[whatmodel.sh](../../README.md) / Gemini 1.0 Pro (001) # Gemini 1.0 Pro (001) Google · released 2023-12 · proprietary **score 47.1** ±1.5 · comparison only: no current run option verified · from 6 benchmark results across 4 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Gemini 1.0 Pro (001) among every model on that task or test, worst to best.}

task            vs the others          rank  counts  tests
chat & writing  ▁▁▁▃▅●▇█▇▇▇▃   #345 of 474*     44%      2
math            ▁▁▃●██▅█▃▁▁▁   #458 of 530*     32%      2

chat & writing · #345 of 474 · 44% of its score

Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Ministral 3 8B](ministral-3-8b.md), [Gemma 4 E4B](gemma-4-e4b.md) here, though they rank higher overall. Trails [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [LFM2 700M](lfm2-700m.md), [Flan-T5 XXL](flan-t5-xxl.md) here, though they rank lower overall.

test                                result  vs the others in its run   difficulty  counts  why
LMArena (text)                      1236.9  ▁▁▁▂▃●▃▆▆▇█▃ beats 86/385  avg 1260       23%  blunt
Polyrating rating (bias-aware re-…  1182.0  ▂▁▂▁▃▇█▆●▄▂▂ beats 77/95   avg 1187       21%  typical

math · #458 of 530 · 32% of its score

Trails [GPT-Neo 1.3B](gpt-neo-1-3b.md), [OPT-1.3B](opt-1-3b.md), [OPT-2.7B](opt-2-7b.md) here, though they rank lower overall.

test                      result  vs the others in its run  difficulty            counts  why
MATH level 5               11.2%  ▂▂●▆▄█▆▃▅▂▃▄ beats 7/92   hard · avg 28%           30%  sharp
OTIS mock AIME 2024–2025    1.1%  ●▃▄▃▃▃▇▇█▃▂▅ beats 9/197  very hard · avg 7.2%    2.7%  sharp · too hard for it · noisy

other tests (2)

test                                result  vs the others in its run    difficulty        counts  why
GPQA Diamond                         34.0%  ▁▁▁▁●▄▆▄▅▇█▅ beats 26/211   hard · avg 39%       17%  sharp
Open PT-LLM Leaderboard average (…   69.9%  ▃▁▁▁▁▁▁▁▂●▅▁ beats 163/276  medium · avg 72%    5.5%  at its level · noisy

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md)

source                                    results  share of the score
Epoch AI                                        3   50% ▇▇▇▇▇▇
LMArena leaderboard dataset                     1   23% ▇▇▇
Polyrating: A Cost-Effective and Bias-A…        1   21% ▇▇▇
Open Portuguese LLM Leaderboard                 1    6% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 47.1

_{Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))} ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/gemini-1-0-pro-001) · [JSON](https://whatmodel.sh/api?format=json&all=1)