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OPT-1.3B

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OPT-1.3B is an open-weights language model from Meta, released May 2022. Its published size is 1.3B parameters and a 2k-token context window. Licence: opt-175b-license-noncommercial.

Catalogue identity notes

Meta's Open Pre-trained Transformer base model as released on Hugging Face (weights under Meta's non-commercial OPT licence).

15.7±1.2#698 of 719 LLMsfrom 14 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of OPT-1.3B, out of 828 with a score, then more from Meta.

#modelscorewhereRAMreleased
804FastChat-T5 3B LMSYS17±2local~8.5GB2023-04
805Dolly v2 12B Databricks17±2comparison only2023-04
806OPT-1.3B Meta16±1local~4.3GB2022-05
807Gemma 3 270M IT Google15±4local~1.7GB2025-08
808Pythia 1B EleutherAI14±1local~2.2GB2023-02
more from Meta
24Muse Spark 1.3 Meta77±1api $2.00/M2026-09
40Muse Spark 1.2 Meta75±1api $2.00/M2026-08
43Muse Spark 1.1 Meta75±1api $2.00/M2026-07

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests10 not in a task · 99% of its score
testresultvs the others in its rundifficultycountswhy
ARC Easy +157.0% ✎▃·▅▃▃▅·██▃██ beats 9/19very easy · avg 90%▇▇▇▇▇▇▇▇▇ 23%at its level · several runs
WinoGrande +159.7% ✎▄····▄▆▂▄▆▆█ beats 12/19easy · avg 89%▇▇▇▇▇▇▇ 17%several runs
SciQ84.5% ✎▂··▂▃▂▂▆▃▃█▂ beats 8/19very easy · avg 98%▇▇▇▇▇ 12%typical
PIQA +171.7% ✎▂·▄▄·▄▂▆▃▄█▄ beats 11/19easy · avg 88%▇▇▇▇▇ 12%blunt · at its level · several runs
ARC Challenge +123.1% ✎█··▁·▁▁·▁▂▁▂ beats 0/19medium · avg 72%▇▇▇▇▇ 12%sharp · too hard for it · several runs
BoolQ59.6% ✎▄█▄···▄··█▄▄ beats 0/8easy · avg 92%▇▇▇ 7.5%typical
LAMBADA (OpenAI version), last-word accuracy57.9% ✎▃··▅▃▃▅▅▅███ beats 10/19very easy · avg 89%▇▇▇ 7.5%blunt · at its level
HellaSwag41.5% ✎▃▃▃··▅····▃█ beats 1/8easy · avg 83%▇▇ 5.8%typical
2 more tests
testresultvs the others in its rundifficultycountswhy
OpenBookQA24.0% ✎█······▂·▆·▂ beats 2/8medium · avg 54%▇ 1.7%too hard for it · noisy
Open PT-LLM Leaderboard average (9 Portuguese tasks)25.6% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 17/276medium · avg 73%▇ 1.0%noisy

$ where to run it

Other ways to run it

  1. Run it as published · needs ~2.63 GB · from huggingface.co/facebook
    uv run --with torch --with 'transformers<5' python -c "from transformers import pipeline; print(pipeline('text-generation', model='facebook/opt-1.3b')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Weights and documentation: huggingface.co/facebook ↗

$ how the score was worked out · Pythia: A Suite for Analyzing… 61%, Textbooks Are All You Need II… 38%, Open Portuguese LLM Leaderboa… 1%

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
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling — authors' own runs6▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 61%CC BY-SA 4.0
Textbooks Are All You Need II: phi-1.5 technical report — authors' own runs7▇▇▇▇▇▇▇▇▇▇▇ 38%CC BY 4.0
Open Portuguese LLM Leaderboard (archived)1▇ 1%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 15.7

$ improve this page

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

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data updated · sources and licences · json