Model comparison
SmolLM3-3B vs Gemini 2.5 Pro
Capability radar, value bars, and side-by-side posture.
SmolLM3-3B
Not scored
value score
Gemini 2.5 Pro
93
value score
SmolLM3-3B ctx
—
Gemini 2.5 Pro ctx
1.0M
Capability radar
Value · context · multimodal · openness · speed posture
Value head-to-head
- Gemini 2.5 Pro93
Attribute tape
| Field | SmolLM3-3B | Gemini 2.5 Pro |
|---|---|---|
| Developer | HuggingFaceTB | Google DeepMind |
| Context | — | 1.0M |
| Modalities | Text, Code, Image, Audio, Video | |
| Openness | — | Proprietary Model |
| Speed | — | — |
| Price | — | $1.25/1M in · $10/1M out |
| Value score | — | 93 |
| Summary | SmolLM3-3B is a 3-billion-parameter open-weight language model from Hugging Face's Smol models research line, designed for reasoning, multilingual use across six languages, and long-context tasks up to 128k tokens. It emphasizes full openness — releasing weights and training details — alongside a hybrid reasoning mode and instruct tuning trained on an 11.2-trillion-token curriculum. | Google frontier multimodal model. |