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Minerva 62B

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Minerva 62B is a closed-weights language model from Google, released June 2022. Its published size is 62.5B parameters. Licence: proprietary.

Catalogue identity notes

Google's Minerva 62B: PaLM further trained on mathematical web pages and arXiv (Lewkowycz et al., arXiv:2206.14858, Table 2). Never released. Comparison-only.

Model information sources

Release date ↗

46.4±1.7comparison 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 Minerva 62B, out of 877 with a score, then more from Google.

#modelscorewhereRAMreleased
538DeepSeek-Coder-V2 Lite Instruct (16B-A2.4B) DeepSeek46±1local~9.6GB2024-06
539Mistral Small (2402) Mistral46±1comparison only2024-02
540Minerva 62B Google46±2never released2022-06
541Llama 3.1 8B Instruct Meta46±0api $0.22/M +local~6.2GB2024-07
542Ministral 3 3B Mistral46±1api $0.10/M +local~3.4GB2025-12
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▁▂▃▆█▇▅▇▃▁▁▁ #270 of 523 * · 66% of its score · 2 tests

Beats GPT-4o (2024-08-06), Command A (03-2025), GPT-4o (2024-05-13) here, though they rank higher overall. Trails Gemma 7B (base), CodeGemma 7B (v1.0), Phi-2 (2.7B) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
GSM8K lab52.4% ✎▃····▃··▃··█ beats 3/5easy · avg 66%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 55%sharp · at its level
Minerva MATH lab27.6% ✎▄··▄··▄·▄··█ beats 4/5hard · avg 32%▇▇▇▇ 10%at its level
other tests2 not in a task · 33% of its score
testresultvs the others in its rundifficultycountswhy
MMLU-STEM (Minerva subset) lab53.9% ✎▄······▄▄·▄█ beats 3/5medium · avg 62%▇▇▇▇▇▇▇▇▇ 23%sharp · at its level
OCWCourses (Minerva) lab12.9% ✎▄·····▄█··▄▄ beats 4/5hard · avg 15%▇▇▇▇ 10%typical

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · Solving Quantitative Reasonin… 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
Solving Quantitative Reasoning Problems with Language Models — authors' own runs · lab-reported4CC BY 4.0
prior · pull toward the middle
weighted average= 46.4

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