whatmodel.sh / GPT-3 Medium (350M, Brown et al. 2020 paper model)

GPT-3 Medium (350M, Brown et al. 2020 paper model)

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GPT-3 Medium (350M, Brown et al. 2020 paper model) is a closed-weights language model from OpenAI, released May 2020. Its published size is 0.35B parameters. Licence: proprietary.

Catalogue identity notes

The research checkpoint 'GPT-3 Medium' of OpenAI's GPT-3 paper (Brown et al., arXiv:2005.14165, Table 2.1: 0.35B 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 ↗

34.9±2.3comparison 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 Medium (350M, 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
HellaSwag lab43.6% ✎█··█·██·████ beats 1/7medium · avg 57%▇▇▇▇▇▇▇ 17%sharp
TriviaQA lab7.6% ✎█·█·██·███·█ beats 1/7hard · avg 22%▇▇▇▇▇▇ 15%sharp
ARC Challenge lab29.5% ✎█···██·█████ beats 1/7very hard · avg 35%▇▇▇▇ 9.2%sharp · too hard for it
WinoGrande lab52.1% ✎█·····█·▄▄·█ beats 1/7hard · avg 59%▇▇▇ 8.3%sharp · too hard for it
COPA lab68.0% ✎█··▄█·▄▄···▄ beats 1/7medium · avg 77%▇▇▇ 8.0%at its level
Story Cloze Test lab68.5% ✎▄··▄·▄▄·█▄·▄ beats 1/7medium · avg 74%▇▇▇ 7.4%at its level
Natural Questions (closed book) lab1.8% ✎▄··▄·▄·▄█▄·▄ beats 1/7very hard · avg 4.0%▇▇▇ 6.8%sharp · too hard for it
LAMBADA (OpenAI version), last-word accuracy lab54.3% ✎█··█·█·█████ beats 1/7medium · avg 64%▇▇▇ 6.7%at its level
5 more tests
testresultvs the others in its rundifficultycountswhy
ARC Easy lab46.5% ✎██··██·███·█ beats 1/7medium · avg 55%▇▇ 5.7%at its level
PIQA lab70.2% ✎█···█·██████ beats 1/7medium · avg 74%▇▇ 5.0%at its level
OpenBookQA lab43.2% ✎▄····▄▄▄▄·▄█ beats 1/7hard · avg 48%▇▇ 4.7%typical
DROP lab13.6% ✎▄···▄▄▄▄·▄·█ beats 1/7hard · avg 16%▇ 2.8%typical
BoolQ lab60.3% ✎▃········▃██ beats 2/7hard · avg 61%▇ 1.5%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= 34.9

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Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json