Gemma 3 4B

Gemma 3 4B is a 4-billion-parameter vision-language model from Google, handling text and image input and generating text output. It features a 128K context window, multilingual support, and open weights. Suitable for question answering, summarization, reasoning, and image understanding tasks.

Benchmark results

Benchmark Score Tags Source
AI2D 74.8% self-reported llm-stats link →
BIG-Bench Extra Hard 11.0% self-reported llm-stats link →
BIG-Bench Hard 72.2% self-reported llm-stats link →
Bird-SQL (dev) 36.3% self-reported llm-stats link →
ChartQA 68.8% self-reported llm-stats link →
DocVQA 75.8% self-reported llm-stats link →
ECLeKTic 4.6% self-reported llm-stats link →
FACTS Grounding 70.1% self-reported llm-stats link →
Global-MMLU-Lite 54.5% self-reported llm-stats link →
GPQA 30.8% self-reported llm-stats link →
GSM8k 89.2% self-reported llm-stats link →
HiddenMath 43.0% self-reported llm-stats link →
HumanEval 71.3% self-reported llm-stats link →
IFEval 90.2% self-reported llm-stats link →
InfoVQA 50.0% self-reported llm-stats link →
LiveCodeBench 12.6% self-reported llm-stats link →
MATH 75.6% self-reported llm-stats link →
MathVista-Mini 50.0% self-reported llm-stats link →
MBPP 63.2% self-reported llm-stats link →
MMLU-Pro 43.6% self-reported llm-stats link →
MMMU (val) 48.8% self-reported llm-stats link →
Natural2Code 70.3% self-reported llm-stats link →
SimpleQA 4.0% self-reported llm-stats link →
TextVQA 57.8% self-reported llm-stats link →
VQAv2 (val) 62.4% self-reported llm-stats link →
WMT24++ 46.8% self-reported llm-stats link →