[whatmodel.sh](../../README.md) / Llama 3.1 70B Instruct
# Llama 3.1 70B Instruct
Meta · released 2024-07 · 70B params · llama-3.1
**score 53.4** ±0.4 · #310 of 732 · from 58 benchmark results across 28 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Llama 3.1 70B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃●█▄▅▅▄▄▂▁ #254 of 313 6.1% 7
coding ▁▁▁▃▅▇●▇▅▄▃▁ #251 of 469 19% 10
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #245 of 402 10% 5
math ▁▁▃▅●▆▄█▄▂▁▁ #294 of 431 16% 7
agent tasks · #254 of 313 · 6.1% of its score
Beats [DeepSeek R1 0528 Qwen3 8B](deepseek-r1-0528-qwen3-8b.md), [Sky-T1 32B Preview](sky-t1-32b-preview.md), [Magistral Small 1.0 (2506)](magistral-small-2506.md) here, though they rank higher overall. Trails [Granite 4.0 1B](granite-4-0-1b.md), [SmolLM3 3B](smollm3-3b.md), [Qwen3-VL 2B Instruct](qwen3-vl-2b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MASSIVE-Agents AST (10k, zero-sho… 18.8% █····███·●██ beats 4/6 very hard · avg 15% 1.6% sharp
BrowserGym WorkArena-L1 (AgentLab… 27.9% ●···█▄▄·▄█·█ beats 1/10 hard · avg 28% 1.1% at its level
BrowserGym WebArena (AgentLab Gen… 18.4% ●··▄·▄█·▄··▄ beats 1/7 hard · avg 17% 1.0% typical
BrowserGym MiniWoB (AgentLab Gene… 57.6% ●····▅▅▃▃·▃█ beats 1/11 medium · avg 53% 0.8% blunt · at its level
BFCL V4 (2025-08-26 archive, mode… 17.6% ●▄····█▄···▄ beats 0/5 hard · avg 26% 0.6% at its level
BFCL v3 53.6% ▁▁▁▁▁▃▅●▅▄▁▁ beats 52/114 medium · avg 51% 0.5% blunt · at its level
BrowserGym WorkArena-L2 (AgentLab… 2.1% ▃●▃·▅▃▃··█·▃ beats 1/11 very hard · avg 3.6% 0.4% sharp · too hard for it · noisy
coding · #251 of 469 · 19% 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 [QwQ-32B-Preview](qwq-32b-preview.md), [InternLM2.5 20B Chat](internlm2-5-20b-chat.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CRUXEval (ZeroEval, 800 problems) 64.3% ▁▂▆▃▆●▆▃▂·▁▁ beats 23/47 medium · avg 57% 5.0% sharp · at its level
LiveCodeBench official (Jan 1–Nov… 26.3% ▁▁·▁▂▄●█▅▂·▂ beats 14/31 hard · avg 23% 4.5% sharp · at its level
Aider code editing (133 Exercism … 58.6% ▁·▁▂▄▁▆●█▆▂▂ beats 28/62 medium · avg 51% 3.8% sharp · at its level
HumanEval (Vector Institute, Insp… 78.7% ▄·●·█·█·█·▄▄ beats 1/9 easy · avg 77% 1.9% sharp
WebApp1K-React pass@1 (onekq-ai) 10.3% ●▁▁█▁▄▅▃▄▇▁▄ beats 1/31 medium · avg 40% 1.0% sharp · at its level · noisy
CodeReviewQA: change localisation… 69.0% █▂▁▁▂▂▂▃▅▅▅● beats 47/47 hard · avg 33% 0.8% sharp · at its level · noisy
SciCode main problems (official l… 0.0% ●···█··▂·▃·▂ beats 2/17 very hard · avg 1.0% 0.7% sharp · too hard for it
CodeReviewQA: solution identifica… 76.7% ▃··▁▂▂▃▂█▃●▂ beats 40/47 medium · avg 51% 0.6% sharp · at its level · noisy
CodeReviewQA: automated code refi… 50.3% ▁▁▁▁▁▁▂▂▃█▅● beats 47/47 hard · avg 32% 0.6% at its level
CodeReviewQA: change type recogni… 68.4% ▁▁▁······▁●█ beats 16/47 easy · avg 67% 0.0% blunt · at its level · noisy
chat & writing · #245 of 402 · 10% of its score
Beats [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Qwen2.5 Coder 32B Instruct](qwen2-5-coder-32b.md), [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [Granite 4.0 H 1B](granite-4-0-h-1b.md), [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena-Hard (v0.1, GPT-4-1106 judg… 55.7% ▂▁▁▅█▅▄●▃▅▃▁ beats 39/60 medium · avg 47% 4.9% sharp · at its level
LMArena (text) 1293.4 ▁▁▁▂▃▃▃●▆▇█▃ beats 125/385 avg 1260 2.1% blunt
Arabic IFEval prompt-level (Incep… 44.8% ▁·▁▁▂▁▄▄▆●█▄ beats 24/38 medium · avg 46% 1.7% sharp · at its level
AlpacaEval 2 length-controlled 32.9% ▃▃·▁▁●▇▅·▁·▁ beats 9/22 medium · avg 39% 1.4% sharp · at its level
IFEval prompt-level loose 88.0% ▁▃▁▃▁▄▁▁█▃●▁ beats 21/22 easy · avg 78% 0.3% typical
math · #294 of 431 · 16% of its score
Beats [Molmo 2 8B](molmo-2-8b.md), [DeepSeek-Coder-V2 Instruct (0724)](deepseek-coder-v2-0724.md) here, though they rank higher overall. Trails [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Gemma 7B (base)](gemma-7b-base.md), [Mistral 7B v0.1 (base)](mistral-7b-v0-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH level 5 36.7% ▂▂▅▆●█▆▃▅▂▃▄ beats 28/92 hard · avg 28% 4.5% sharp · at its level
MATH (Vector Institute, Inspect E… 60.0% ▄··●█▄··▄·▄█ beats 1/9 medium · avg 50% 3.3% sharp · at its level
MATH Level 5 (ZeroEval, 721 probl… 43.1% ▁▁▁▆█▂●▇▄··▂ beats 21/38 hard · avg 30% 2.7% sharp · at its level
GSM8K 94.5% ▂▃··▃▂▅█▆▃▂● beats 21/22 very easy · avg 87% 2.1% sharp · small run · several runs
GSM8K (ZeroEval, zero-shot) 94.2% ▁▄·▁▄█▅▅▅█●█ beats 32/43 very easy · avg 87% 1.7% sharp
OTIS mock AIME 2024–2025 3.6% ▃▃●▃▃▃▇▇█▃▂▅ beats 24/197 very hard · avg 7.2% 0.8% sharp · noisy
MATH (subset unspecified) 56.2% ▃▃█●▂▆·····▁ beats 14/22 medium · avg 58% 0.7% at its level
other tests (27)
test result vs the others in its run difficulty counts why
LiveBench 2024-07-26 48.4% ▁▁▁▂▂▃▂█▆▃●▃ beats 51/61 medium · avg 38% 8.7% at its level
MMLU-Redux (ZeroEval, 2,778 quest… 83.0% ▁·▁▁▄▄▄▅●▄▂▁ beats 40/53 easy · avg 75% 7.7% at its level
ZebraLogic (ZeroEval, 1,000 puzzl… 24.9% ▁▁▃▆█●▁▁·▁▁▁ beats 36/57 hard · avg 15% 3.5% sharp
MMLU 85.2% ▁▂·▁·▁▁█·▃▃● beats 19/22 easy · avg 75% 3.5% at its level · several runs
MMLU-Pro 62.8% ▃▂▅█▃·●▃▅··▂ beats 14/23 medium · avg 54% 3.4% at its level
GPQA Diamond 44.2% ▁▁▁▁▂▄●▄▅▇█▅ beats 48/211 hard · avg 39% 2.8% sharp · at its level
BIG-Bench Hard 83.0% ▁▄·▁▁·▅█·▄●▁ beats 19/22 medium · avg 72% 2.3% sharp · at its level
GPQA Diamond (Vector Institute, I… 43.2% ▃··▃●▃▃·▃·▃█ beats 2/9 hard · avg 40% 2.2% sharp · at its level
MMLU-Pro (Vector Institute, Inspe… 66.9% ▃····▃●·▃▅·█ beats 2/9 medium · avg 58% 1.8% at its level
CPTUB average (SpeakLeash, 0-5 ju… 72.5% ▁▂▂▂▃█▃●▅▄▂▃ beats 59/95 medium · avg 65% 1.4% at its level
BRIDGE clinical text, zero-shot a… 39.1% ▁▁▁▁▁▂▂▃▄▆●▃ beats 64/97 hard · avg 31% 1.2% blunt · at its level
AraGen-v3 3C3H score (Inception, … 50.0% ▁▁·▁▂▂▄█▄●▄▄ beats 42/66 hard · avg 29% 1.2% at its level
HindiGen-v1 3C3H score (Inception… 70.5% ▁▁▁▁·▂·█▆●▇▁ beats 19/31 medium · avg 46% 1.1% at its level
Polish EQ-Bench v2 (SpeakLeash) 72.5% ▁··▁▁▂▂▆▅▆█● beats 61/70 medium · avg 62% 1.1% at its level
ARC Challenge (maker) 70.4% ▄▁▁▂▁▄█▇●▂▃▂ beats 118/140 medium · avg 66% 1.1% sharp · at its level · small run
EvalPerf correctness (118 tasks, … 77.3% ▂·▂▂▂▆▅●▆▆▃█ beats 12/29 easy · avg 74% 1.0% typical
Open Persian LLM Leaderboard v2 a… 68.0% ▂▄▂▃█▄▃▆▃●▂▂ beats 67/81 medium · avg 57% 0.9% blunt · at its level
PopQA 46.5% ▁▁▁▃▃▃▁█▁▃·● beats 20/21 hard · avg 31% 0.7% at its level
WinoGrande (maker) 85.1% ▁▁▁▁▂▁▁▅▇█▃● beats 130/131 easy · avg 83% 0.6% at its level · small run
LongSocialBench (full-context, 4-… 38.0% ▆●·▄█·▂▂·▄▄▂ beats 3/16 very hard · avg 36% 0.5% blunt
Open PT-LLM Leaderboard average (… 79.3% ▃▁▁▁▁▁▁▁▂█●▁ beats 255/276 medium · avg 72% 0.4% at its level · noisy
HellaSwag (maker) 86.4% ▁▁▁▂▂▂▄▆█▇▄● beats 130/135 easy · avg 85% 0.4% small run
RULER 64k 88.4% ▁▁·▁·▂▃▄█●·▁ beats 21/22 easy · avg 82% 0.4% typical
EuroEval Life in the UK (English … 93.9% ▁▁▁▁▁▁▁▂▄█▄● beats 133/146 easy · avg 82% 0.2% noisy
RULER 128k 66.6% ▄··▂▄▂▆●█▄▄▂ beats 9/21 easy · avg 67% 0.1% at its level · noisy
Vectara hallucination leaderboard… 95.0% ▁▁▂▃▄▄●▆▃▂▂▁ beats 59/140 very easy · avg 94% 0.1% blunt · maxed out · noisy
TruthfulQA MC2 66.8% ▃▃▂·▃▃·▅█●▅▂ beats 17/21 medium · avg 60% 0.1% blunt · at its level
source results share of the score
WildEval ZeroEval published results 5 21% ▇▇
Vector Institute Evaluation Leaderboard 4 9% ▇
2 OLMo 2 Furious 8 9% ▇
LiveBench model judgments 1 9% ▇
Epoch AI 3 8% ▇
Arena-Hard-Auto v0.1 leaderboard 1 5% ▇
LiveCodeBench official website results 1 5% ▇
Puzzle: Distillation-Based NAS for Infe… 5 4% ▇
Inception Arabic/Hindi leaderboards 3 4% ▇
Aider code editing benchmark runs 1 4% ▇
ServiceNow BrowserGym leaderboard 4 3% ▇
MMLU-Pro author-released evaluation runs 1 3% ▇
LMArena leaderboard dataset 1 2% ▇
CodeReviewQA: The Code Review Comprehen… 4 2% ▇
MASSIVE-Agents 1 2% ▇
SpeakLeash CPTUB 1 1% ▇
BRIDGE Medical Leaderboard 1 1% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 1% ▇
Berkeley Function Calling Leaderboard r… 2 1% ▇
EvalPlus / EvalPerf published evaluatio… 1 1% ▇
WebApp1K models leaderboard 1 1% ▇
Open Persian LLM Leaderboard v2 1 1% ▇
SciCode leaderboard 1 1% ▇
LongSocialBench: Do Long-Context LLMs U… 1 1% ▇
NVIDIA RULER repository README results … 2 0% ▇
Open Portuguese LLM Leaderboard 1 0% ▇
EuroEval leaderboards 1 0% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 53.4
Licences: WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · Vector Institute Evaluation Leaderboard ([Apache-2.0](../../licenses/vector-inspect-evals-leaderboard-Apache-2.0.txt)) · 2 OLMo 2 Furious ([CC BY 4.0](../../licenses/paper-2501.00656v3-CC-BY-4.0.txt)) · LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · 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)) · LiveCodeBench official website results ([CC-BY-SA-4.0](../../licenses/lcb-sharealike-NOTICE.txt)) · Puzzle: Distillation-Based NAS for Inference-Optimized LLMs ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · ServiceNow BrowserGym leaderboard ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · MMLU-Pro author-released evaluation runs ([Apache-2.0](../../licenses/mmlu-pro-author-runs-Apache-2.0.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · MASSIVE-Agents ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · BRIDGE Medical Leaderboard ([MIT](../../licenses/bridge-medical-leaderboard-MIT.txt)) · SpeakLeash Polish EQ-Bench 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)) · EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-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)) · SciCode leaderboard ([Apache-2.0](../../licenses/scicode-Apache-2.0.txt)) · LongSocialBench: Do Long-Context LLMs Understand Online Discussion Threads? ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · NVIDIA RULER repository README results table ([Apache-2.0](https://github.com/NVIDIA/RULER/blob/c3f5e3b4f87f97e048793bb510a3a6b19a46bf3a/LICENSE)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-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
### Through an API
api (Amazon Bedrock) · $0.72/M blended · $0.72 in / $0.72 out per 1M tokens · [docs.aws.amazon.com](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html)
[price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/amazon-bedrock/models/us.meta.llama3-1-70b-instruct-v1:0.toml): Standard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/meta/llama-3.1-70b-instruct.toml): model catalogue context ceiling (not a promise by a hosted API)
### 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 3.1 70B Instruct (Q4_K_M, needs ~42.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF/tree/8f3c3f95990547b772cc2e90a1f7a32c398f771e))
```sh
llama cli -hf lmstudio-community/Meta-Llama-3.1-70B-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-3.1-70B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~75 GB) `llama cli -hf lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF:Q8_0` · IQ2_M (~24.1 GB) `llama cli -hf lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF:IQ2_M`
### 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 3.1 70B Instruct (Q4_K_M, needs ~42.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF/tree/8f3c3f95990547b772cc2e90a1f7a32c398f771e))
```sh
lms get https://huggingface.co/lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~75 GB) `lms get https://huggingface.co/lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF@Q8_0 -y` · IQ2_M (~24.1 GB) `lms get https://huggingface.co/lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF@IQ2_M -y` · 4bit (~39.7 GB, Mac only) `lms get https://huggingface.co/mlx-community/Meta-Llama-3.1-70B-Instruct-4bit -y` · 8bit (~75 GB, Mac only) `lms get https://huggingface.co/mlx-community/Meta-Llama-3.1-70B-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 3.1 70B Instruct (Q4_K_M, needs ~42.5 GB, from [ollama.com](https://ollama.com/library/llama3.1/tags))
```sh
ollama run llama3.1:70b
```
### 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 3.1 70B Instruct (4bit, needs ~39.7 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Meta-Llama-3.1-70B-Instruct-4bit/tree/7772c93cf077b642f5503dd8d763a4176d7d406c))
```sh
mlx_lm.chat --model mlx-community/Meta-Llama-3.1-70B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Meta-Llama-3.1-70B-Instruct-4bit
```
Other versions: 8bit (~75 GB, Mac only) `mlx_lm.chat --model mlx-community/Meta-Llama-3.1-70B-Instruct-8bit`
Model repository and documentation: [huggingface.co/meta-llama](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)
[interactive version](https://whatmodel.sh/model/llama-3-1-70b) · [JSON](https://whatmodel.sh/api?format=json&all=1)