[whatmodel.sh](../../README.md) / Llama 4 Scout (109B-A17B)
# Llama 4 Scout (109B-A17B)
Meta · released 2025-04 · 109B params (17B active) · llama-4
**score 54.8** ±1.0 · #284 of 732 · from 19 benchmark results across 14 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Llama 4 Scout (109B-A17B) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃●█▄▅▅▄▄▂▁ #284 of 313 11% 4
chat & writing ▁▁▁▃▆▄▆●▆▇▇▃ #201 of 402* 10% 1
math ▁▁▃▅█●▄█▄▂▁▁ #238 of 431* 27% 2
vision & documents ▁▁▂▂▁▃●▂▆▇█▄ #106 of 153* 8.3% 2
web search ▁▁▁▅▄●▃▄·▁·▁ #34 of 84* 3.3% 1
agent tasks · #284 of 313 · 11% of its score
Beats [Magistral Small 1.0 (2506)](magistral-small-2506.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) 28.1% ▁▁▁▂█●▅▅▆▂▂▂ beats 26/77 hard · avg 31% 4.4% at its level
Finance Agent Benchmark v1 (537 q… 5.8% █·▃●·█▅▅▅▅▃▅ beats 4/18 very hard · avg 5.5% 3.3% sharp
BFCL v3 45.4% ▁▁▁▁▁●▅█▅▄▁▁ beats 24/114 medium · avg 51% 2.1% blunt · at its level
Galileo Agent Leaderboard v1 (too… 62.9% ●▃▃▃▆▅▆▅█▂▅▂ beats 0/31 easy · avg 78% 1.6% typical
chat & writing · #201 of 402 · 10% of its score
Beats [Gemma 2 9B IT SimPO](gemma-2-9b-simpo.md), [Granite 4.2 8B](granite-4-2-8b.md), [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1321.4 ▁▁▁▂▃▃▃●▆▇█▃ beats 155/385 avg 1260 10% blunt
math · #238 of 431 · 27% of its score
Beats [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md), [Command A (03-2025)](command-a.md), [GPT-4o (2024-05-13)](gpt-4o-2024-05-13.md) here, though they rank higher overall. Trails [Gemma 7B (base)](gemma-7b-base.md), [CodeGemma 7B (v1.0)](codegemma-7b.md), [Phi-2 (2.7B)](phi-2.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH level 5 62.3% ▂▂▅▆▄█●▃▅▂▃▄ beats 52/92 hard · avg 28% 22% sharp · at its level
OTIS mock AIME 2024–2025 7.8% ▃▃▄●▃▃▇▇█▃▂▅ beats 45/197 very hard · avg 7.2% 4.9% sharp · noisy
vision & documents · #106 of 153 · 8.3% of its score
Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md), [Mistral Small 3.2 24B](mistral-small-3-2.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
Arena vision (style controlled) 1127.5 ▁▁▂▂▂▂●▄▅▅█▇ beats 40/152 avg 1061 6.5% blunt
VMMU (Vietnamese multimodal under… 49.1% ▅······▃·▅·● beats 5/7 very hard · avg 36% 1.9% noisy
web search · #34 of 84 · 3.3% of its score
Beats [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md), [Claude Opus 4.7](claude-opus-4-7.md), [GPT-5.3-Codex](gpt-5-3-codex.md) here, though they rank higher overall. Trails [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Finance Agent Benchmark v1 (537 q… 5.8% █·▃●·█▅▅▅▅▃▅ beats 4/18 very hard · avg 5.5% 3.3% sharp
other tests (10)
test result vs the others in its run difficulty counts why
GPQA Diamond 51.8% ▁▁▁▁▂▄●▄▅▇█▅ beats 73/211 hard · avg 39% 14% sharp · at its level
CPTUB average (SpeakLeash, 0-5 ju… 75.0% ▁▂▂▂▃█▃▆●▄▂▃ beats 69/95 medium · avg 65% 6.4% at its level
EQ-Bench 3 (emotional intelligenc… 993.6 ▁·●▁▄▅▅▃█▄▁▁ beats 3/62 avg 1034 5.9% typical
BRIDGE clinical text, zero-shot a… 35.1% ▁▁▁▁▁▂▂▃▄●█▃ beats 45/97 hard · avg 31% 5.8% blunt · at its level
Open Persian LLM Leaderboard v2 a… 69.0% ▂▄▂▃█▄▃▆▃●▂▂ beats 70/81 medium · avg 57% 4.3% blunt · at its level
LongSocialBench (full-context, 4-… 40.2% ▆▂·●█·▂▂·▄▄▂ beats 5/16 very hard · avg 36% 2.5% blunt
Open PT-LLM Leaderboard average (… 78.7% ▃▁▁▁▁▁▁▁▂█●▁ beats 246/276 medium · avg 72% 2.0% at its level · noisy
EuroEval HellaSwag (English), tes… 86.4% ▃·▁▁▂▁▂▅█▇●▁ beats 86/100 medium · avg 67% 1.6% sharp · at its level · noisy
EuroEval Life in the UK (English … 91.0% ▁▁▁▁▁▁▁▂▄█●▃ beats 120/146 easy · avg 82% 0.9% noisy
Vectara hallucination leaderboard… 92.3% ▁▂▁▂▅█▂●▄▃▁▁ beats 64/98 very easy · avg 90% 0.0% blunt
source results share of the score
Epoch AI 3 41% ▇▇▇▇▇
LMArena leaderboard dataset 2 16% ▇▇
Berkeley Function Calling Leaderboard r… 2 7% ▇
SpeakLeash CPTUB 1 6% ▇
EQ-Bench 3 canonical leaderboard results 1 6% ▇
BRIDGE Medical Leaderboard 1 6% ▇
Open Persian LLM Leaderboard v2 1 4% ▇
Finance Agent Benchmark 1 3% ▇
EuroEval leaderboards 2 3% ▇
LongSocialBench: Do Long-Context LLMs U… 1 3% ▇
Open Portuguese LLM Leaderboard 1 2% ▇
VMMU: A Vietnamese Multitask Multimodal… 1 2% ▇
Galileo Agent Leaderboard 1 2% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 54.8
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([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)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · BRIDGE Medical Leaderboard ([MIT](../../licenses/bridge-medical-leaderboard-MIT.txt)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Finance Agent Benchmark ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · LongSocialBench: Do Long-Context LLMs Understand Online Discussion Threads? ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · VMMU: A Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Galileo Agent Leaderboard ([Apache-2.0](../../licenses/galileo-agent-leaderboard-Apache-2.0.txt)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## How to run it
### Through an API
api (DeepInfra) · $0.15/M blended · $0.1 in / $0.3 out per 1M tokens · [deepinfra.com](https://deepinfra.com/meta-llama/Llama-4-Scout-17B-16E-Instruct)
[price source](https://github.com/BerriAI/litellm/blob/c19ce71bcdad234ca87a7af10cee45375ce811a8/model_prices_and_context_window.json): USD per 1M text tokens, standard uncached input and output. Product: Serverless text generation. API model: meta-llama/Llama-4-Scout-17B-16E-Instruct. Settings: service tier=standard, batch=no, cache=uncached input. Additional/excluded charges: Tools, cache storage, priority service and other modalities excluded.. Currency: USD. · [context source](https://github.com/BerriAI/litellm/blob/c19ce71bcdad234ca87a7af10cee45375ce811a8/model_prices_and_context_window.json): provider maximum input tokens; not an intrinsic model maximum or legacy max\_tokens
### 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 Llama 4 Scout (109B-A17B) (ud-q2_k_xl, needs ~33.2 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF/tree/main))
```sh
llama cli -hf unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF:UD-Q2_K_XL
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF:UD-Q2_K_XL
```
### 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 Llama 4 Scout (109B-A17B) (ud-q2_k_xl, needs ~33.2 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF/tree/main))
```sh
lms get https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF@UD-Q2_K_XL -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Llama 4 Scout (109B-A17B) (Q4_K_M, needs ~65.9 GB, from [ollama.com](https://ollama.com/library/llama4/tags))
```sh
ollama run llama4:scout
```
Model repository and documentation: [huggingface.co/meta-llama](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-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
AMD Ryzen AI Max+ 395 (Stri… Q4_K_M 18.32 tok/s llama.cpp b8933, Vulkan (RADV) · …
AMD Ryzen AI Max+ 395 (Stri… UD-Q4_K_XL 19.78 tok/s llama.cpp 83bc2f288 (commit from …
Each speed links to its source, which has the full settings.
[interactive version](https://whatmodel.sh/model/llama-4-scout) · [JSON](https://whatmodel.sh/api?format=json&all=1)