Gemma 3 1B
The Gemma 3 1B model is a lightweight, 1-billion-parameter language model by Google, optimized for efficiency on resource-limited devices. At 529MB, it processes text at 2,585 tokens/second with a context window of 128,000 tokens. It supports 35+ languages but handles text-only input, unlike larger multimodal Gemma models. This balance of speed and efficiency makes it ideal for fast text processing on mobile and low-power devices.
Benchmark results
| Benchmark | Score | Tags | Source |
|---|---|---|---|
| BIG-Bench Extra Hard | 7.2% | self-reported llm-stats | link → |
| BIG-Bench Hard | 39.1% | self-reported llm-stats | link → |
| Bird-SQL (dev) | 6.4% | self-reported llm-stats | link → |
| ECLeKTic | 1.4% | self-reported llm-stats | link → |
| FACTS Grounding | 36.4% | self-reported llm-stats | link → |
| Global-MMLU-Lite | 34.2% | self-reported llm-stats | link → |
| GPQA | 19.2% | self-reported llm-stats | link → |
| GSM8k | 62.8% | self-reported llm-stats | link → |
| HiddenMath | 15.8% | self-reported llm-stats | link → |
| HumanEval | 41.5% | self-reported llm-stats | link → |
| IFEval | 80.2% | self-reported llm-stats | link → |
| LiveCodeBench | 1.9% | self-reported llm-stats | link → |
| MATH | 48.0% | self-reported llm-stats | link → |
| MBPP | 35.2% | self-reported llm-stats | link → |
| MMLU-Pro | 14.7% | self-reported llm-stats | link → |
| Natural2Code | 56.0% | self-reported llm-stats | link → |
| SimpleQA | 2.2% | self-reported llm-stats | link → |
| WMT24++ | 35.9% | self-reported llm-stats | link → |