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Flan-T5 Small

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Flan-T5 Small is an open-weights language model from Google, released October 2022. Its published size is 0.077B parameters. Licence: apache-2.0.

Catalogue identity notes

Instruction-finetuned T5 v1.1 LM-adapted checkpoint released with Chung et al. 2022.

13.8±3.2#703 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of Flan-T5 Small, out of 828 with a score, then more from Google.

#modelscorewhereRAMreleased
809StableLM-Tuned-Alpha 7B Stability AI14±2local~6.9GB2023-04
810BLOOM 1.7B BigScience14±1local~3.1GB2022-07
811Flan-T5 Small Google14±3local~1.8GB2022-10
812GPT-Neo 1.3B EleutherAI14±2local~7.1GB2021-03
813GPT-2 XL (1.5B) OpenAI11±1local~2.8GB2019-11
more from Google
9Gemini 4 Argon (limited preview) Google82±1limited preview2026-09
23Gemini 3.8 Flash Google77±1api $1.50/M2026-09
32Gemini 3.7 Flash Google76±1api $1.50/M2026-08

see all LLMs · find the ones that fit your machine

$ what it's good at

math██·█···█···█ #5 of 5 * · 18% of its score · 1 test
testresultvs the others in its rundifficultycountswhy
MGSM lab0.2% ✎▃·▁·▁··▂▄█▄▄ beats 1/26hard · avg 22%▇▇▇▇▇▇▇ 18%too hard for it
other tests2 not in a task · 77% of its score
testresultvs the others in its rundifficultycountswhy
BIG-Bench Hard lab29.1% ✎▁▁▁▃▂▄▅▅█▃▄▁ beats 3/129easy · avg 80%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 39%sharp · too hard for it
MMLU lab28.7% ✎▁▁▁▃▁▂▃▅█▃▆▃ beats 5/183easy · avg 82%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 39%typical

$ where to run it

Other ways to run it

  1. Run it as published · needs ~0.31 GB · from huggingface.co/google (official)
    uv run --with torch --with 'transformers<5' python -c "from transformers import pipeline; print(pipeline('text2text-generation', model='google/flan-t5-small')('Answer the question: what is the capital of France?', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/google ↗

$ how the score was worked out · Scaling Instruction-Finetuned… 95%

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
Scaling Instruction-Finetuned Language Models — authors' own runs · lab-reported3▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 95%CC BY 4.0
prior · pull toward the middle▇ 5%
weighted average= 13.8

$ 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