al. 2020 paper model)

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GPT-3 Large (760M, Brown et al. 2020 paper model) is a closed-weights language model from OpenAI, released May 2020. Its published size is 0.76B parameters. Licence: proprietary.

Catalogue identity notes

The research checkpoint 'GPT-3 Large' of OpenAI's GPT-3 paper (Brown et al., arXiv:2005.14165, Table 2.1: 0.76B parameters, 300B training tokens). Weights never released. Deliberately NOT merged with the API engines (gpt-3-ada/babbage/curie/davinci): OpenAI never officially stated which paper size each API engine is (AI21's Jurassic-1 white paper calls the mapping an estimate and notes the API may be 'a slightly different set of models'). Comparison-only.

Model information sources

Release date ↗

43.8±2.0comparison only: no current run option verifiedfrom 13 benchmark results across 1 source

Score within the ARC Challenge comparison set only.

$ how it compares

Scored LLMs released closest to GPT-3 Large (760M, Brown et al. 2020 paper model), out of 1182 with a score, then more from OpenAI.

#modelscorewhereRAMreleased
1063GPT-3 davinci (API base model) OpenAI32±1comparison only2020-06
1116GPT-3 curie (API base model) OpenAI25±1comparison only2020-06
1151GPT-3 babbage (API base model) OpenAI16±1comparison only2020-06
1166GPT-3 ada (API base model) OpenAI8±1comparison only2020-06
1168GPT-2 XL (1.5B) OpenAI7±1local~2.8GB2019-11
more from OpenAI
1GPT-6 Astra OpenAI86±1api $20.00/M2026-09
3GPT-6.1 Sol OpenAI84±1api $4.00/M2026-09
8GPT-6 Sol OpenAI81±1api $4.00/M2026-09

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests13 not in a task · 98% of its score
testresultvs the others in its rundifficultycountswhy
TriviaQA lab14.0% ✎█·█·██·███·█ beats 2/7hard · avg 22%▇▇▇▇▇▇▇▇ 19%sharp
HellaSwag lab51.0% ✎█··█·██·████ beats 2/7medium · avg 57%▇▇▇▇▇▇ 16%sharp · at its level
ARC Challenge lab31.8% ✎█···██·█████ beats 2/7very hard · avg 35%▇▇▇▇ 11%sharp
WinoGrande lab57.4% ✎█·····█·▄▄·█ beats 2/7hard · avg 59%▇▇▇▇ 10%sharp
Natural Questions (closed book) lab2.7% ✎▄··▄·▄·▄█▄·▄ beats 2/7very hard · avg 4.0%▇▇▇ 8.7%sharp · too hard for it
COPA lab73.0% ✎█··▄█·▄▄···▄ beats 2/7medium · avg 77%▇▇▇ 6.5%at its level
Story Cloze Test lab72.4% ✎▄··▄·▄▄·█▄·▄ beats 2/7medium · avg 74%▇▇ 6.1%at its level
LAMBADA (OpenAI version), last-word accuracy lab60.4% ✎█··█·█·█████ beats 2/7medium · avg 64%▇▇ 5.0%at its level
5 more tests
testresultvs the others in its rundifficultycountswhy
ARC Easy lab53.0% ✎██··██·███·█ beats 2/7medium · avg 55%▇▇ 4.9%at its level
OpenBookQA lab45.2% ✎▄····▄▄▄▄·▄█ beats 2/7hard · avg 48%▇▇ 4.1%at its level
PIQA lab72.9% ✎█···█·██████ beats 2/7medium · avg 74%▇▇ 4.1%at its level
DROP lab14.4% ✎▄···▄▄▄▄·▄·█ beats 2/7hard · avg 16%▇ 2.5%typical
BoolQ lab58.9% ✎▃········▃██ beats 1/7hard · avg 61%▇ 1.4%noisy

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · Language Models are Few-Shot… 98%

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
Language Models are Few-Shot Learners — maker's own results · lab-reported13Factual vendor-reported score (not licensed content)
prior · pull toward the middle
weighted average= 43.8

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data updated · sources and licences · json