[whatmodel.sh](../../README.md) / Gemini 2.5 Pro Preview (05-06)
# Gemini 2.5 Pro Preview (05-06)
Google · released 2025-05 · proprietary
**score 66.2** ±1.1 · comparison only: no current run option verified · from 19 benchmark results across 8 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 (05-06) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▂▆●▄▅▄▄▃▁▁ #252 of 354* 2.2% 1
coding ▁▁▁▃▄▆█▇●▄▃▁ #111 of 539 41% 3
math ▁▁▃▆██▅●▃▁▁▁ #111 of 530* 7.4% 1
vision & documents ▁▁▂▃▂▃▅▄●██▄ #89 of 189* 1.9% 1
agent tasks · #252 of 354 · 2.2% 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 59.9% ▁▁▁▁▁▃▅█●▄▁▁ beats 82/114 medium · avg 51% 2.2% blunt · at its level
coding · #111 of 539 · 41% of its score
Beats [Grok 4.20 (0309 reasoning)](grok-4-20.md), [Grok 4.3](grok-4-3.md), [Muse Glimmer 30B](muse-glimmer-30b.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Qwen3 235B-A22B Thinking 2507](qwen3-235b-a22b-thinking-2507.md), [o4-mini](o4-mini.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Aider polyglot (225 exercises, tw… 76.9% ▂·▂▃▅▂▃█▆▄●▂ beats 44/49 very hard · avg 7.6% 27% sharp · at its level
LiveCodeBench official (Aug 1, 20… 71.8% ▃▃▅▃··▃▅█●▅▃ beats 12/15 hard · avg 24% 7.2% sharp · at its level
WebApp1K-Duo-React pass@1 (onekq-… 67.4% ▁··▁·▁▁▁▆●▇█ beats 25/45 hard · avg 29% 6.0% at its level
math · #111 of 530 · 7.4% of its score
Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [MiniMax-M3](minimax-m3.md), [Nemotron 3 Ultra 550B-A55B](nemotron-3-ultra-550b-a55b.md) here, though they rank higher overall. Trails [Gemini 2.5 Flash](gemini-2-5-flash.md), [GLM-4.5-Air](glm-4-5-air.md), [Granite 4.2 30B](granite-4-2-30b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH level 5 95.9% ▂▂▅▆▄█▆▃▅▂●▄ beats 83/92 hard · avg 28% 7.4% sharp · maxed out
vision & documents · #89 of 189 · 1.9% of its score
Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-5.4 nano](gpt-5-4-nano.md), [GPT-5 mini](gpt-5-mini.md) here, though they rank higher overall. Trails [Gemma 4 12B](gemma-4-12b.md), [Qwen3.5 9B](qwen3-5-9b.md), [GPT-4.1](gpt-4-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MSEarth-Bench Earth-science figur… 61.3% █·██····██·● beats 5/5 hard · avg 48% 1.9% blunt · at its level
other tests (13)
test result vs the others in its run difficulty counts why
GPQA Diamond 66.7% ▁▁▁▁▂▄▆▄●▇█▅ beats 99/211 hard · avg 39% 17% sharp · at its level
AHELM Speech Robust Bench (LibriS… 3.9% ▂·█··▆▄·▂▂·● beats 13/13 avg 34% 8.3% typical
AraGen-v3 3C3H score (Inception, … 68.4% ▁▁·▁▂▂▄█▄▅●▄ beats 54/66 hard · avg 29% 6.0% at its level
HindiGen-v1 3C3H score (Inception… 71.8% ▁▁▁▁·▂·█▆●▇▁ beats 21/31 medium · avg 46% 4.3% typical
AHELM VoxCeleb2 same-speaker dete… 75.1% ▂·▂·▂█▂▄▄··● beats 12/13 very hard · avg 54% 3.4% sharp · at its level · noisy
AHELM CoRe-Bench conversational a… 81.3% ▂·▄··▂▂·▄▂█● beats 13/13 medium · avg 52% 2.6% typical
AHELM MUStARD audio sarcasm detec… 65.5% ▂·······▆█▆● beats 12/13 very hard · avg 57% 2.1% blunt · at its level
AHELM LibriSpeech transcription b… 3.9% ▃···▃·█▃▅▃▅● beats 13/13 avg 11% 2.1% typical
AHELM AIR-Bench Foundation music … 68.3% ▃█▃▅▃·▃·●▅▃▃ beats 9/13 medium · avg 57% 0.7% blunt · at its level
AHELM MELD audio emotion classifi… 47.3% ▃··▃▃▃·▃▅▅●▅ beats 10/13 medium · avg 39% 0.6% blunt · at its level
AHELM FLEURS 7-language transcrip… 21.1% ▄▂···▂▆▂█▂·● beats 13/13 avg 83% 0.4% blunt · noisy
AHELM Multilingual LibriSpeech 7-… 19.8% ▂▂▂··●▂·▂▂█▆ beats 3/13 avg 14% 0.1% blunt · noisy
AHELM VocalSound human-sound reco… 86.0% ██▄▄·█·█·█●▄ beats 12/13 easy · avg 67% 0.0% blunt · at its level · noisy
source results share of the score
Aider polyglot benchmark runs 1 27% ▇▇▇
Epoch AI 2 24% ▇▇▇
AHELM: A Holistic Evaluation of Audio-L… 10 20% ▇▇
Inception Arabic/Hindi leaderboards 2 10% ▇
LiveCodeBench official website results 1 7% ▇
WebApp1K models leaderboard 1 6% ▇
Berkeley Function Calling Leaderboard r… 1 2% ▇
MSEarth: A Multimodal Benchmark for Ear… 1 2% ▇
prior: pull toward the middle 1% ▇
weighted average = 66.2
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/)) · AHELM: A Holistic Evaluation of Audio-Language Models ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Inception Arabic/Hindi leaderboards ([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)) · WebApp1K models leaderboard ([MIT](../../licenses/webapp1k-leaderboard-MIT.txt)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/))
## How to run it
No verified API or local download yet.
[interactive version](https://whatmodel.sh/model/gemini-2-5-pro-preview-05-06) · [JSON](https://whatmodel.sh/api?format=json&all=1)