whatmodel.sh / GPT-3 text-davinci-001

GPT-3 text-davinci-001

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GPT-3 text-davinci-001 is a closed-weights language model from OpenAI, released January 2022. Licence: proprietary.

Catalogue identity notes

OpenAI InstructGPT-series completion model 'text-davinci-001'; retired by OpenAI in January 2024. Not text-davinci-002/003 and not the base davinci.

26.4±2.5comparison only: no current run option verifiedfrom 3 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of GPT-3 text-davinci-001, out of 828 with a score, then more from OpenAI.

#modelscorewhereRAMreleased
767EuroLLM 1.7B Instruct UTTER (EuroLLM)27±3local~2.4GB2024-08
768Qwen2.5 0.5B Instruct Alibaba27±2local~1.8GB2024-09
769GPT-3 text-davinci-001 OpenAI26±3comparison only2022-01
770GPT4All-13B-Snoozy Nomic AI26±2local~56GB2023-04
771OPT-13B (13 billion parameters) Meta26±1local~29GB2022-05
more from OpenAI
1GPT-6 Astra OpenAI87±1api $20.00/M2026-09
3GPT-6.1 Sol OpenAI85±1api $4.00/M2026-09
7GPT-6 Sol OpenAI83±1api $4.00/M2026-09

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▃▁▁▃▅▄▆█▇▇▇▃ #458 of 463 * · 78% of its score · 2 tests

Trails Gemma 3 270M IT, SmolLM2 360M Instruct, OpenAssistant SFT-4 Pythia 12B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled9.0% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 19/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 44%too hard for it
AlpacaEval 1.0 win rate (vs text-davinci-003, GPT-4 judge)15.2% ✎▃▂·▆·▇▃▆█▆▄▂ beats 0/45very easy · avg 96%▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 35%sharp · at its level · noisy
other tests1 not in a task · 19% of its score
testresultvs the others in its rundifficultycountswhy
EQ-Bench v1 (60 questions, paper run)15.2% ✎▃··▄·▂▆▄▂█·▂ beats 0/14medium · avg 57%▇▇▇▇▇▇▇▇ 19%typical

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 78%, EQ-Bench: An Emotional Intell… 19%

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
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 78%Apache-2.0
EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models — authors' own runs1▇▇▇▇▇▇ 19%CC BY 4.0
prior · pull toward the middle▇ 3%
weighted average= 26.4

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

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