[whatmodel.sh](../../README.md) / Gemini 2.5 Flash-Lite # Gemini 2.5 Flash-Lite Google · released 2025-07 · closed weights **score 57.2** ±1.7 · #240 of 732 · from 8 benchmark results across 7 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 Flash-Lite among every model on that task or test, worst to best. 
task                vs the others          rank  counts  tests
agent tasks         ▁▁▃▆●▄▅▅▄▄▂▁   #203 of 313*     20%      2
math                ▁▁▃▅█●▄█▄▂▁▁   #243 of 431*     10%      1
vision & documents  ▁▁▂▂▁▃●▂▆▇█▄   #105 of 153*     63%      2
agent tasks · #203 of 313 · 20% of its score Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [DeepSeek R1 0528](deepseek-r1-0528.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
BFCL V4 (December 2025 archive)      36.9%  ▁▁▁▂██●▅▆▂▂▂ beats 40/77  hard · avg 31%     14%  at its level
Galileo Agent Leaderboard v2 (act…   47.0%  ▅▃·▃▃▅▅█●█▅▅ beats 12/19  hard · avg 26%    5.9%  at its level
math · #243 of 431 · 10% of its score Beats [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md), [Command A (03-2025)](command-a.md), [GPT-4o (2024-05-13)](gpt-4o-2024-05-13.md) here, though they rank higher overall. Trails [Gemma 7B (base)](gemma-7b-base.md), [CodeGemma 7B (v1.0)](codegemma-7b.md), [Phi-2 (2.7B)](phi-2.md) here, though they rank lower overall. 
test         result  vs the others in its run  difficulty        counts  why
MATH-Vision   52.1%  ▁▁·▁··●▁▃▇▇█ beats 4/24   medium · avg 47%     10%  at its level
vision & documents · #105 of 153 · 63% of its score Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md) here, though they rank higher overall. Trails [Llama 4 Maverick (402B-A17B)](llama-4-maverick.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty        counts  why
CharXiv (reasoning questions) (ma…   55.5%  ●·▃·▃▃▃·▃▃█▅ beats 0/11   medium · avg 38%     53%  at its level
MATH-Vision                          52.1%  ▁▁·▁··●▁▃▇▇█ beats 4/24   medium · avg 47%     10%  at its level
other tests (4) 
test                                result  vs the others in its run    difficulty           counts  why
Icelandic LLM Leaderboard average…   26.2%  ▁▁▃▂▂●▁▃▄▄▄█ beats 10/38    very hard · avg 14%    7.8%  typical
Open PT-LLM Leaderboard average (…   82.3%  ▃▁▁▁▁▁▁▁▂█▅● beats 269/276  medium · avg 72%       5.9%  at its level · noisy
Vectara hallucination leaderboard…   97.1%  ▁▁▂▃▄▄█●▃▂▂▁ beats 107/140  very easy · avg 94%    1.4%  blunt · maxed out · noisy
Vectara hallucination leaderboard…   96.7%  ▁▂▁▂▅█▂▃▄▃▁● beats 97/98    very easy · avg 90%    0.0%  blunt
## 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
Google DeepMind Gemini model page and m…        1   53% ▇▇▇▇▇▇
Berkeley Function Calling Leaderboard r…        1   14% ▇▇
Qwen/Qwen3.5-4B model card                      1   10% ▇
Miðeind Icelandic LLM Leaderboard               1    8% ▇
Galileo Agent Leaderboard                       1    6% ▇
Open Portuguese LLM Leaderboard                 1    6% ▇
Vectara hallucination leaderboard               2    1% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 57.2
Licences: Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · Qwen/Qwen3.5-4B model card ([apache-2.0](../../licenses/Qwen--Qwen3.5-4B.txt)) · Miðeind Icelandic LLM Leaderboard ([CC BY 4.0](../../licenses/mideind-icelandic-llm-leaderboard-CC-BY-4.0.txt)) · Galileo Agent Leaderboard ([Apache-2.0](../../licenses/galileo-agent-leaderboard-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) ## How to run it ### Through an API api (Google) · $0.18/M blended · $0.1 in / $0.4 out per 1M tokens · [ai.google.dev](https://ai.google.dev/gemini-api/docs/models) [price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/google/models/gemini-2.5-flash-lite.toml): Standard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/google/gemini-2.5-flash-lite.toml) [interactive version](https://whatmodel.sh/model/gemini-2-5-flash-lite) · [JSON](https://whatmodel.sh/api?format=json&all=1)