[whatmodel.sh](../../README.md) / Falcon3 10B Instruct
# Falcon3 10B Instruct
TII · released 2024-12 · 10.31B params · falcon-llm
**score 45.0** ±2.4 · #478 of 732 · from 7 benchmark results across 3 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Falcon3 10B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃●█▄▅▅▄▄▂▁ #283 of 313* 36% 2
coding ▁▁▁▃▅●█▇▅▄▃▁ #358 of 469 47% 4
agent tasks · #283 of 313 · 36% of its score
Beats [Magistral Small 1.0 (2506)](magistral-small-2506.md), [Llama 4 Scout (109B-A17B)](llama-4-scout.md), [Bielik 11B v2.3 Instruct](bielik-11b-v2-3-instruct.md) here, though they rank higher overall. Trails [Granite 4.0 H 350M](granite-4-0-h-350m.md), [Qwen3 0.6B](qwen3-0-6b.md), [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL V4 (December 2025 archive) 27.0% ▁▁▁▂●█▅▅▆▂▂▂ beats 19/77 hard · avg 31% 22% typical
BFCL v3 45.1% ▁▁▁▁▁●▅█▅▄▁▁ beats 22/114 medium · avg 51% 14% blunt · at its level
coding · #358 of 469 · 47% of its score
Beats [LFM2.5 2.6B](lfm2-5-2-6b.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [Qwen3-VL 2B Thinking](qwen3-vl-2b-thinking.md) here, though they rank higher overall. Trails [Code Tulu 2 13B](code-tulu-2-13b.md), [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CodeReviewQA: solution identifica… 18.0% ▃··▁▂●▃▂█▃▄▂ beats 12/47 medium · avg 51% 18% sharp · at its level · noisy
CodeReviewQA: change localisation… 12.0% █▂▁▁▂▂●▃▅▅▅▇ beats 18/47 hard · avg 33% 15% sharp · noisy
CodeReviewQA: automated code refi… 22.3% ▁▁▁▁▁▁▂▂●█▅▄ beats 14/47 hard · avg 32% 13% typical
CodeReviewQA: change type recogni… 44.7% ▁▁▁······●▃█ beats 4/47 easy · avg 67% 0.6% blunt · at its level · noisy
other tests (1)
test result vs the others in its run difficulty counts why
Open PT-LLM Leaderboard average (… 74.6% ▃▁▁▁▁▁▁▁▂█●▁ beats 219/276 medium · avg 72% 15% at its level · noisy
source results share of the score
CodeReviewQA: The Code Review Comprehen… 4 47% ▇▇▇▇▇▇
Berkeley Function Calling Leaderboard r… 2 36% ▇▇▇▇
Open Portuguese LLM Leaderboard 1 15% ▇▇
prior: pull toward the middle 3% ▇
weighted average = 45.0
Licences: CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Falcon3 10B Instruct (Q4_K_M, needs ~6.3 GB, from [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon3-10B-Instruct-GGUF/tree/0072d525c72df8cbb1af9561672da481bd9e5595))
```sh
llama cli -hf tiiuae/Falcon3-10B-Instruct-GGUF:q4_k_m
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf tiiuae/Falcon3-10B-Instruct-GGUF:q4_k_m
```
Other versions: Q8_0 (~11 GB) `llama cli -hf tiiuae/Falcon3-10B-Instruct-GGUF:q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Falcon3 10B Instruct (Q4_K_M, needs ~6.3 GB, from [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon3-10B-Instruct-GGUF/tree/0072d525c72df8cbb1af9561672da481bd9e5595))
```sh
lms get https://huggingface.co/tiiuae/Falcon3-10B-Instruct-GGUF@q4_k_m -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~11 GB) `lms get https://huggingface.co/tiiuae/Falcon3-10B-Instruct-GGUF@q8_0 -y` · 4bit (~5.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/Falcon3-10B-Instruct-4bit -y` · 8bit (~11 GB, Mac only) `lms get https://huggingface.co/mlx-community/Falcon3-10B-Instruct-8bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Falcon3 10B Instruct (Q4_K_M, needs ~6.69 GB, from [ollama.com](https://ollama.com/library/falcon3/tags))
```sh
ollama run falcon3:10b-instruct-q4_K_M
```
Other versions: Q8_0 (~12.87 GB) `ollama run falcon3:10b-instruct-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Falcon3 10B Instruct (4bit, needs ~5.8 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Falcon3-10B-Instruct-4bit/tree/27572c09b8faeef934f7702a8588e0f409a599bc))
```sh
mlx_lm.chat --model mlx-community/Falcon3-10B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Falcon3-10B-Instruct-4bit
```
Other versions: 8bit (~11 GB, Mac only) `mlx_lm.chat --model mlx-community/Falcon3-10B-Instruct-8bit`
Model repository and documentation: [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon3-10B-Instruct)
## Published speed measurements
Reference results for the hardware, engine and settings below. These are not verified speeds of the downloads above or predictions for your machine.
hardware precision speed engine and settings
Ryzen 7 H 255 (Radeon 780M)… q4_k_m 12.8 tok/s llama.cpp · whatmodel bench.py 3.…
Each speed links to its source, which has the full settings.
[interactive version](https://whatmodel.sh/model/falcon3-10b) · [JSON](https://whatmodel.sh/api?format=json&all=1)