[whatmodel.sh](../../README.md) / GPT-3 text-davinci-001
# GPT-3 text-davinci-001
OpenAI · released 2022-01 · proprietary
**score 33.8** ±1.1 · comparison only: no current run option verified · from 20 benchmark results across 11 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks GPT-3 text-davinci-001 among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ●▁▁▃▅▄▇█▇▇▇▃ #469 of 474* 18% 2
math ▁▁▃●██▅█▃▁▁▁ #421 of 530* 10% 1
chat & writing · #469 of 474 · 18% of its score
Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [OpenAssistant SFT-4 Pythia 12B](oasst-sft-4-pythia-12b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 9.0% ▁▂▂●▆▆█▆▄▄▁▁ beats 19/122 medium · avg 35% 15% sharp · too hard for it
AlpacaEval 1.0 win rate (vs text-… 15.2% ●▂·▆·▇▃▆█▆▄▂ beats 0/45 very easy · avg 94% 3.0% sharp · at its level · noisy
math · #421 of 530 · 10% of its score
Beats [Athene-70B](athene-70b.md), [GPT-3.5 Turbo (0125)](gpt-3-5-turbo-0125.md), [DBRX Instruct](dbrx-instruct.md) here, though they rank higher overall. Trails [OPT-2.7B](opt-2-7b.md), [Gemma 2B (base)](gemma-2b-base.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K 26.5% ▂▂▂▂▃●▅▇██▆▄ beats 55/206 easy · avg 76% 10% sharp · at its level · small run
other tests (17)
test result vs the others in its run difficulty counts why
New Yorker Caption Contest matchi… 35.6% █·····●··▅·▃ beats 3/6 medium · avg 71% 15% sharp
ARC Challenge 46.2% ▄▁▁▂▁▄●▇▃▂▃▂ beats 74/140 easy · avg 78% 14% sharp · at its level
BoolQ 64.2% ▁····▁▂●▆▅▆▄ beats 34/103 medium · avg 78% 4.9% at its level
OpenBookQA 52.8% ▅▁▁▁▂▂▃●▃▁▃▁ beats 77/107 easy · avg 77% 4.7% at its level
HellaSwag 77.6% ▁▁▁▂▂▂▄▆█●▄▂ beats 102/135 very easy · avg 90% 4.3% at its level
EQ-Bench v1 (60 questions, paper … 15.2% ●··▄·▂▆▄▂█·▂ beats 0/14 medium · avg 53% 4.2% at its level
WinoGrande 66.3% ▁▁▁▁▂▁▁▅●█▃▁ beats 69/131 easy · avg 86% 4.1% at its level
COPA 83.0% ▁·▁▁▁▅▅●▆▂▁▁ beats 35/67 easy · avg 92% 3.4% at its level
Natural Questions (closed book) 4.8% ▂▁▁▁▂●▄▃▅█▅▁ beats 14/68 very hard · avg 13% 2.9% too hard for it
CSK-PN boolean commonsense QA (10… 80.6% ▃·▃·····●▃▃█ beats 2/7 easy · avg 87% 2.4% blunt · at its level
ARC Easy 67.0% ▁▃▂▄▆●▅▃▁··▁ beats 50/100 easy · avg 81% 2.3% blunt · at its level
Super-NaturalInstructions English… 52.1% █····█··●··█ beats 2/3 easy · avg 71% 2.3% at its level
SelfAware unanswerable-question d… 49.6% █●▄▄·······▄ beats 2/5 medium · avg 63% 1.7% blunt · at its level
Super-NaturalInstructions English… 40.8% ▄·▄······▄▄● beats 4/5 medium · avg 63% 1.5% at its level · noisy · small run
PIQA 79.1% ▁▁▁▂▂▃▅●▅▃▁▁ beats 85/123 easy · avg 86% 1.4% blunt · at its level
SummEdits factual-consistency det… 51.9% ●····▄·█···▄ beats 0/4 hard · avg 65% 1.3% noisy
LMentry accuracy (zero-shot) 63.1% ▂▆·▄▄█▂·●··▂ beats 13/14 medium · avg 65% 0.8% blunt · at its level
source results share of the score
UPRISE: Universal Prompt Retrieval for … 9 42% ▇▇▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards 2 18% ▇▇
Do Androids Laugh at Electric Sheep? Hu… 1 15% ▇▇
PAL: Program-aided Language Models 1 10% ▇
EQ-Bench: An Emotional Intelligence Ben… 1 4% ▇
Say What You Mean! Large Language Model… 1 2% ▇
Super-NaturalInstructions: Generalizati… 1 2% ▇
Do Large Language Models Know What They… 1 2% ▇
Self-Instruct: Aligning Language Models… 1 1% ▇
SummEdits: Measuring LLM Ability at Fac… 1 1% ▇
LMentry: A Language Model Benchmark of … 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 33.8
Licences: UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-758-CC-BY-4-0.txt)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Do Androids Laugh at Electric Sheep? Humor \`\`Understanding'' Benchmarks from The New Yorker Caption Contest ([CC BY 4.0](../../licenses/acl-2023-acl-long-41-CC-BY-4-0.txt)) · PAL: Program-aided Language Models ([CC0 1.0](../../licenses/paper-2211.10435v2-pal-CC0-1-0.txt)) · EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models ([CC BY 4.0](../../licenses/paper-2312.06281v2-CC-BY-4.0.txt)) · Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledge ([CC BY 4.0](../../licenses/acl-2023-acl-long-550-CC-BY-4-0.txt)) · Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks ([CC BY 4.0](../../licenses/acl-2022-emnlp-main-340-CC-BY-4-0.txt)) · Do Large Language Models Know What They Don't Know? ([CC BY 4.0](../../licenses/acl-2023-findings-acl-551-CC-BY-4-0.txt)) · Self-Instruct: Aligning Language Models with Self-Generated Instructions ([CC BY 4.0](../../licenses/acl-2023-acl-long-754-CC-BY-4-0.txt)) · SummEdits: Measuring LLM Ability at Factual Reasoning Through The Lens of Summarization ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-600-CC-BY-4-0.txt)) · LMentry: A Language Model Benchmark of Elementary Language Tasks ([CC BY 4.0](../../licenses/acl-2023-findings-acl-666-CC-BY-4-0.txt))
## How to run it
No verified API or local download yet.
[interactive version](https://whatmodel.sh/model/text-davinci-001) · [JSON](https://whatmodel.sh/api?format=json&all=1)