[whatmodel.sh](../../README.md) / Gemini 2.0 Flash (001)
# Gemini 2.0 Flash (001)
Google · released 2025-02 · proprietary
**score 60.2** ±0.9 · comparison only: no current run option verified · from 25 benchmark results across 13 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Gemini 2.0 Flash (001) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▂▆●▄▅▄▄▃▁▁ #236 of 354 8.1% 3
chat & writing ▁▁▁▃▅▄▇█●▇▇▃ #180 of 474* 9.1% 1
math ▁▁▃▆██●█▃▁▁▁ #210 of 530* 22% 2
vision & documents ▁▁▂▃▂▃▅●▆██▄ #111 of 189* 6.1% 1
web search ▂▁▂▆▅█▅▄●▂▁▁ #10 of 96* 5.0% 1
agent tasks · #236 of 354 · 8.1% of its score
Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [DeepSeek R1 (Jan '25)](deepseek-r1.md) here, though they rank higher overall. 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
Finance Agent Benchmark v1 (537 q… 14.4% █·▃▃·█●▅▅▅▃▅ beats 9/18 very hard · avg 5.5% 5.0% sharp
BFCL v3 61.2% ▁▁▁▁▁▃▅█●▄▁▁ beats 84/114 medium · avg 51% 1.9% blunt · at its level
Galileo Agent Leaderboard v1 (too… 93.8% ▃▃▃▃▆▅▆▅█▂●▂ beats 29/31 easy · avg 78% 1.2% typical
chat & writing · #180 of 474 · 9.1% of its score
Beats [INTELLECT-3](intellect-3.md), [MiniMax-M2](minimax-m2.md), [Nemotron 3 Super 120B-A12B](nemotron-3-super-120b-a12b.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [LFM2.5 2.6B](lfm2-5-2-6b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1360.0 ▁▁▁▂▃▃▃▆●▇█▃ beats 204/385 avg 1260 9.1% blunt
math · #210 of 530 · 22% of its score
Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 0324](deepseek-v3-0324.md) here, though they rank higher overall. Trails [Phi-4-mini-reasoning 3.8B](phi-4-mini-reasoning.md), [OpenThinker3 7B](openthinker3-7b.md), [Olmo 3 7B Think](olmo-3-7b-think.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH level 5 82.2% ▂▂▅▆▄█▆▃●▂▃▄ beats 68/92 hard · avg 28% 14% sharp
OTIS mock AIME 2024–2025 31.1% ▃▃▄▃▃●▇▇█▃▂▅ beats 67/197 very hard · avg 7.2% 7.4% sharp · at its level · noisy
vision & documents · #111 of 189 · 6.1% of its score
Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GLM-4.6V](glm-4-6v.md), [GPT-5 nano](gpt-5-nano.md) here, though they rank higher overall. Trails [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Qwen3.5 4B](qwen3-5-4b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena vision (style controlled) 1172.0 ▁▁▂▂▂▂▃▄●▅█▇ beats 58/152 avg 1061 6.1% blunt
web search · #10 of 96 · 5.0% of its score
Beats [GPT-6 Astra](gpt-6-astra.md), [Claude Opus 5.5](claude-opus-5-5.md), [GPT-6.1 Sol](gpt-6-1-sol.md) here, though they rank higher overall. Trails [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Finance Agent Benchmark v1 (537 q… 14.4% █·▃▃·█●▅▅▅▃▅ beats 9/18 very hard · avg 5.5% 5.0% sharp
other tests (18)
test result vs the others in its run difficulty counts why
GPQA Diamond 64.1% ▁▁▁▁▂▄▆●▅▇█▅ beats 92/211 hard · avg 39% 15% sharp · at its level
AHELM Speech Robust Bench (LibriS… 17.8% ▂·█··▆●·▂▂·▄ beats 8/13 avg 34% 6.5% typical
BRIDGE clinical text, zero-shot a… 43.0% ▁▁▁▁▁▂▂▃▄▆█● beats 88/97 hard · avg 31% 5.7% blunt · at its level
EQ-Bench 3 (emotional intelligenc… 1096.6 ▁·▂▁●▅▅▃█▄▁▁ beats 12/62 avg 1034 5.5% typical
CPTUB average (SpeakLeash, 0-5 ju… 85.8% ▁▂▂▂▃█▃▆▅▄▂● beats 90/95 medium · avg 65% 4.9% typical
Open Persian LLM Leaderboard v2 a… 73.8% ▂▄▂▃█▄▃▆▃▅●▂ beats 77/81 medium · avg 57% 3.7% blunt · at its level
AHELM CoRe-Bench conversational a… 75.6% ▂·▄··▂▂·▄▂●▄ beats 10/13 medium · avg 52% 2.7% at its level
AHELM VoxCeleb2 same-speaker dete… 52.9% ▂·▂·▂●▂▄▄··▄ beats 5/13 very hard · avg 54% 2.0% sharp · noisy
DiagnosticIQ industrial maintenan… 57.6% ▁··▃▁▁▁●█▅▁▅ beats 7/24 hard · avg 51% 1.9% blunt · at its level
AHELM LibriSpeech transcription b… 4.3% ▃···▃·█▃▅▃▅● beats 12/13 avg 11% 1.6% typical
Open PT-LLM Leaderboard average (… 82.3% ▃▁▁▁▁▁▁▁▂█▅● beats 268/276 medium · avg 72% 1.6% noisy
AHELM MUStARD audio sarcasm detec… 60.4% ▂·······▆█●▆ beats 9/13 very hard · avg 57% 1.4% blunt
AHELM AIR-Bench Foundation music … 69.7% ▃█▃▅▃·▃·▃●▃▃ beats 10/13 medium · avg 57% 0.6% blunt · at its level
AHELM MELD audio emotion classifi… 42.3% ▃··▃▃▃·●▅▅█▅ beats 4/13 medium · avg 39% 0.5% blunt · at its level
Vectara hallucination leaderboard… 99.3% ▁▁▂▃▄▄█▆▃▂▂● beats 140/140 very easy · avg 94% 0.4% blunt · maxed out · noisy
AHELM FLEURS 7-language transcrip… 64.8% ▄▂···▂●▂█▂·▂ beats 5/13 avg 83% 0.3% blunt · noisy
AHELM Multilingual LibriSpeech 7-… 6.0% ▂▂▂··▂▂·▂▂█● beats 12/13 avg 14% 0.1% blunt · noisy
AHELM VocalSound human-sound reco… 71.9% ██▄▄·●·█·█▄▄ beats 7/13 easy · avg 67% 0.0% blunt · at its level · noisy
source results share of the score
Epoch AI 3 37% ▇▇▇▇
AHELM: A Holistic Evaluation of Audio-L… 10 16% ▇▇
LMArena leaderboard dataset 2 15% ▇▇
BRIDGE Medical Leaderboard 1 6% ▇
EQ-Bench 3 canonical leaderboard results 1 6% ▇
Finance Agent Benchmark 1 5% ▇
SpeakLeash CPTUB 1 5% ▇
Open Persian LLM Leaderboard v2 1 4% ▇
DiagnosticIQ: A Benchmark for LLM-Based… 1 2% ▇
Berkeley Function Calling Leaderboard r… 1 2% ▇
Open Portuguese LLM Leaderboard 1 2% ▇
Galileo Agent Leaderboard 1 1% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 60.2
Licences: 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/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · BRIDGE Medical Leaderboard ([MIT](../../licenses/bridge-medical-leaderboard-MIT.txt)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · Finance Agent Benchmark ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules ([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)) · Galileo Agent Leaderboard ([Apache-2.0](../../licenses/galileo-agent-leaderboard-Apache-2.0.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-2-0-flash-001) · [JSON](https://whatmodel.sh/api?format=json&all=1)