whatmodel.sh / Flan-T5 Base

Flan-T5 Base

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Flan-T5 Base is an open-weights language model from Google, released October 2022. Its published size is 0.248B parameters. Licence: apache-2.0.

Catalogue identity notes

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

20.1±2.5#690 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

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

#modelscorewhereRAMreleased
794ChatGLM-6B Zhipu AI (THUDM)20±2local~16GB2023-03
795Pythia 2.8B (deduped) EleutherAI20±1local~3.4GB2023-02
796Flan-T5 Base Google20±3local~2.5GB2022-10
797Falcon-RW 1B TII20±2local~4.3GB2023-04
798OPT-2.7B Meta19±1local~7.1GB2022-05
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██·█···█···█ #4 of 5 * · 19% of its score · 1 test
testresultvs the others in its rundifficultycountswhy
MGSM lab0.4% ✎▃·▁·▁··▂▄█▄▄ beats 0/26hard · avg 22%▇▇▇▇▇▇▇ 19%too hard for it
other tests2 not in a task · 79% of its score
testresultvs the others in its rundifficultycountswhy
BIG-Bench Hard lab31.3% ✎▁▁▁▁▃▄▅▅█▃▄▁ beats 8/129easy · avg 80%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 44%sharp · too hard for it
MMLU lab35.9% ✎▁▁▁▁▁▃▃▅█▃▆▃ beats 12/183easy · avg 82%▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 35%typical

$ where to run it

Other ways to run it

  1. Run it as published · needs ~0.99 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-base')('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… 97%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 97%CC BY 4.0
prior · pull toward the middle▇ 3%
weighted average= 20.1

$ 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