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Flan-cont-PaLM 62B

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Flan-cont-PaLM 62B is a closed-weights language model from Google, released October 2022. Its published size is 62.5B parameters. Licence: proprietary.

Catalogue identity notes

Flan instruction-finetuned cont-PaLM 62B (Chung et al., arXiv:2210.11416, Table 2). Never released. Comparison-only.

Model information sources

Release date ↗

47.1±1.4comparison only: no current run option verifiedfrom 4 benchmark results across 1 source

Score within the LMArena (text) comparison set only.

$ how it compares

The LLMs ranked either side of Flan-cont-PaLM 62B, out of 877 with a score, then more from Google.

#modelscorewhereRAMreleased
521StarCoder2-15B-Instruct-v0.1 BigCode47±1local~11GB2024-04
522Qwen2.5-Math 1.5B Instruct Alibaba47±2local~1.8GB2024-09
523Flan-cont-PaLM 62B Google47±1never released2022-10
524Qwen1.5 72B Chat Alibaba47±1local~31GB2024-02
525Granite 3.3 8B Instruct IBM47±1local~6.3GB2025-04
more from Google
11Gemini 4 Argon Google80±1api $4.00/M2026-09
26Gemini 3.8 Flash Google76±1api $1.50/M2026-09
34Gemini 3.7 Flash Google75±1api $1.50/M2026-08

see all LLMs · find the ones that fit your machine

$ what it's good at

math▁▂▃▆█▇▅▇▃▁▁▁ #242 of 523 * · 30% of its score · 1 test

Beats MiniMax-M2.1, DeepSeek V3 (Dec '24), GPT-4o (2024-08-06) here, though they rank higher overall. Trails Phi-2 (2.7B), Granite 8B Code Base (4K), Mixtral 8x7B v0.1 (base) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
MGSM lab40.3% ✎▆·▂·▄▂▂▂▂█▄▆ beats 14/18medium · avg 47%▇▇▇▇▇▇▇▇▇▇▇▇ 30%sharp · at its level
other tests3 not in a task · 69% of its score
testresultvs the others in its rundifficultycountswhy
MMLU lab66.1% ✎▁···▁··▃▁▅▅█ beats 13/18easy · avg 74%▇▇▇▇▇▇▇▇▇▇▇▇ 30%at its level
BIG-Bench Hard lab51.0% ✎▂▂▂▂·▅·▃▂█▃▃ beats 15/18medium · avg 60%▇▇▇▇▇▇▇▇▇▇▇ 28%sharp · at its level
TyDiQA-GoldP (1-shot, EM, 8-language average) lab62.7% ✎▂··▂·▂▄▄·███ beats 16/18medium · avg 61%▇▇▇▇ 11%at its level

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · Scaling Instruction-Finetuned… 99%

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-reported4CC BY 4.0
prior · pull toward the middle
weighted average= 47.1

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data updated · sources and licences · json