[whatmodel.sh](../../README.md) / Qwen2.5 72B Instruct
# Qwen2.5 72B Instruct
Alibaba · released 2024-09 · 72B params · qwen
**score 55.9** ±0.5 · #260 of 732 · from 62 benchmark results across 30 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Qwen2.5 72B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆●▄▅▅▄▄▂▁ #209 of 313 5.8% 6
coding ▁▁▁▃▅▇●▇▅▄▃▁ #224 of 469 29% 16
chat & writing ▁▁▁▃▆▄▆●▆▇▇▃ #219 of 402 15% 9
math ▁▁▃▅█●▄█▄▂▁▁ #233 of 431 12% 6
agent tasks · #209 of 313 · 5.8% of its score
Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [DeepSeek R1 (Jan '25)](deepseek-r1.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Mini](jamba-1-5-mini.md), [Mellum2 12B-A2.5B Instruct](mellum2-12b-a2-5b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified 33.0% ▃···▃·▃·▃●·▃ beats 5/7 hard · avg 17% 2.7% sharp · at its level
ContractBench success rate (33 ob… 23.2% ▆··▆●▂▄▂▆▆█▄ beats 6/23 very hard · avg 13% 1.5% sharp
BFCL v3 63.4% ▁▁▁▁▁▃▅█●▄▁▁ beats 90/114 medium · avg 51% 0.5% blunt · at its level
Galileo Agent Leaderboard v2 (act… 51.0% ▅▃·▃▃▅▅█▃●▅▅ beats 13/19 hard · avg 26% 0.4% at its level
Multi-SWE-bench, 7 languages (Mag… 2.6% ●···▄▄·▄▄·▄█ beats 0/7 very hard · avg 2.6% 0.4% blunt · too hard for it
Galileo Agent Leaderboard v1 (too… 81.7% ▃▃▃▃●▅▆▅█▂▅▂ beats 11/31 easy · avg 78% 0.4% typical
coding · #224 of 469 · 29% of its score
Beats [Qwen3.5 4B](qwen3-5-4b.md), [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md), [s1.1 32B](s1-1-32b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench official (Jan 1–Nov… 41.7% ▁▁·▁▂▄▆█●▂·▂ beats 26/31 hard · avg 23% 5.6% sharp · at its level
CRUXEval (ZeroEval, 800 problems) 73.9% ▁▂▆▃▆█●▃▂·▁▁ beats 33/47 medium · avg 57% 5.0% sharp · at its level
Aider code editing (133 Exercism … 65.4% ▁·▁▂▄▁▆▄●▆▂▂ beats 37/62 medium · avg 51% 4.0% sharp · at its level
SWE-bench Verified 33.0% ▃···▃·▃·▃●·▃ beats 5/7 hard · avg 17% 2.7% sharp · at its level
Aider polyglot (225 exercises, tw… 8.0% █▄··●·█····▄ beats 3/7 very hard · avg 5.5% 2.4% sharp · noisy
HumanEval+ 78.4% ▁·▁▁▂▃▄▄█●▄▃ beats 104/145 easy · avg 72% 1.8% sharp
LiveCodeBench v5 (Oct 2024–Feb 20… 26.3% ▄▄·▄·····●▄█ beats 3/7 hard · avg 18% 1.5% at its level
WebApp1K-React pass@1 (onekq-ai) 35.4% ▃▁▁█●▄▅▃▄▇▁▄ beats 10/31 medium · avg 40% 1.0% sharp · at its level · noisy
MBPP+ 88.6% ██·██···██●█ beats 6/7 easy · avg 78% 1.0% typical
CodeReviewQA: change localisation… 58.3% █▂▁▁▂▂▂▃▅▅▅● beats 40/47 hard · avg 33% 0.9% sharp · at its level · noisy
BIRD-SQL (dev) 50.1% ▄··▄·●·█··▄█ beats 2/7 medium · avg 47% 0.8% at its level
CodeReviewQA: automated code refi… 48.7% ▁▁▁▁▁▁▂▂▃█▅● beats 46/47 hard · avg 32% 0.7% at its level
HumanEval (simple-evals) 80.4% ▂●▆█▅▃··▂··▂ beats 2/19 easy · avg 76% 0.6% typical
CodeReviewQA: solution identifica… 90.9% ▃··▁▂▂▃▂█▃▄● beats 47/47 medium · avg 51% 0.6% sharp · at its level · noisy
Multi-SWE-bench, 7 languages (Mag… 2.6% ●···▄▄·▄▄·▄█ beats 0/7 very hard · avg 2.6% 0.4% blunt · too hard for it
CodeReviewQA: change type recogni… 79.8% ▁▁▁······▁▃● beats 47/47 easy · avg 67% 0.0% blunt · at its level · noisy
chat & writing · #219 of 402 · 15% 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), [Granite 4.0 H Micro 3B](granite-4-0-h-micro.md), [Gemma 3n E4B](gemma-3n-e4b.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… 78.0% ▂▁▁▅█▅▄▃▃●▃▁ beats 50/60 medium · avg 47% 4.8% sharp · at its level
LMArena (text) 1302.9 ▁▁▁▂▃▃▃●▆▇█▃ beats 129/385 avg 1260 2.3% blunt
Arena-Hard v2.0 hard prompts (sty… 10.1% ▂·●▅▅·▃█▆▂▂▂ beats 1/22 very hard · avg 12% 2.2% sharp · at its level
Arabic IFEval prompt-level (Incep… 67.3% ▁·▁▁▂▁▄▄▆▄█● beats 36/38 medium · avg 46% 1.8% sharp · at its level
AlpacaEval 2 length-controlled 47.7% ▃▃·▁▁█▇●·▁·▁ beats 18/22 medium · avg 39% 1.5% sharp · at its level
Arena-Hard (version/judge unspeci… 78.4% ▃▃▂▅▃▆▅█▅●▂▃ beats 25/30 medium · avg 65% 1.0% typical
Arena-Hard v2.0 creative writing … 10.2% ●▂▂▆▂▂▃█▂▃·▂ beats 1/22 hard · avg 19% 0.5% sharp · at its level · noisy
IFEval prompt-level loose 87.6% ▁▃▁▃▁▄▁▁█▃●▁ beats 20/22 easy · avg 78% 0.4% typical
IFEval (aggregation unspecified) 85.0% ▁▂▄▂▂▄▆●█▅▂▁ beats 75/145 easy · avg 76% 0.3% typical
math · #233 of 431 · 12% 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), [Phi-2 (2.7B)](phi-2.md), [Granite 8B Code Base (4K)](granite-8b-code-base-4k.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH level 5 63.2% ▂▂▅▆▄█●▃▅▂▃▄ beats 53/92 hard · avg 28% 5.1% sharp · at its level
MATH Level 5 (ZeroEval, 721 probl… 60.2% ▁▁▁▆█▂▇▇●··▂ beats 34/38 hard · avg 30% 3.2% sharp · at its level
OTIS mock AIME 2024–2025 8.1% ▃▃▄●▃▃▇▇█▃▂▅ beats 46/197 very hard · avg 7.2% 1.4% sharp · noisy
MATH (subset unspecified) 75.9% ▃▃█▂▂●·····▁ beats 19/22 medium · avg 58% 0.7% at its level
GSM8K 89.5% ▂▃··▃▂▅█▆●▂▃ beats 18/22 very easy · avg 88% 0.6% sharp · maxed out
MGSM (simple-evals) 87.3% ▁·▁▁·▁●▃█▁▃▁ beats 8/20 easy · avg 81% 0.6% typical
other tests (27)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 85.6% ▁·▁▁▄▄▄▅█●▂▁ beats 45/53 easy · avg 75% 7.7% typical
ZebraLogic (ZeroEval, 1,000 puzzl… 26.6% ▁▁▃▆█●▁▁·▁▁▁ beats 41/57 hard · avg 15% 4.7% sharp · at its level
GPQA Diamond 49.1% ▁▁▁▁▂▄●▄▅▇█▅ beats 68/211 hard · avg 39% 3.6% sharp · at its level
MMLU 85.5% ▁▂·▁·▁▁█·▃▃● beats 20/22 easy · avg 75% 2.5% typical
BIG-Bench Hard 80.4% ▁▄·▁▁·▅█·●▄▁ beats 17/22 medium · avg 72% 2.3% sharp
GPQA Diamond (simple-evals) 49.0% ▁·····▄●▂▂▃▃ beats 6/22 hard · avg 39% 2.1% sharp · at its level
CPTUB average (SpeakLeash, 0-5 ju… 78.9% ▁▂▂▂▃█▃▆▅●▂▃ beats 79/95 medium · avg 65% 1.5% at its level
MMLU-Pro 69.6% ▁▁·▁▁▁▃▂●▅█▁ beats 87/188 medium · avg 58% 1.4% at its level
EQ-Bench 3 (emotional intelligenc… 1076.0 ▁·▂▁●▅▅▃█▄▁▁ beats 9/62 avg 1034 1.4% typical
BRIDGE clinical text, zero-shot a… 41.6% ▁▁▁▁▁▂▂▃▄▆●▃ beats 83/97 hard · avg 31% 1.4% blunt · at its level
AraGen-v3 3C3H score (Inception, … 48.9% ▁▁·▁▂▂▄█▄●▄▄ beats 41/66 hard · avg 29% 1.4% at its level
RepoQA (32K, threshold 0.8) 83.2% ▄▄▄··●▄··▄·█ beats 3/7 easy · avg 80% 1.3% typical
LiveBench 2024-08-31 52.9% ▄█·▄·●▄·▄··▄ beats 4/7 medium · avg 40% 1.3% at its level
KINA (Knowledge Index of Noah's A… 23.0% ▁▁▂▁●▄█▆▄▅▂▃ beats 7/39 very hard · avg 21% 1.2% typical
Polish EQ-Bench v2 (SpeakLeash) 68.9% ▁··▁▁▂▂▆▅▆●▆ beats 49/70 medium · avg 62% 1.1% at its level
BigCodeBench (Instruct) 43.6% ▄··▄▄···▄●█▄ beats 4/7 hard · avg 35% 1.1% at its level
Open Persian LLM Leaderboard v2 a… 65.6% ▂▄▂▃█▄▃▆●▅▂▂ beats 65/81 medium · avg 57% 1.0% blunt · at its level
EvalPerf correctness (118 tasks, … 88.3% ▂·▂▂▂▆▅▆▆▆▃● beats 27/29 easy · avg 74% 1.0% typical
PopQA 30.6% ▁▁▁▃▃▃▁●▁▃·▄ beats 14/21 hard · avg 31% 0.8% at its level
MMLU (simple-evals, zero-shot CoT) 85.3% ▁···▂▁●█▂▂▃▁ beats 7/22 easy · avg 80% 0.7% typical
SurgiQ surgical MCQ (zero-shot, l… 68.0% ▂···▂▂·▃▂▃█● beats 17/17 medium · avg 53% 0.6% at its level
DiagnosticIQ industrial maintenan… 61.2% ▁··▃▁▁▁▅●▅▁▅ beats 11/24 hard · avg 51% 0.5% blunt · at its level
Open PT-LLM Leaderboard average (… 81.2% ▃▁▁▁▁▁▁▁▂█●▁ beats 264/276 medium · avg 72% 0.5% at its level · noisy
EuroEval Life in the UK (English … 96.3% ▁▁▁▁▁▁▁▂▄█▄● beats 146/146 easy · avg 82% 0.2% noisy
Vectara hallucination leaderboard… 95.7% ▁▁▂▃▄▄●▆▃▂▂▁ beats 80/140 very easy · avg 94% 0.1% blunt · maxed out · noisy
DROP F1, 3-shot (simple-evals) 76.7% ●▂▆█▆·▃·▂▅·▂ beats 2/23 easy · avg 79% 0.1% blunt
TruthfulQA MC2 69.9% ▃▃▂·▃▃·▅█▂●▂ beats 18/21 medium · avg 60% 0.1% blunt · at its level
source results share of the score
WildEval ZeroEval published results 4 21% ▇▇
Cohere 7 11% ▇
Epoch AI 3 10% ▇
2 OLMo 2 Furious 8 9% ▇
Microsoft Research 9 9% ▇
LiveCodeBench official website results 1 6% ▇
Arena-Hard-Auto v0.1 leaderboard 1 5% ▇
Aider code editing benchmark runs 1 4% ▇
Inception Arabic/Hindi leaderboards 2 3% ▇
Arena-Hard-Auto v2.0-Preview leaderboard 2 3% ▇
LMArena leaderboard dataset 1 2% ▇
CodeReviewQA: The Code Review Comprehen… 4 2% ▇
SpeakLeash CPTUB 1 1% ▇
ContractBench: Can LLM Agents Preserve … 1 1% ▇
EQ-Bench 3 canonical leaderboard results 1 1% ▇
BRIDGE Medical Leaderboard 1 1% ▇
LiveBench model judgments 1 1% ▇
Knowledge Index of Noah's Ark 1 1% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 1% ▇
Open Persian LLM Leaderboard v2 1 1% ▇
EvalPlus / EvalPerf published evaluatio… 1 1% ▇
WebApp1K models leaderboard 1 1% ▇
Galileo Agent Leaderboard 2 1% ▇
SurgiQ: A Large-Scale Multi-Domain Benc… 1 1% ▇
Berkeley Function Calling Leaderboard r… 1 1% ▇
DiagnosticIQ: A Benchmark for LLM-Based… 1 0% ▇
Open Portuguese LLM Leaderboard 1 0% ▇
Multi-SWE-bench experiments 1 0% ▇
EuroEval leaderboards 1 0% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 55.9
Licences: WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · Cohere ([CC BY 4.0](../../licenses/report-2504.00698v2.txt)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · 2 OLMo 2 Furious ([CC BY 4.0](../../licenses/paper-2501.00656v3-CC-BY-4.0.txt)) · Microsoft Research ([CC BY 4.0](../../licenses/report-2412.08905v1.txt)) · LiveCodeBench official website results ([CC-BY-SA-4.0](../../licenses/lcb-sharealike-NOTICE.txt)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Arena-Hard-Auto v2.0-Preview leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-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/)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · ContractBench: Can LLM Agents Preserve Observation Contracts? ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · BRIDGE Medical Leaderboard ([MIT](../../licenses/bridge-medical-leaderboard-MIT.txt)) · LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · Knowledge Index of Noah's Ark ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · 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)) · 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)) · Galileo Agent Leaderboard ([Apache-2.0](../../licenses/galileo-agent-leaderboard-Apache-2.0.txt)) · SurgiQ: A Large-Scale Multi-Domain Benchmark for Evaluating Surgical Understanding in 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)) · DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules ([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)) · Multi-SWE-bench experiments ([Apache-2.0](../../licenses/multi-swe-bench-experiments-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 (DeepInfra) · $0.37/M blended · $0.36 in / $0.4 out per 1M tokens · [deepinfra.com](https://deepinfra.com/Qwen/Qwen2.5-72B-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: Qwen/Qwen2.5-72B-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 Qwen2.5 72B Instruct (Q4_K_M, needs ~44 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct-GGUF/tree/7ca3bc388f97b264c4283bc9bf1055e2abc38441))
```sh
llama cli -hf Qwen/Qwen2.5-72B-Instruct-GGUF:q4_k_m
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf Qwen/Qwen2.5-72B-Instruct-GGUF:q4_k_m
```
Other versions: Q8_0 (~77.5 GB) `llama cli -hf Qwen/Qwen2.5-72B-Instruct-GGUF:q8_0` · Q2_K (~27.3 GB) `llama cli -hf Qwen/Qwen2.5-72B-Instruct-GGUF:q2_k`
### 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 Qwen2.5 72B Instruct (Q4_K_M, needs ~44 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct-GGUF/tree/7ca3bc388f97b264c4283bc9bf1055e2abc38441))
```sh
lms get https://huggingface.co/Qwen/Qwen2.5-72B-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 (~77.5 GB) `lms get https://huggingface.co/Qwen/Qwen2.5-72B-Instruct-GGUF@q8_0 -y` · Q2_K (~27.3 GB) `lms get https://huggingface.co/Qwen/Qwen2.5-72B-Instruct-GGUF@q2_k -y` · 4bit (~40.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2.5-72B-Instruct-4bit -y` · 8bit (~77.3 GB, Mac only) `lms get https://huggingface.co/GGorman/Qwen2.5-72B-Instruct-Q8-mlx -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 Qwen2.5 72B Instruct (Q4_K_M, needs ~43.7 GB, from [ollama.com](https://ollama.com/library/qwen2.5/tags))
```sh
ollama run qwen2.5:72b
```
### 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 Qwen2.5 72B Instruct (4bit, needs ~40.9 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen2.5-72B-Instruct-4bit/tree/36a74b07390031bb18c9f12fb7c06699bc6273c4))
```sh
mlx_lm.chat --model mlx-community/Qwen2.5-72B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Qwen2.5-72B-Instruct-4bit
```
Other versions: 8bit (~77.3 GB, Mac only) `mlx_lm.chat --model GGorman/Qwen2.5-72B-Instruct-Q8-mlx`
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct)
[interactive version](https://whatmodel.sh/model/qwen2-5-72b) · [JSON](https://whatmodel.sh/api?format=json&all=1)