[whatmodel.sh](../../README.md) / Gemini 2.5 Pro Preview (06-05) # Gemini 2.5 Pro Preview (06-05) Google · released 2025-06 · proprietary **score 69.4** ±1.5 · comparison only: no current run option verified · from 6 benchmark results across 6 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Gemini 2.5 Pro Preview (06-05) among every model on that task or test, worst to best. 
task    vs the others        rank  counts  tests
coding  ▁▁▁▃▄▆█▇●▄▃▁   #93 of 539     60%      3
coding · #93 of 539 · 60% of its score Beats [Grok 4.20 (0309 reasoning)](grok-4-20.md), [GPT-5.4 mini](gpt-5-4-mini.md), [GPT-5.1](gpt-5-1.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [GLM-4.7](glm-4-7.md), [Gemini 3.5 Flash-Lite](gemini-3-5-flash-lite.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty            counts  why
Aider polyglot (225 exercises, tw…   83.1%  ▂·▂▃▅▂▃█▆▄▂● beats 47/49  very hard · avg 7.6%     38%  sharp
LiveCodeBench official (Aug 1, 20…   73.6%  ▃▃▅▃··▃▅█▅●▃ beats 13/15  hard · avg 24%           12%  sharp · at its level
WebApp1K-Duo-React pass@1 (onekq-…   70.4%  ▁··▁·▁▁▁▆█●█ beats 31/45  hard · avg 29%           10%  typical
other tests (3) 
test                                result  vs the others in its run    difficulty           counts  why
GPQA Diamond                         84.8%  ▁▁▁▁▂▄▆▄▅●█▅ beats 145/211  hard · avg 39%          25%  sharp
EQ-Bench 3 (emotional intelligenc…  1304.7  ▁·▂▁▄▅▅▃●▄▁▁ beats 47/62    avg 1034                14%  typical
Vectara hallucination leaderboard…   97.4%  ▁▁▂▃▄▄█▆●▂▂▁ beats 113/140  very easy · avg 94%    0.6%  blunt · maxed out · 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
Aider polyglot benchmark runs                   1   38% ▇▇▇▇▇
Epoch AI                                        1   25% ▇▇▇
EQ-Bench 3 canonical leaderboard results        1   14% ▇▇
LiveCodeBench official website results          1   12% ▇
WebApp1K models leaderboard                     1   10% ▇
Vectara hallucination leaderboard               1    1% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 69.4
Licences: Aider polyglot benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · LiveCodeBench official website results ([CC-BY-SA-4.0](../../licenses/lcb-sharealike-NOTICE.txt)) · WebApp1K models leaderboard ([MIT](../../licenses/webapp1k-leaderboard-MIT.txt)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/gemini-2-5-pro-preview-06-05) · [JSON](https://whatmodel.sh/api?format=json&all=1)