What it is
SmolLM3-3B is a small language model from Hugging Face, released on July 8, 2025. It has about 3.1 billion parameters and was trained on 11.2 trillion tokens using 384 H100 GPUs over 24 days. It was trained at a 64K-token context and stretches to 128K with YaRN, a standard context-extension method; the shipped config is set to 65,536 tokens, so the longer window needs a config change.
It has two modes: extended thinking, which is on by default, and direct answers, switched with /think and /no_think. It supports tool calling in XML or Python-call style and six languages: English, French, Spanish, German, Italian and Portuguese.
The weights are under Apache 2.0. Hugging Face also published the data mixture, training configs, intermediate checkpoints and evaluation code, so researchers can reproduce or modify the training. The weights were last updated in September 2025, and Hugging Face has announced no SmolLM4.
Our review
SmolLM3's real value is openness, not ability. Few models publish the full recipe: data mixture, configs, intermediate checkpoints and evaluation code. Anyone who researches or teaches how language models are trained, or wants to continue pretraining from a known state, gets a better-documented starting point here than from any other 3B model, and the 731,000 Hugging Face downloads in the past month suggest plenty do.
As a model it was competitive at launch and is not now. Hugging Face's own tables show it beating Llama-3.2-3B and Qwen2.5-3B but trailing Qwen3-4B with thinking on: 36.7 against 58.8 on AIME 2025, and 41.7 against 55.3 on GPQA Diamond. Thinking matters a great deal (AIME rises from 9.3 to 36.7), and tool calling holds up at 92.3 on BFCL without it.
The 2026 field has left it behind. IBM reports 78.33 on AIME 2025 for Granite 4.2 3B in its own setup, more than double SmolLM3's best, and Google's Gemma 4 E2B and E4B also read images and audio. SmolLM3 is text only, covers six European languages, and has not been updated since September 2025.
Its size is the one practical advantage that has not aged. At about 6GB in BF16 it runs on a laptop, and Hugging Face ships llama.cpp, MLX, ONNX, MLC and ExecuTorch builds, so the same weights a researcher studies can be put on a phone. That is the niche: experiments where knowing exactly what went into the model is worth more than the 40-odd AIME points Granite 4.2 3B claims over it.
Where it earns its place
A university lecturer running a graduate course on LLM training
Show students every stage of a real pretraining run
Use the published data mixture, configs and intermediate checkpoints to compare the model at different points in training, then have students fine-tune from a checkpoint on one GPU. Free under Apache 2.0; no other 3B model documents its run as completely.
An ML researcher studying data mixtures
Run controlled continued-pretraining or ablation experiments from a known baseline
Start from SmolLM3-3B-Base or an intermediate checkpoint and change one variable in the published recipe. At about 3B parameters, experiments fit on a single node; the cost of reproducing the original 24-day, 384-GPU run is far higher.
A mobile engineer prototyping an offline assistant
Ship a fully open on-device model in English and five European languages
Run the ONNX or ExecuTorch build locally, with /no_think for fast replies and tool calls for app actions. It is cheap and permissive, but Gemma 4 E2B or Granite 4.2 3B will answer harder questions noticeably better.
Before you commit
- Setup
- Needs Transformers 4.53 or later, or vLLM and SGLang servers with the Hermes tool parser; the 128K window requires editing max_position_embeddings and rope_scaling in the config.
- Integrations
- Transformers, vLLM, SGLang, llama.cpp, MLX, ONNX, MLC and ExecuTorch, with quantised checkpoints in Hugging Face's collection. No hosted endpoint on Hugging Face's Inference Providers.
- Data and privacy
- Apache 2.0 with no acceptable-use addendum; commercial use and redistribution allowed. Self-hosted only, so data stays on your device or server. The training data mixture is public, which makes provenance checks possible.
- Cost at scale
- About 3.1B parameters, roughly 6GB in BF16, so a single consumer GPU, an Apple laptop or a 4-bit phone build. With no paid API listed, cost at volume is your own hardware.
- Limits
- 64K native context, 128K with YaRN; six languages; text only; Hugging Face warns outputs may be inaccurate or biased; weaker reasoning than 2026 3B to 4B models.
What's good
- Apache 2.0 with data mixture, configs and intermediate checkpoints published
- Switchable thinking with /think and /no_think
- About 6GB in BF16, with llama.cpp, MLX, ONNX and ExecuTorch builds
- 92.3 on BFCL tool calling without thinking, per Hugging Face
What's not
- 36.7 on AIME 2025 against Granite 4.2 3B's self-reported 78.33
- Only six European languages
- Text only
- No update since September 2025 and no announced successor
Best for
Researchers and educators who want a small, fully reproducible model to study or fine-tune.
Alternatives
SmolLM3-3B: common questions
- Is SmolLM3-3B worth it?
- SmolLM3 is a glass-walled training run more than a product: unmatched for learning how a 3B model is made, outclassed for shipping one. We rate it 3.5 out of 5.
- How much does SmolLM3-3B cost?
- Free weights under Apache 2.0, with the training data recipe and checkpoints published. No hosted API on Hugging Face's own Inference Providers.
- What are the downsides of SmolLM3-3B?
- 36.7 on AIME 2025 against Granite 4.2 3B's self-reported 78.33; only six European languages; text only; no update since September 2025 and no announced successor.
- Who is SmolLM3-3B best for?
- Researchers and educators who want a small, fully reproducible model to study or fine-tune.
- How much does SmolLM3-3B cost for a team or in production?
- About 3.1B parameters, roughly 6GB in BF16, so a single consumer GPU, an Apple laptop or a 4-bit phone build. With no paid API listed, cost at volume is your own hardware.
- Is SmolLM3-3B safe for company data?
- Apache 2.0 with no acceptable-use addendum; commercial use and redistribution allowed. Self-hosted only, so data stays on your device or server. The training data mixture is public, which makes provenance checks possible.
- What are the best SmolLM3-3B alternatives?
- The closest alternatives we have reviewed are Granite 4.2, Gemma 4 and Phi-4-multimodal.
Answers checked Oct 22, 2026.