BOAR runs GGUF models with llama.cpp, on the phone's own processor. It downloads one at setup, and you can add others.
The default: Qwen2.5-1.5B
Qwen2.5-1.5B-Instruct (Alibaba, Apache-2.0), quantized to Q4_K_M, about 0.99 GB. It is the quickest model BOAR offers and the only one that completed every question in our evaluation within the time limit. In that evaluation, it performed less well on long synthesis prompts.
Alongside it, setup downloads bge-small-en-v1.5 (BAAI, MIT, about 35 MB). It doesn't write answers: it finds passages in the library by meaning.
Optional models, measured
Each of these is in Settings → Models, and was run on our test phone (Xiaomi 2311DRK48G, MediaTek Dimensity 8300, 11.6 GB RAM) over the same 17 questions:
| Model | Size on disk | License | Speed | Median answer | What we saw |
|---|---|---|---|---|---|
| Qwen2.5-1.5B (default) | 0.99 GB | Apache-2.0 | 11.4 tok/s | 21 s | fast; fine on simple questions, slips on synthesis |
| LFM2.5-8B-A1B | 5.16 GB | LFM Open License v1.0 | 14.8 tok/s | 57 s | 8B total, ~1.5B active per generated token; the only tested model to answer the RAM-budget question; 4 of 17 answers ended at the token limit |
| Phi-3.5-mini | 2.39 GB | MIT | 4.0 tok/s | 73 s | 4 timeouts; slow; invents citations |
| Qwen2.5-7B | 4.68 GB | Apache-2.0 | 2.7 tok/s | 107 s | accurate, often too slow to finish |
| Gemma 4 E4B | 5.15 GB | Apache-2.0 | not measured yet | – | loads and answers |
Speed is tokens a second (a token is roughly three quarters of a word). The full runs are in Results.
Which one to use
- Everyday questions, on any phone: the default.
- Harder reasoning, if you can wait: LFM2.5-8B-A1B. It's a mixture of experts: a large model whose parameters mostly sit unused, with a small part active for each generated token. In our test, it wrote at a similar speed to the 1.5B model while using roughly 1.5B active parameters per generated token.
- Careful comparisons, with time to spare: Phi-3.5-mini, and check its citations.
The model list shows a green, yellow or red hint for whether a model is likely to fit your phone's memory. It's an estimate from the file size, not a guarantee.
Let BOAR choose
With Adaptive Routing on (Settings), BOAR classifies each message and picks which of your downloaded models answers it: a light one for small talk, a stronger one for research. It only routes between models you've downloaded.
Find more models on Hugging Face
Settings → Tone & Model → Find more models searches Hugging Face for GGUF files. Tap a file to add it to your model list, then download it like any other.
This search is one of the two times BOAR uses the internet, and it only runs when you search. See What stays on your phone.
Switching models
Pick a model in Settings to make it active. BOAR unloads the previous one before loading the next, so memory doesn't pile up. The first answer after a switch takes longer while the model loads.
Test a model yourself
Found a promising model? Benchmark it on your phone and share the measurements so others can compare results across hardware.