[whatmodel.sh](../../README.md) / Gemini 2.5 Pro Experimental (03-25) # Gemini 2.5 Pro Experimental (03-25) Google · released 2025-03 · proprietary **score 67.4** ±2.3 · comparison only: no current run option verified · from 5 benchmark results across 5 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 Experimental (03-25) among every model on that task or test, worst to best. 
task         vs the others          rank  counts  tests
agent tasks  ▁▁▂▆●▄▅▄▄▃▁▁   #233 of 354*     11%      1
coding       ▁▁▁▃▄▆█▇▅●▃▁    #57 of 539*    4.5%      1
agent tasks · #233 of 354 · 11% of its score Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Mini](jamba-1-5-mini.md), [Mellum2 12B-A2.5B Instruct](mellum2-12b-a2-5b-instruct.md) here, though they rank lower overall. 
test     result  vs the others in its run   difficulty        counts  why
BFCL v3   62.9%  ▁▁▁▁▁▃▅█●▄▁▁ beats 88/114  medium · avg 51%     11%  blunt · at its level
coding · #57 of 539 · 4.5% of its score Beats [GPT-5.4](gpt-5-4.md), [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md), [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md) here, though they rank higher overall. 
test                                result  vs the others in its run  difficulty        counts  why
LiveCodeBench official release_v5…   80.9%  █···█····█·● beats 3/3    medium · avg 51%    4.5%  noisy
other tests (3) 
test                                result  vs the others in its run    difficulty           counts  why
GPQA Diamond                         83.8%  ▁▁▁▁▂▄▆▄▅●█▅ beats 141/211  hard · avg 39%          74%  sharp · at its level
Open PT-LLM Leaderboard average (…   88.4%  ▃▁▁▁▁▁▁▁▂█▅● beats 276/276  medium · avg 72%       6.6%  noisy
Vectara hallucination leaderboard…   98.9%  ▁▁▂▃▄▄█▆▃▂●▁ beats 136/140  very easy · avg 94%    1.6%  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
Epoch AI                                        1   74% ▇▇▇▇▇▇▇▇▇
Berkeley Function Calling Leaderboard r…        1   11% ▇
Open Portuguese LLM Leaderboard                 1    7% ▇
LiveCodeBench official website results          1    4% ▇
Vectara hallucination leaderboard               1    2% ▇
prior: pull toward the middle                        2% ▇
weighted average                                   = 67.4
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · LiveCodeBench official website results ([CC-BY-SA-4.0](../../licenses/lcb-sharealike-NOTICE.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-exp-03-25) · [JSON](https://whatmodel.sh/api?format=json&all=1)