[whatmodel.sh](../../README.md) / Llama 3 8B Instruct
# Llama 3 8B Instruct
Meta · released 2024-04 · 8.03B params · llama3
**score 43.6** ±0.4 · #507 of 732 · from 45 benchmark results across 25 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Llama 3 8B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁●▃▆█▄▅▅▄▄▂▁ #305 of 313* 0.8% 2
coding ▁▁▁▃▅●█▇▅▄▃▁ #351 of 469 41% 9
chat & writing ▁▁▁▃▆●▆█▆▇▇▃ #301 of 402 12% 4
math ▁▁●▅█▆▄█▄▂▁▁ #391 of 431 10% 6
agent tasks · #305 of 313 · 0.8% of its score
Beats [QwQ-32B-Preview](qwq-32b-preview.md), [DeepSeek-Coder-V2 Lite Instruct (16B-A2.4B)](deepseek-coder-v2-lite-instruct.md), [Phi-4-mini 3.8B](phi-4-mini.md) here, though they rank higher overall. Trails [Granite 4.0 350M](granite-4-0-350m.md), [Granite 4.0 H 350M](granite-4-0-h-350m.md), [Qwen2.5 0.5B Instruct](qwen2-5-0-5b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL v3 33.1% ▁▁▁●▁▃▅█▅▄▁▁ beats 9/114 medium · avg 51% 0.4% blunt · at its level
BFCL (version not stated) 58.9% ●·········██ beats 1/5 easy · avg 77% 0.4% blunt · at its level
coding · #351 of 469 · 41% 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 [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [OpenChat 3.5](openchat-3-5.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 61.6% ▁·▁▁▃▄▆●█▃▁▁ beats 47/99 easy · avg 76% 14% sharp · at its level
HumanEval+ 56.7% ▁·▁▁▁▃▄▄●█▃▁ beats 48/99 easy · avg 70% 11% sharp · at its level
CRUXEval (ZeroEval, 800 problems) 37.8% ▁▂●▃▆█▆▃▂·▁▁ beats 3/47 medium · avg 57% 4.7% sharp · at its level
HumanEvalSynthesize (HumanEvalPac… 36.9% ▁·▂▁▂▆●█▄▄▁▂ beats 13/35 medium · avg 62% 3.0% at its level
MBPP+ 54.8% ▁···▁▁▂▅●█▆▂ beats 27/75 easy · avg 66% 2.1% at its level
LiveCodeBench official (Jan 1–Nov… 13.3% ▁▁·●▂▄▆█▅▂·▂ beats 2/31 hard · avg 23% 2.1% sharp
HumanEvalExplain (HumanEvalPack, … 29.1% ▄▇█▃▄▇●▁▇▃·▁ beats 25/35 medium · avg 46% 2.0% at its level
HumanEvalFix (HumanEvalPack, 6-la… 29.3% ▁·▁▁▄██▄●▁▅▅ beats 24/35 medium · avg 51% 1.9% sharp · at its level
WebApp1K-React pass@1 (onekq-ai) 6.8% ●▁▁█▁▄▅▃▄▇▁▄ beats 0/31 medium · avg 40% 0.5% sharp · noisy
chat & writing · #301 of 402 · 12% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Gemma 4 E4B](gemma-4-e4b.md), [OpenThinker3 7B](openthinker3-7b.md) here, though they rank higher overall. Trails [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [LFM2 700M](lfm2-700m.md), [Flan-T5 XXL](flan-t5-xxl.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 22.9% ▁▂▂▃▆▆●▆▄▄▁▁ beats 60/122 medium · avg 35% 5.2% sharp
Arena-Hard (v0.1, GPT-4-1106 judg… 20.6% ▂▁▁▅●▅▄▃▃▅▃▁ beats 14/60 medium · avg 47% 3.1% sharp
LMArena (text) 1223.7 ▁▁▁▂▃●▃▆▆▇█▃ beats 79/385 avg 1260 1.8% blunt
Polyrating rating (bias-aware re-… 1150.0 ▂▁▂▁▃▇█▆●▄▂▂ beats 66/95 avg 1187 1.7% typical
math · #391 of 431 · 10% of its score
Beats [Yi 34B Chat](yi-34b-chat.md) here, though they rank higher overall. Trails [GPT-Neo 125M](gpt-neo-125m.md), [GPT-2 XL (1.5B)](gpt-2-xl.md), [GPT-Neo 1.3B](gpt-neo-1-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (ZeroEval, zero-shot) 78.5% ▁▄·▁▄●▅▅▅███ beats 8/43 very easy · avg 87% 3.7% sharp · at its level
GSM8K 72.9% ▂▂▂▂▃▄▅▇●█▆▄ beats 123/206 very easy · avg 88% 2.2% sharp
MATH level 5 6.1% ▂●▅▆▄█▆▃▅▂▃▄ beats 3/92 hard · avg 28% 1.3% sharp
MATH Level 5 (ZeroEval, 721 probl… 7.9% ▁●▁▆█▂▇▇▄··▂ beats 1/38 hard · avg 30% 1.2% sharp
MATH (subset unspecified) 27.9% ▂▄●▃█▇▅▆▄▃▄▁ beats 12/80 medium · avg 56% 0.9% at its level
OTIS mock AIME 2024–2025 1.9% ▃●▄▃▃▃▇▇█▃▂▅ beats 15/197 very hard · avg 7.2% 0.1% sharp · too hard for it · noisy
other tests (24)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 61.7% ▁·▁▁●▄▄▅█▄▂▁ beats 4/53 easy · avg 75% 8.8% at its level
LiveBench 2024-07-26 27.6% ▁▁▁▂▂▃▂●▆▃▅▃ beats 21/61 medium · avg 38% 5.9% at its level
MMLU-Pro 40.1% ▃▂▅●▃·█▃▅··▂ beats 6/23 medium · avg 54% 2.7% at its level
BigCodeBench (Complete) 36.8% ▂▁▁▄▂●▇█▆▂▂▂ beats 18/58 medium · avg 48% 1.9% at its level
WildBench v2 WB-Score (task macro) 29.2028 WB-Score ▁·▁▁·▃●▄▇▆▅▆ beats 11/52 avg 43.019 WB-Score 1.9% sharp
BigCodeBench (Instruct) 31.7% ▃▂·▆●██▆▅▃▃▃ beats 7/33 medium · avg 39% 1.9% at its level
MedQA (USMLE, 4 options) 60.7% ▁▁··▄▁▁··▄▃● beats 17/17 medium · avg 71% 1.6% sharp · at its level
CPTUB average (SpeakLeash, 0-5 ju… 60.0% ▁▂▂▂▃●▃▆▅▄▂▃ beats 30/95 medium · avg 65% 1.5% at its level
BIG-Bench Hard 54.4% ▁▁▁▁▂▄▅●█▃▃▁ beats 75/155 medium · avg 72% 1.4% sharp · at its level · small run
MMLU 68.4% ▁▁▁▁▁▂▂▅█●▆▄ beats 125/214 easy · avg 77% 1.3% at its level
EvalPerf correctness (118 tasks, … 43.7% ●·▂▂▂▆▅▆▆▆▃█ beats 0/29 easy · avg 74% 1.3% at its level
Polish EQ-Bench v2 (SpeakLeash) 46.3% ▁··▁▁▂▂●▅▆█▆ beats 20/70 medium · avg 62% 1.1% at its level
ZebraLogic (ZeroEval, 1,000 puzzl… 11.9% ▁▁▃●██▁▁·▁▁▁ beats 14/57 hard · avg 15% 1.0% sharp · too hard for it
GPQA Diamond 26.1% ▁●▁▁▂▄▆▄▅▇█▅ beats 6/211 hard · avg 39% 0.9% sharp · too hard for it
Open Persian LLM Leaderboard v2 a… 50.4% ▂▄▂▃●▄▃▆▃▅▂▂ beats 32/81 medium · avg 57% 0.8% blunt · at its level
MedMCQA 56.9% ▂▂··▄▄▂▂·▄█● beats 15/17 medium · avg 63% 0.7% at its level
GPQA (subset unspecified) 26.1% ▂●▂▂▄▃▆▃▅█▃▃ beats 6/106 hard · avg 38% 0.6% sharp
AraGen-v3 3C3H score (Inception, … 9.2% ▁▁·▁▂●▄█▄▅▄▄ beats 6/66 hard · avg 29% 0.6% typical
Open PT-LLM Leaderboard average (… 50.1% ▃▁▁▁▁▁▁●▂█▅▁ beats 67/276 medium · avg 72% 0.4% at its level · noisy
CMMLU 53.3% ●·▅····█▃▅█▅ beats 1/14 medium · avg 73% 0.3% at its level · noisy
EuroEval Life in the UK (English … 83.5% ▁▁▁▁▁▁▁▂▄●▄▃ beats 87/146 easy · avg 82% 0.3% at its level · noisy
Vectara hallucination leaderboard… 92.6% ▁▁▂▃●▄█▆▃▂▂▁ beats 40/140 very easy · avg 94% 0.2% blunt · noisy
PubMedQA 74.6% ▂·▂▆·▄▂▄▄▂●▂ beats 13/17 medium · avg 75% 0.1% blunt · at its level
Chess Puzzles (Epoch) 0.0% ●▁▁▁▁▂▃▃▃▂▁▁ beats 7/137 very hard · avg 5.8% 0.0% sharp · too hard for it · noisy
source results share of the score
EvalPlus / EvalPerf published evaluatio… 4 28% ▇▇▇
WildEval ZeroEval published results 5 20% ▇▇
Granite Code Models: A Family of Open F… 3 7% ▇
InternLM GitHub repository model cards 6 7% ▇
LiveBench model judgments 1 6% ▇
AlpacaEval 1.0 and 2.0 leaderboards 1 5% ▇
BigCodeBench paper 2 4% ▇
Arena-Hard-Auto v0.1 leaderboard 1 3% ▇
MMLU-Pro author-released evaluation runs 1 3% ▇
Open Medical-LLM Leaderboard results 3 2% ▇
Epoch AI 4 2% ▇
LiveCodeBench official website results 1 2% ▇
WildBench v2 leaderboard data 1 2% ▇
LMArena leaderboard dataset 1 2% ▇
Polyrating: A Cost-Effective and Bias-A… 1 2% ▇
SpeakLeash CPTUB 1 1% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 1% ▇
Open Persian LLM Leaderboard v2 1 1% ▇
Inception Arabic/Hindi leaderboards 1 1% ▇
WebApp1K models leaderboard 1 1% ▇
Open Portuguese LLM Leaderboard 1 0% ▇
Berkeley Function Calling Leaderboard r… 1 0% ▇
ChatGLM: A Family of Large Language Mod… 1 0% ▇
EuroEval leaderboards 1 0% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 43.6
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · Granite Code Models: A Family of Open Foundation Models for Code Intelligence ([CC BY 4.0](../../licenses/paper-2405.04324v1-CC-BY-4.0.txt)) · InternLM GitHub repository model cards ([Apache-2.0](https://github.com/InternLM/InternLM/blob/68fdc71330059255fd647abf28219ad11e8e4865/LICENSE)) · LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · MMLU-Pro author-released evaluation runs ([Apache-2.0](../../licenses/mmlu-pro-author-runs-Apache-2.0.txt)) · Open Medical-LLM Leaderboard results ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LiveCodeBench official website results ([CC-BY-SA-4.0](../../licenses/lcb-sharealike-NOTICE.txt)) · WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · WebApp1K models leaderboard ([MIT](../../licenses/webapp1k-leaderboard-MIT.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## 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 Llama 3 8B Instruct (Q4_K_M, needs ~4.9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF/tree/0910a3e69201d274d4fd68e89448114cd78e4c82))
```sh
llama cli -hf lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.5 GB) `llama cli -hf lmstudio-community/Meta-Llama-3-8B-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 Llama 3 8B Instruct (Q4_K_M, needs ~4.9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF/tree/0910a3e69201d274d4fd68e89448114cd78e4c82))
```sh
lms get https://huggingface.co/lmstudio-community/Meta-Llama-3-8B-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 (~8.5 GB) `lms get https://huggingface.co/lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF@Q8_0 -y` · 4bit (~5.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Meta-Llama-3-8B-Instruct-4bit -y` · 8bit (~9 GB, Mac only) `lms get https://huggingface.co/mlx-community/Meta-Llama-3-8B-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 Llama 3 8B Instruct (Q4_0, needs ~5.32 GB, from [ollama.com](https://ollama.com/library/llama3/tags))
```sh
ollama run llama3:8b
```
Other versions: Q8_0 (~10.14 GB) `ollama run llama3:8b-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 Llama 3 8B Instruct (4bit, needs ~5.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Meta-Llama-3-8B-Instruct-4bit/tree/c38b3b1f03cce0ce0ccd235e5c97b0d3d255e651))
```sh
mlx_lm.chat --model mlx-community/Meta-Llama-3-8B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Meta-Llama-3-8B-Instruct-4bit
```
Other versions: 8bit (~9 GB, Mac only) `mlx_lm.chat --model mlx-community/Meta-Llama-3-8B-Instruct-8bit`
Model repository and documentation: [huggingface.co/meta-llama](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
[interactive version](https://whatmodel.sh/model/llama-3-8b) · [JSON](https://whatmodel.sh/api?format=json&all=1)