[whatmodel.sh](../../README.md) / Llama 3 70B Instruct
# Llama 3 70B Instruct
Meta · released 2024-04 · 70.55B params · llama3
**score 50.9** ±0.4 · #349 of 732 · from 34 benchmark results across 23 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 70B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃●█▄▅▅▄▄▂▁ #263 of 313* 0.4% 1
coding ▁▁▁▃▅▇●▇▅▄▃▁ #275 of 469 40% 10
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #266 of 402 17% 4
math ▁▁▃●█▆▄█▄▂▁▁ #341 of 431 8.8% 4
agent tasks · #263 of 313 · 0.4% of its score
Beats [DeepSeek R1 0528 Qwen3 8B](deepseek-r1-0528-qwen3-8b.md), [Magistral Small 1.0 (2506)](magistral-small-2506.md), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank higher overall. Trails [Granite 4.0 H 1B](granite-4-0-h-1b.md), [Granite 4.0 1B](granite-4-0-1b.md), [SmolLM3 3B](smollm3-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL v3 50.8% ▁▁▁▁▁▃●█▅▄▁▁ beats 37/114 medium · avg 51% 0.4% blunt · at its level
coding · #275 of 469 · 40% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [Mistral NeMo Instruct (2407)](mistral-nemo-instruct-2407.md), [QwQ-32B-Preview](qwq-32b-preview.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 77.4% ▁·▁▁▃▄▆▅●▃▁▁ beats 81/99 easy · avg 76% 10% sharp
HumanEval+ 72.0% ▁·▁▁▁▃▄▄▄●▃▁ beats 80/99 easy · avg 70% 9.4% sharp · at its level
CRUXEval (ZeroEval, 800 problems) 58.9% ▁▂▆▃●█▆▃▂·▁▁ beats 18/47 medium · avg 57% 5.3% sharp · at its level
LiveCodeBench official (Jan 1–Nov… 24.3% ▁▁·▁▂▄●█▅▂·▂ beats 9/31 hard · avg 23% 3.8% sharp · at its level
HumanEvalSynthesize (HumanEvalPac… 65.5% ▁·▂▁▂▆▅█▄▄▁● beats 35/35 medium · avg 62% 3.1% at its level
HumanEvalExplain (HumanEvalPack, … 45.4% ▄▇█▃▄▇▄▁▇●·▁ beats 34/35 medium · avg 46% 2.5% at its level
HumanEvalFix (HumanEvalPack, 6-la… 52.4% ▁·▁▁▄██▄█▁▅● beats 33/35 medium · avg 51% 2.4% sharp · at its level
MBPP+ 69.0% ▁···▁▁▂▅▇█●▂ beats 60/75 easy · avg 66% 2.0% at its level
WebApp1K-React pass@1 (onekq-ai) 33.2% ▃▁▁●▁▄▅▃▄▇▁▄ beats 8/31 medium · avg 40% 0.9% sharp · at its level · noisy
SciCode main problems (official l… 0.0% ●···█··▂·▃·▂ beats 0/17 very hard · avg 1.0% 0.5% sharp · too hard for it
chat & writing · #266 of 402 · 17% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Ministral 3 14B](ministral-3-14b.md), [OpenThinker2 32B](openthinker2-32b.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [North Micro Vision Instruct (2.4B)](north-micro-vision-instruct.md), [Granite 4.0 H 1B](granite-4-0-h-1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 34.4% ▁▂▂▃▆▆█●▄▄▁▁ beats 87/122 medium · avg 35% 8.1% sharp · at its level
Arena-Hard (v0.1, GPT-4-1106 judg… 46.6% ▂▁▁▅█▅●▃▃▅▃▁ beats 35/60 medium · avg 47% 4.9% sharp · at its level
LMArena (text) 1276.6 ▁▁▁▂▃▃●▆▆▇█▃ beats 109/385 avg 1260 2.0% blunt
Polyrating rating (bias-aware re-… 1187.0 ▂▁▂▁▃▇█▆▇●▂▂ beats 79/95 avg 1187 1.8% typical
math · #341 of 431 · 8.8% of its score
Beats [Athene-70B](athene-70b.md) here, though they rank higher overall. Trails [Gemma 2B (base)](gemma-2b-base.md), [Stable Code 3B](stable-code-3b.md), [Zephyr 7B beta](zephyr-7b-beta.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH level 5 22.6% ▂▂▅●▄█▆▃▅▂▃▄ beats 18/92 hard · avg 28% 3.8% sharp · at its level
MATH Level 5 (ZeroEval, 721 probl… 25.1% ▁▁▁▆●▂▇▇▄··▂ beats 13/38 hard · avg 30% 2.4% sharp · at its level
GSM8K (ZeroEval, zero-shot) 93.0% ▁▄·▁▄█▅▅▅●██ beats 28/43 very easy · avg 87% 2.2% sharp
OTIS mock AIME 2024–2025 4.3% ▃▃●▃▃▃▇▇█▃▂▅ beats 27/197 very hard · avg 7.2% 0.5% sharp · noisy
other tests (15)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 78.0% ▁·▁▁▄▄▄●█▄▂▁ beats 25/53 easy · avg 75% 8.2% at its level
LiveBench 2024-07-26 37.7% ▁▁▁▂▂▃▂█●▃▅▃ beats 41/61 medium · avg 38% 8.0% at its level
ZebraLogic (ZeroEval, 1,000 puzzl… 16.8% ▁▁▃▆●█▁▁·▁▁▁ beats 23/57 hard · avg 15% 2.7% sharp
BigCodeBench (Instruct) 43.2% ▃▂·▆▆██▆▅●▃▃ beats 29/33 medium · avg 39% 2.3% at its level
BigCodeBench (Complete) 53.9% ▂▁▁▄▂▅▇█▆▂●▂ beats 55/58 medium · avg 48% 2.2% at its level
GPQA Diamond 40.6% ▁▁▁▁▂●▆▄▅▇█▅ beats 39/211 hard · avg 39% 2.2% sharp
WildBench v2 WB-Score (task macro) 47.7708 WB-Score ▁·▁▁·▃█▄▇●▅▆ beats 38/52 avg 43.019 WB-Score 2.1% sharp
CPTUB average (SpeakLeash, 0-5 ju… 75.6% ▁▂▂▂▃█▃▆●▄▂▃ beats 71/95 medium · avg 65% 1.4% at its level
Polish EQ-Bench v2 (SpeakLeash) 71.2% ▁··▁▁▂▂▆▅▆●▆ beats 58/70 medium · avg 62% 1.1% at its level
AraGen-v3 3C3H score (Inception, … 26.1% ▁▁·▁▂▂▄●▄▅▄▄ beats 21/66 hard · avg 29% 1.0% at its level
Open Persian LLM Leaderboard v2 a… 65.9% ▂▄▂▃█▄▃▆●▅▂▂ beats 66/81 medium · avg 57% 0.9% blunt · at its level
KINA (Knowledge Index of Noah's A… 26.6% ▁▁▂▁▃▄●▆▄▅▂▃ beats 12/39 very hard · avg 21% 0.8% typical
Open PT-LLM Leaderboard average (… 78.5% ▃▁▁▁▁▁▁▁▂█●▁ beats 244/276 medium · avg 72% 0.5% at its level · noisy
EuroEval Life in the UK (English … 94.3% ▁▁▁▁▁▁▁▂▄█▄● beats 135/146 easy · avg 82% 0.2% noisy
Vectara hallucination leaderboard… 95.9% ▁▁▂▃▄▄●▆▃▂▂▁ beats 84/140 very easy · avg 94% 0.1% blunt · maxed out · noisy
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 22% ▇▇▇
WildEval ZeroEval published results 5 21% ▇▇
AlpacaEval 1.0 and 2.0 leaderboards 1 8% ▇
LiveBench model judgments 1 8% ▇
Granite Code Models: A Family of Open F… 3 8% ▇
Epoch AI 3 6% ▇
Arena-Hard-Auto v0.1 leaderboard 1 5% ▇
BigCodeBench paper 2 5% ▇
LiveCodeBench official website results 1 4% ▇
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% ▇
Inception Arabic/Hindi leaderboards 1 1% ▇
WebApp1K models leaderboard 1 1% ▇
Open Persian LLM Leaderboard v2 1 1% ▇
Knowledge Index of Noah's Ark 1 1% ▇
SciCode leaderboard 1 1% ▇
Open Portuguese LLM Leaderboard 1 0% ▇
Berkeley Function Calling Leaderboard r… 1 0% ▇
EuroEval leaderboards 1 0% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 50.9
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)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · 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)) · Epoch AI ([CC BY 4.0](https://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)) · BigCodeBench paper ([CC BY 4.0](http://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)) · 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 Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Knowledge Index of Noah's Ark ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · SciCode leaderboard ([Apache-2.0](../../licenses/scicode-Apache-2.0.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)) · 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 70B Instruct (Q4_K_M, needs ~42.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Meta-Llama-3-70B-Instruct-GGUF/tree/8917ee6563e5362a9b4622a98b96423fad2b82ab))
```sh
llama cli -hf lmstudio-community/Meta-Llama-3-70B-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-3-70B-Instruct-GGUF:Q4_K_M
```
Other versions: IQ2_XS (~21.1 GB) `llama cli -hf lmstudio-community/Meta-Llama-3-70B-Instruct-GGUF:IQ2_XS`
### 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 70B Instruct (Q4_K_M, needs ~42.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Meta-Llama-3-70B-Instruct-GGUF/tree/8917ee6563e5362a9b4622a98b96423fad2b82ab))
```sh
lms get https://huggingface.co/lmstudio-community/Meta-Llama-3-70B-Instruct-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: IQ2_XS (~21.1 GB) `lms get https://huggingface.co/lmstudio-community/Meta-Llama-3-70B-Instruct-GGUF@IQ2_XS -y` · 4bit (~39.7 GB, Mac only) `lms get https://huggingface.co/mlx-community/Meta-Llama-3-70B-Instruct-4bit -y` · 8bit (~75 GB, Mac only) `lms get https://huggingface.co/mlx-community/Meta-Llama-3-70B-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 70B Instruct (Q4_0, needs ~42.83 GB, from [ollama.com](https://ollama.com/library/llama3/tags))
```sh
ollama run llama3:70b
```
Other versions: Q8_0 (~85.16 GB) `ollama run llama3:70b-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 70B Instruct (4bit, needs ~39.7 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Meta-Llama-3-70B-Instruct-4bit/tree/de868feebd6ec3704319d445cc464abf7d37ab7b))
```sh
mlx_lm.chat --model mlx-community/Meta-Llama-3-70B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Meta-Llama-3-70B-Instruct-4bit
```
Other versions: 8bit (~75 GB, Mac only) `mlx_lm.chat --model mlx-community/Meta-Llama-3-70B-Instruct-8bit`
Model repository and documentation: [huggingface.co/meta-llama](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)
[interactive version](https://whatmodel.sh/model/llama-3-70b) · [JSON](https://whatmodel.sh/api?format=json&all=1)