[whatmodel.sh](../../README.md) / Gemma 4 E2B
# Gemma 4 E2B
Google · released 2026-04 · 5.1B params (2.3B active) · apache-2.0
**score 47.1** ±0.6 · #431 of 732 · from 78 benchmark results across 12 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Gemma 4 E2B among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁●▆█▄▅▅▄▄▂▁ #287 of 313 14% 15
coding ▁▁▁▃▅●█▇▅▄▃▁ #359 of 469 10% 8
chat & writing ▁▁▁●▆▄▆█▆▇▇▃ #375 of 402 1.7% 2
math ▁▁▃▅●▆▄█▄▂▁▁ #297 of 431 24% 6
vision & documents ▁▁●▂▁▃▄▂▆▇█▄ #138 of 153 12% 13
web search ●·▅▅█·▃▃▃·▅▃ #14 of 15* 0.9% 2
agent tasks · #287 of 313 · 14% of its score
Beats [Magistral Small 1.0 (2506)](magistral-small-2506.md), [Bielik 11B v2.3 Instruct](bielik-11b-v2-3-instruct.md), [Mixtral 8x22B Instruct v0.1](mixtral-8x22b-instruct-v0-1.md) here, though they rank higher overall. Trails [Granite 4.0 H 350M](granite-4-0-h-350m.md), [Qwen3 0.6B](qwen3-0-6b.md), [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL V4 (December 2025 archive) 36.6% ●·█·▄▄▄▄·▄·▄ beats 0/8 medium · avg 43% 3.0% at its level · several runs
Tau2 – airline (maker) 31.0% ●·▃··▃·····█ beats 0/5 medium · avg 42% 1.9% at its level
τ²-bench Telecom (maker) 19.7% ▄····●··▄·▄▄ beats 2/5 very hard · avg 13% 1.3% sharp · noisy · several runs
MCP-Atlas (public set) 12.6% ●·▁▂▂·▁▃█▆▆▂ beats 0/39 hard · avg 21% 1.1% typical
ContractBench success rate (33 ob… 7.1% ●··▆▄▂▄▂▆▆█▄ beats 2/23 very hard · avg 13% 1.1% sharp
τ²-bench aggregate (maker) 24.5% ▄·●··▄····█▄ beats 1/5 hard · avg 34% 1.1% at its level
τ³-bench Banking 3.9% ▂▂·●█▂▃▃▃▆▂▅ beats 3/24 very hard · avg 4.0% 0.9% blunt · too hard for it · several runs
Terminal-Bench Hard (maker) 3.0% ●█··█·█·█··█ beats 0/5 very hard · avg 4.7% 0.8% sharp · too hard for it
BrowseComp-ZH 4.7% ▄●·▄▄▄·█···█ beats 1/8 very hard · avg 13% 0.6% noisy
SWE-bench Verified 2.0% ▄··●▄█··▄·▄█ beats 1/8 very hard · avg 11% 0.6% sharp · too hard for it · noisy
Tau2 – retail (maker) 34.6% ▄····●··▄·▄█ beats 1/5 medium · avg 40% 0.6% at its level · noisy
τ³-bench 8.8% ▃·▃·●·▃··▃·▃ beats 2/7 very hard · avg 13% 0.4% typical
SWE-bench Pro (split unspecified) 0.0% ●▂···▂···▄·▂ beats 2/8 very hard · avg 3.9% 0.4% sharp · too hard for it · noisy
BrowseComp 3.7% ●···█·▃···▃▃ beats 1/7 very hard · avg 11% 0.3% noisy
T1-Bench multi-domain conversatio… 28.4% ▂▂··▂·▂·●▂█▄ beats 4/11 hard · avg 25% 0.0% blunt · noisy
coding · #359 of 469 · 10% 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 [Code Tulu 2 13B](code-tulu-2-13b.md), [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench v6 (window unspecif… 42.9% ▃····●·▃▅▃·▃ beats 3/8 medium · avg 38% 3.9% at its level · several runs
LiveCodeBench Pro 2025Q2 (Easy) 27.1% ▄···●▄··▄█▄▄ beats 1/8 hard · avg 33% 1.6% at its level
LiveCodeBench v6 (Feb–May 2025) (… 44.0% ▄··●·▄···▄·█ beats 1/5 medium · avg 43% 1.1% at its level
SciCode (maker) 21.0% ●▄·······▄▄▄ beats 1/5 hard · avg 21% 1.0% typical
SciCode (level unspecified) 20.5% █·····█●████ beats 2/6 hard · avg 17% 0.7% typical
SWE-bench Verified 2.0% ▄··●▄█··▄·▄█ beats 1/8 very hard · avg 11% 0.6% sharp · too hard for it · noisy
LiveCodeBench Pro 2025Q2 (Medium) 0.0% ●···▂··▄▂··▂ beats 1/8 very hard · avg 2.0% 0.6% sharp · too hard for it · noisy
SWE-bench Pro (split unspecified) 0.0% ●▂···▂···▄·▂ beats 2/8 very hard · avg 3.9% 0.4% sharp · too hard for it · noisy
chat & writing · #375 of 402 · 1.7% of its score
Beats [Code Llama 70B Instruct](codellama-70b-instruct.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [Granite 4.0 350M](granite-4-0-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
IFEval (aggregation unspecified) 31.4% ●▄····▄··███ beats 0/8 easy · avg 78% 1.0% several runs
IFEval (maker) 94.6% ▄··●··█···▄▄ beats 1/5 very easy · avg 94% 0.8% maxed out
math · #297 of 431 · 24% of its score
Beats [Molmo 2 8B](molmo-2-8b.md), [DeepSeek-Coder-V2 Instruct (0724)](deepseek-coder-v2-0724.md), [Llama 3.1 Nemotron 70B Instruct](llama-3-1-nemotron-70b.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
HMMT February 2026 17.8% ●··▃▃·▃··▃·█ beats 0/8 hard · avg 32% 7.8% sharp · several runs
AIME 2025 31.7% ●▄▄▄▄····█·▄ beats 1/8 medium · avg 42% 5.2% sharp · at its level · several runs
AIME 2026 39.8% ▄·●█·▄▄···█▄ beats 1/8 medium · avg 48% 4.8% sharp · at its level · several runs
MATH-500 85.4% ●█▄▄▄··▄···▄ beats 0/8 very easy · avg 89% 2.5% typical
IMO-AnswerBench 22.6% ●▄···▄·▄··█▄ beats 0/6 hard · avg 32% 2.2% at its level
MATH-Vision (maker) 52.4% █●·█·····███ beats 1/5 medium · avg 53% 1.7% at its level
vision & documents · #138 of 153 · 12% of its score
Trails [Qwen3-VL 2B Instruct](qwen3-vl-2b-instruct.md), [Ministral 3 3B](ministral-3-3b.md), [Qwen2-VL 7B Instruct](qwen2-vl-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
InfographicVQA (maker) 63.9% ●█·······█·▄ beats 0/5 easy · avg 70% 2.2% at its level
MMMU Pro (maker) 44.2% ●█·█····█·██ beats 0/5 medium · avg 48% 2.0% at its level
CharXiv descriptive questions 75.1% █·▄·······●█ beats 3/5 easy · avg 78% 1.8% at its level
MATH-Vision (maker) 52.4% █●·█·····███ beats 1/5 medium · avg 53% 1.7% at its level
MMStar 52.9% ▄▄▄●·······▄ beats 3/5 medium · avg 61% 1.4% at its level
OmniDocBench 1.5 ↓ (maker) 29.0% █·●·····████ beats 1/5 avg 31% 0.7% blunt
MMBench-EN dev v1.1 69.3% ●·····▃··▃·▃ beats 2/5 easy · avg 79% 0.7% blunt
HallusionBench 59.8% ▄●·▄·····█·▄ beats 1/5 easy · avg 66% 0.6% blunt · at its level
RealWorldQA 50.7% ●···█··▄▄··▄ beats 0/5 easy · avg 68% 0.3% blunt · at its level
CountBench 53.4% ●··▄··█··▄·▄ beats 0/5 easy · avg 84% 0.3% noisy
OCRBench 71.9% █··●██·█···█ beats 1/5 easy · avg 81% 0.2% blunt
AI2D test 71.2% ●···▄▄···▄·▄ beats 0/5 easy · avg 81% 0.2% blunt · at its level
ChartQA 42.2% ●······▄··██ beats 0/5 easy · avg 79% 0.1% blunt · noisy
web search · #14 of 15 · 0.9% of its score
Beats [Nemotron 3 Nano 4B](nemotron-3-nano-4b.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
BrowseComp-ZH 4.7% ▄●·▄▄▄·█···█ beats 1/8 very hard · avg 13% 0.6% noisy
BrowseComp 3.7% ●···█·▃···▃▃ beats 1/7 very hard · avg 11% 0.3% noisy
other tests (21)
test result vs the others in its run difficulty counts why
MMLU-Pro 56.0% ●··▂█·▂▂···▂ beats 0/8 medium · avg 60% 6.9% at its level · several runs
GPQA Diamond (maker) 43.4% ●···▄·····▄█ beats 1/5 hard · avg 46% 3.1% sharp
EuroEval Life in the UK (English … 69.7% ▂·●▂·▂·▃▃·█▃ beats 1/14 easy · avg 80% 3.1% at its level
MMMLU (maker) 67.4% ●█··█···█·██ beats 0/5 medium · avg 69% 2.6% at its level
IFBench (aggregation unspecified) 25.7% ●····▃▃·█·▃▃ beats 0/8 medium · avg 39% 2.5% at its level · several runs
MMLU-Redux 71.8% ●···█··▂▂··▂ beats 0/8 easy · avg 76% 2.2% at its level
GPQA (subset unspecified) 43.8% ●···▃···▅·▃▃ beats 0/7 hard · avg 44% 2.1% sharp
MMLU 69.2% ▄·····▄▄··▄● beats 5/5 medium · avg 69% 1.9% at its level
BRIDGE clinical text, zero-shot a… 32.7% ▁▁▁▁▁▂▂▃●▆█▃ beats 37/97 hard · avg 31% 1.9% blunt · at its level
Open Persian LLM Leaderboard v2 a… 50.5% ▂▄▂▃●▄▃▆▃▅▂▂ beats 34/81 medium · avg 57% 1.7% blunt · at its level
Multi-IF (aggregation unspecified) 40.3% ●▄···▄·▄·▄██ beats 0/8 medium · avg 57% 1.6% at its level · several runs
IFBench (maker) 38.0% ▄●·▄······▄█ beats 1/5 medium · avg 40% 1.4% at its level
EuroEval HellaSwag (English), val… 53.6% ▂··●▂·▂▆▆█·▄ beats 1/15 medium · avg 63% 1.4% at its level
Humanity's Last Exam 2.5% ▄●·▄··▄·▄·▄█ beats 1/7 very hard · avg 3.8% 1.3% too hard for it
MedXPertQA MM (maker) 23.5% ▂···●·▃··▆▂█ beats 1/13 very hard · avg 30% 1.3% sharp
MRCR v2 8-needle, 128k (maker) 19.1% ▄●·▄···█···▄ beats 1/5 hard · avg 22% 1.1% typical
SuperGPQA 30.3% ▃·●·▃▅█····▃ beats 1/8 hard · avg 32% 1.0% at its level
Big Bench Extra Hard micro avg (m… 21.9% ●··▄···▄·▄·▄ beats 1/5 hard · avg 28% 1.0% typical
OJBench 11.6% ▃····▃●··█▃▃ beats 3/8 very hard · avg 12% 0.9% typical
Claw-Eval average score 53.1% ●·▄··█▄▄▄▄▄▄ beats 0/9 medium · avg 53% 0.8% blunt · at its level
LongBench v2 33.2% ▃······▅●▃·█ beats 4/8 very hard · avg 32% 0.7% too hard for it
source results share of the score
openbmb/MiniCPM5-2B model card 20 29% ▇▇▇
Gemma Team (submitted / maker-reported) 19 29% ▇▇▇
XHToken/Spark-X2.5-4B model card 11 14% ▇▇
CohereLabs/North-Micro-Vision-Instruct … 13 11% ▇
LiquidAI/LFM2.5-2.6B model card 7 7% ▇
EuroEval leaderboards 2 4% ▇
BRIDGE Medical Leaderboard 1 2% ▇
Open Persian LLM Leaderboard v2 1 2% ▇
ContractBench: Can LLM Agents Preserve … 1 1% ▇
google/gemma-4-31B-it model card (submi… 1 1% ▇
LiquidAI/LFM2.5-8B-A1B model card 1 0% ▇
T1-Bench: Benchmarking Multi-Scenario A… 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 47.1
Licences: openbmb/MiniCPM5-2B model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Gemma Team ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · XHToken/Spark-X2.5-4B model card ([Apache-2.0](https://huggingface.co/XHToken/Spark-X2.5-4B/blob/0bcb35678590218655dff3765b9e61c83b35e9c4/README.md)) · CohereLabs/North-Micro-Vision-Instruct model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · LiquidAI/LFM2.5-2.6B model card ([LFM Open License v1.0](https://huggingface.co/LiquidAI/LFM2.5-2.6B/blob/654f9463ce32b05d0429d76fe1f580b27d4c1ac0/LICENSE)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · 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)) · ContractBench: Can LLM Agents Preserve Observation Contracts? ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · google/gemma-4-31B-it model card ([Apache-2.0](../../licenses/google--gemma-4-31B-it.txt)) · LiquidAI/LFM2.5-8B-A1B model card ([LFM Open License v1.0](../../licenses/cards-gap-NOTICE.txt)) · T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## How to run it
### Through an API
api (Amazon Bedrock) · $0.050/M blended · $0.04 in / $0.08 out per 1M tokens · [docs.aws.amazon.com](https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-google-gemma-4-e2b.html)
[price source](https://aws.amazon.com/bedrock/pricing): USD per 1M tokens, Amazon Bedrock on-demand Gemma 4 E2B, US East (N. Virginia)/Ohio and US West (Oregon); Frankfurt and other regions priced higher. Currency: USD. Amazon Bedrock official pricing page, observed 2026-09-25. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/google/gemma-4-E2B-it.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 Gemma 4 E2B (Q4_K_M, needs ~3.4 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/gemma-4-E2B-it-GGUF/tree/eed445ccfb8aef2e279df1a97e133baf06e443b4))
```sh
llama cli -hf lmstudio-community/gemma-4-E2B-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-4-E2B-it-GGUF:Q4_K_M
```
Other versions: Q8_0 (~5 GB) `llama cli -hf lmstudio-community/gemma-4-E2B-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 4 E2B (Q4_K_M, needs ~3.4 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/gemma-4-E2B-it-GGUF/tree/eed445ccfb8aef2e279df1a97e133baf06e443b4))
```sh
lms get https://huggingface.co/lmstudio-community/gemma-4-E2B-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 (~5 GB) `lms get https://huggingface.co/lmstudio-community/gemma-4-E2B-it-GGUF@Q8_0 -y` · 4bit (~4.3 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/gemma-4-E2B-it-MLX-4bit -y` · 8bit (~5.9 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/gemma-4-E2B-it-MLX-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 4 E2B (Q4_K_M, needs ~3.56 GB, from [ollama.com](https://ollama.com/library/gemma4/tags))
```sh
ollama run gemma4:e2b
```
Other versions: qat (~3.56 GB) `ollama run gemma4:e2b-it-qat`
### 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 4 E2B (4bit, needs ~4.3 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/gemma-4-E2B-it-MLX-4bit/tree/31512dab743c73974a99795270031a6f85072d94))
```sh
mlx_lm.chat --model lmstudio-community/gemma-4-E2B-it-MLX-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model lmstudio-community/gemma-4-E2B-it-MLX-4bit
```
Other versions: 8bit (~5.9 GB, Mac only) `mlx_lm.chat --model lmstudio-community/gemma-4-E2B-it-MLX-8bit`
Model repository and documentation: [huggingface.co/google](https://huggingface.co/google/gemma-4-E2B-it)
[interactive version](https://whatmodel.sh/model/gemma-4-e2b) · [JSON](https://whatmodel.sh/api?format=json&all=1)