[whatmodel.sh](../../README.md) / Gemma 2 9B IT
# Gemma 2 9B IT
Google · released 2024-06 · 9.24B params · gemma
**score 48.5** ±0.5 · #400 of 732 · from 56 benchmark results across 20 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Gemma 2 9B IT among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #271 of 469 8.1% 5
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #265 of 402 8.5% 6
math ▁▁▃●█▆▄█▄▂▁▁ #351 of 431 23% 8
coding · #271 of 469 · 8.1% 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), [QwQ-32B-Preview](qwq-32b-preview.md), [Phi-3 Medium 128K Instruct](phi-3-medium-128k-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CRUXEval (ZeroEval, 800 problems) 46.0% ▁▂▆●▆█▆▃▂·▁▁ beats 11/47 medium · avg 57% 6.1% sharp · at its level
CodeReviewQA: change localisation… 52.0% █▂▁▁▂▂▂▃▅▅●▇ beats 36/47 hard · avg 33% 0.7% sharp · at its level · noisy
CodeReviewQA: solution identifica… 49.6% ▃··▁▂▂▃▂●▃▄▂ beats 25/47 medium · avg 51% 0.7% sharp · at its level · noisy
CodeReviewQA: automated code refi… 39.0% ▁▁▁▁▁▁▂▂▃█●▄ beats 38/47 hard · avg 32% 0.5% at its level
CodeReviewQA: change type recogni… 74.1% ▁▁▁······▁▃● beats 29/47 easy · avg 67% 0.0% blunt · at its level · noisy
chat & writing · #265 of 402 · 8.5% 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
LMArena (text) 1267.0 ▁▁▁▂▃▃●▆▆▇█▃ beats 100/385 avg 1260 2.2% blunt
Polyrating rating (bias-aware re-… 1204.0 ▂▁▂▁▃▇█▆▇●▂▂ beats 82/95 avg 1187 2.0% typical
Arabic IFEval prompt-level (Incep… 51.5% ▁·▁▁▂▁▄▄▆▄●▄ beats 28/38 medium · avg 46% 1.8% sharp · at its level
AlpacaEval 2 length-controlled 43.7% ▃▃·▁▁█▇●·▁·▁ beats 17/22 medium · avg 39% 1.4% sharp · at its level
Arena-Hard (version/judge unspeci… 43.2% ▃▃▂●▃▆▅█▅▅▂▃ beats 5/30 medium · avg 55% 0.7% at its level · small run
IFEval prompt-level loose 69.9% ▁▃▁▃▁●▁▁█▃▃▁ beats 7/22 easy · avg 78% 0.4% typical
math · #351 of 431 · 23% of its score
Beats [Athene-70B](athene-70b.md) here, though they rank higher overall. Trails [OPT-2.7B](opt-2-7b.md), [Gemma 2B (base)](gemma-2b-base.md), [Stable Code 3B](stable-code-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K 79.7% ▂▃··▃▂●█▆▃▂▃ beats 6/22 very easy · avg 90% 6.3% sharp · several runs
MGSM 75.1% ▅▃▃█·▅···●·▃ beats 9/10 easy · avg 72% 4.2% sharp · at its level
MATH level 5 21.0% ▂▂▅●▄█▆▃▅▂▃▄ beats 17/92 hard · avg 28% 3.4% sharp
GSM8K (ZeroEval, zero-shot) 87.4% ▁▄·▁▄█▅●▅███ beats 20/43 very easy · avg 87% 3.1% sharp
MATH Level 5 (ZeroEval, 721 probl… 19.4% ▁▁▁●█▂▇▇▄··▂ beats 7/38 hard · avg 30% 2.3% sharp · at its level
MATH (subset unspecified) 29.8% ▃●█▂▂▆·····▁ beats 3/22 medium · avg 56% 2.0% at its level · several runs
MATH 51.3% █▄█●···▄▄▄·▄ beats 6/10 medium · avg 61% 1.5% at its level
OTIS mock AIME 2024–2025 0.6% ●▃▄▃▃▃▇▇█▃▂▅ beats 4/197 very hard · avg 7.2% 0.4% sharp · too hard for it · noisy
other tests (29)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 72.8% ▁·▁▁▄▄●▅█▄▂▁ beats 18/53 easy · avg 75% 10% at its level
LiveBench 2024-07-26 31.3% ▁▁▁▂▂▃▂●▆▃▅▃ beats 31/61 medium · avg 38% 8.6% at its level
MMLU 69.1% ▁▂·▁·▁▁●·▃▃▅ beats 9/22 easy · avg 75% 8.0% at its level · several runs
BIG-Bench Hard 64.9% ▁▄·▁▁·●█·▄▄▁ beats 7/22 medium · avg 72% 5.7% sharp · at its level · several runs
ARC Challenge 89.8% ▂▂▂█▂···●··▂ beats 8/10 very easy · avg 89% 3.1% sharp
ZebraLogic (ZeroEval, 1,000 puzzl… 12.8% ▁▁▃●██▁▁·▁▁▁ beats 16/57 hard · avg 15% 2.3% sharp
WildBench v2 WB-Score (task macro) 42.6962 WB-Score ▁·▁▁·▃█▄▇●▅▆ beats 31/52 avg 43.019 WB-Score 2.3% sharp
MMLU-Pro 50.1% █▄▄·█▄●▄·▄·▄ beats 7/10 medium · avg 54% 2.0% at its level
GPQA Diamond 27.5% ▁▁●▁▂▄▆▄▅▇█▅ beats 11/211 hard · avg 39% 1.9% sharp
OpenBookQA 90.0% ▂··▄█▂·▂···● beats 10/10 easy · avg 85% 1.8% typical
GPQA (subset unspecified) 39.1% █··▄▂··▂▂··● beats 9/10 hard · avg 38% 1.8% sharp · several runs
Multilingual MMLU (Microsoft) 63.8% █▄▄██··●···▄ beats 8/10 medium · avg 63% 1.6% at its level
EvalPerf correctness (118 tasks, … 68.1% ▂·▂▂▂●▅▆▆▆▃█ beats 4/29 easy · avg 74% 1.4% at its level
EQ-Bench 3 (emotional intelligenc… 975.3 ▁·●▁▄▅▅▃█▄▁▁ beats 1/62 avg 1034 1.3% typical
BRIDGE clinical text, zero-shot a… 35.1% ▁▁▁▁▁▂▂▃▄●█▃ beats 44/97 hard · avg 31% 1.2% blunt · at its level
Open Persian LLM Leaderboard v2 a… 60.6% ▂▄▂▃█▄▃●▃▅▂▂ beats 53/81 medium · avg 57% 1.0% blunt · at its level
WinoGrande 74.0% ▃····▃▃█▃·▅● beats 9/10 medium · avg 71% 0.8% at its level
HellaSwag 87.1% ▃▅█·▃▃▃···●▃ beats 9/10 easy · avg 82% 0.7% at its level
PopQA 28.3% ▁▁▁▃▃▃▁●▁▃·▄ beats 10/21 hard · avg 31% 0.6% at its level
Social IQA 74.7% ▃·▃▅●█·▃···▃ beats 5/10 medium · avg 76% 0.6% blunt · at its level
BoolQ 85.7% ▃··▃··██▃·●▃ beats 9/10 easy · avg 83% 0.5% at its level
Open PT-LLM Leaderboard average (… 74.3% ▃▁▁▁▁▁▁▁▂█●▁ beats 216/276 medium · avg 72% 0.5% at its level · noisy
SurgiQ surgical MCQ (zero-shot, l… 43.9% ▂···▂▂·●▂▃██ beats 3/17 medium · avg 53% 0.5% at its level
EuroEval HellaSwag (English), tes… 78.3% ▃·▁▁▂▁▂▅█●▄▁ beats 67/100 medium · avg 67% 0.4% sharp · at its level · noisy
CMMLU 60.3% ▅·●····█▃▅█▅ beats 2/14 medium · avg 73% 0.4% at its level · noisy
EuroEval Life in the UK (English … 84.3% ▁▁▁▁▁▁▁▂▄●▄▃ beats 94/146 easy · avg 82% 0.3% at its level · noisy
PIQA 83.7% ▃·▅·▃█·▃●··▃ beats 9/10 medium · avg 82% 0.2% blunt · at its level
TruthfulQA MC2 61.4% ▃▃▂·▃▃·●█▂▅▂ beats 9/21 medium · avg 64% 0.2% blunt · at its level · several runs
Vectara hallucination leaderboard… 89.9% ▁▁▂●▄▄█▆▃▂▂▁ beats 24/140 very easy · avg 94% 0.2% blunt · maxed out · noisy
source results share of the score
microsoft/Phi-4-mini-instruct model card 16 27% ▇▇▇
WildEval ZeroEval published results 5 24% ▇▇▇
2 OLMo 2 Furious 8 11% ▇
LiveBench model judgments 1 9% ▇
InternLM GitHub repository model cards 5 6% ▇
Epoch AI 3 6% ▇
WildBench v2 leaderboard data 1 2% ▇
LMArena leaderboard dataset 1 2% ▇
Polyrating: A Cost-Effective and Bias-A… 1 2% ▇
CodeReviewQA: The Code Review Comprehen… 4 2% ▇
Inception Arabic/Hindi leaderboards 1 2% ▇
EvalPlus / EvalPerf published evaluatio… 1 1% ▇
EQ-Bench 3 canonical leaderboard results 1 1% ▇
BRIDGE Medical Leaderboard 1 1% ▇
Open Persian LLM Leaderboard v2 1 1% ▇
Jamba-1.5: Hybrid Transformer-Mamba Mod… 1 1% ▇
EuroEval leaderboards 2 1% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
SurgiQ: A Large-Scale Multi-Domain Benc… 1 0% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 48.5
Licences: microsoft/Phi-4-mini-instruct model card ([MIT](../../licenses/card-microsoft-phi-4-mini-instruct.txt)) · WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-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)) · InternLM GitHub repository model cards ([Apache-2.0](https://github.com/InternLM/InternLM/blob/68fdc71330059255fd647abf28219ad11e8e4865/LICENSE)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · 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/)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([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)) · EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.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)) · Jamba-1.5: Hybrid Transformer-Mamba Models at Scale ([CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-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/)) · 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 Gemma 2 9B IT (Q4_K_M, needs ~5.8 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/gemma-2-9b-it-GGUF/tree/8fcfcc720a20ee55fa1c9ed602dc12aabc04e525))
```sh
llama cli -hf lmstudio-community/gemma-2-9b-it-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/gemma-2-9b-it-GGUF:Q4_K_M
```
Other versions: Q8_0 (~9.8 GB) `llama cli -hf lmstudio-community/gemma-2-9b-it-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 Gemma 2 9B IT (Q4_K_M, needs ~5.8 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/gemma-2-9b-it-GGUF/tree/8fcfcc720a20ee55fa1c9ed602dc12aabc04e525))
```sh
lms get https://huggingface.co/lmstudio-community/gemma-2-9b-it-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 (~9.8 GB) `lms get https://huggingface.co/lmstudio-community/gemma-2-9b-it-GGUF@Q8_0 -y` · 4bit (~5.2 GB, Mac only) `lms get https://huggingface.co/mlx-community/gemma-2-9b-it-4bit -y` · 8bit (~9.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/gemma-2-9b-it-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 Gemma 2 9B IT (Q4_0, needs ~6.04 GB, from [ollama.com](https://ollama.com/library/gemma2/tags))
```sh
ollama run gemma2:9b
```
Other versions: Q8_0 (~11.59 GB) `ollama run gemma2:9b-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 Gemma 2 9B IT (4bit, needs ~5.2 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/gemma-2-9b-it-4bit/tree/ff12eb39da2cd9b3b0f4b4f9ffb274603f05bb29))
```sh
mlx_lm.chat --model mlx-community/gemma-2-9b-it-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/gemma-2-9b-it-4bit
```
Other versions: 8bit (~9.8 GB, Mac only) `mlx_lm.chat --model mlx-community/gemma-2-9b-it-8bit`
Model repository and documentation: [huggingface.co/google](https://huggingface.co/google/gemma-2-9b-it)
[interactive version](https://whatmodel.sh/model/gemma-2-9b) · [JSON](https://whatmodel.sh/api?format=json&all=1)