whatmodel.sh / GPT-3.5 text-davinci-002

GPT-3.5 text-davinci-002

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GPT-3.5 text-davinci-002 is a closed-weights language model from OpenAI, released March 2022. Licence: proprietary.

Catalogue identity notes

Comparison-only. OpenAI completions model text-davinci-002 (InstructGPT-era GPT-3.5). OpenAI's deprecations page lists its shutdown on 2024-01-04. Release month from the snapshot date in the model name (text-davinci-002: March 2022 API release; text-davinci-003: 2022-11-28).

39.5±4.8comparison only: no current run option verifiedfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of GPT-3.5 text-davinci-002, out of 1132 with a score, then more from OpenAI.

#modelscorewhereRAMreleased
879Stable LM 2 Zephyr 1.6B Stability AI40±7local~2.5GB2024-01
880LLaVA-v1.6-34B LLaVA40±2local~24GB2024-01
881GPT-3.5 text-davinci-002 OpenAI40±5comparison only2022-03
882Bielik 1.5B v3.0 Instruct SpeakLeash40±3local~2.4GB2025-04
883OpenHermes 2.5 Mistral 7B Nous Research39±1local~6.1GB2023-10
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

other tests1 not in a task · 90% of its score
testresultvs the others in its rundifficultycountswhy
EQ-Bench v1 (60 questions, paper run)39.4% ✎▂··▄·▂▆▄▂█·▂ beats 6/14medium · avg 57%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 90%at its level

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · EQ-Bench: An Emotional Intell… 90%

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
EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models — authors' own runs1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 90%CC BY 4.0
prior · pull toward the middle▇▇▇ 10%
weighted average= 39.5

$ 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