[whatmodel.sh](../../README.md) / Gemini 2.0 Flash-Lite (001)
# Gemini 2.0 Flash-Lite (001)
Google · released 2025-02 · proprietary
**score 61.7** ±1.7 · comparison only: no current run option verified · from 15 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 2.0 Flash-Lite (001) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▂▆●▄▅▄▄▃▁▁ #268 of 354* 10% 2
agent tasks · #268 of 354 · 10% of its score
Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Mini](jamba-1-5-mini.md), [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL v3 56.2% ▁▁▁▁▁▃▅●▅▄▁▁ beats 63/114 medium · avg 51% 6.5% blunt · at its level
Galileo Agent Leaderboard v1 (too… 93.3% ▃▃▃▃▆▅▆▅█▂●▂ beats 28/31 easy · avg 78% 3.9% typical
other tests (13)
test result vs the others in its run difficulty counts why
AHELM Speech Robust Bench (LibriS… 4.9% ▂·█··▆▄·▂▂·● beats 12/13 avg 34% 23% typical
CPTUB average (SpeakLeash, 0-5 ju… 81.9% ▁▂▂▂▃█▃▆▅●▂▃ beats 85/95 medium · avg 65% 16% typical
Open Persian LLM Leaderboard v2 a… 69.1% ▂▄▂▃█▄▃▆▃●▂▂ beats 71/81 medium · avg 57% 12% blunt · at its level
AHELM CoRe-Bench conversational a… 73.7% ▂·▄··▂▂·▄▂●▄ beats 8/13 medium · avg 52% 8.8% typical
AHELM VoxCeleb2 same-speaker dete… 52.7% ▂·▂·▂●▂▄▄··▄ beats 4/13 very hard · avg 54% 7.6% sharp · noisy
AHELM LibriSpeech transcription b… 4.9% ▃···▃·█▃▅▃●█ beats 10/13 avg 11% 5.6% typical
AHELM MUStARD audio sarcasm detec… 66.1% ▂·······▆█▆● beats 13/13 very hard · avg 57% 5.2% blunt
Open PT-LLM Leaderboard average (… 80.6% ▃▁▁▁▁▁▁▁▂█●▁ beats 262/276 medium · avg 72% 5.1% noisy
AHELM AIR-Bench Foundation music … 64.1% ▃█▃▅▃·●·▃▅▃▃ beats 8/13 medium · avg 57% 2.0% blunt · at its level
AHELM MELD audio emotion classifi… 36.8% ▃··▃▃●·▃▅▅█▅ beats 3/13 medium · avg 39% 1.7% blunt · at its level
AHELM FLEURS 7-language transcrip… 44.3% ▄▂···▂▆▂●▂·▂ beats 11/13 avg 83% 1.1% blunt · noisy
AHELM Multilingual LibriSpeech 7-… 6.7% ▂▂▂··▂▂·▂▂●▆ beats 10/13 avg 14% 0.2% blunt · noisy
AHELM VocalSound human-sound reco… 50.6% █●▄▄·█·█·█▄▄ beats 3/13 easy · avg 67% 0.0% blunt · at its level · noisy
source results share of the score
AHELM: A Holistic Evaluation of Audio-L… 10 55% ▇▇▇▇▇▇▇
SpeakLeash CPTUB 1 16% ▇▇
Open Persian LLM Leaderboard v2 1 12% ▇
Berkeley Function Calling Leaderboard r… 1 7% ▇
Open Portuguese LLM Leaderboard 1 5% ▇
Galileo Agent Leaderboard 1 4% ▇
prior: pull toward the middle 1% ▇
weighted average = 61.7
Licences: AHELM: A Holistic Evaluation of Audio-Language Models ([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)) · 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))
## How to run it
No verified API or local download yet.
[interactive version](https://whatmodel.sh/model/gemini-2-0-flash-lite-001) · [JSON](https://whatmodel.sh/api?format=json&all=1)