Title: Gemma 4 E2B It vs Gemma 4 E4B IT: Parameters, License %E2%80%94 LLM EU
Canonical: https://llmeu.com/en/compare/gemma-4-e2b-it-vs-gemma-4-e4b-it/
Markdown mirror: https://llmeu.com/en/compare/gemma-4-e2b-it-vs-gemma-4-e4b-it.md
Machine-readable: https://llmeu.com/llms.txt (index) - https://llmeu.com/llms-full.txt (full catalogue) - https://llmeu.com/llms-catalogue.json

# Gemma 4 E2B It vs Gemma 4 E4B IT

Put two to four models side by side on the fields that decide a deployment: context, price, region, license, languages, hosting, and what is verified.

Where two models differ, this table shows the difference and stops. LLM EU publishes no winner badge without a documented score for that task, and a missing figure stays missing instead of becoming a zero.

## When to use it

### [Gemma 4 E2B It](/en/models/gemma-4-e2b-it/)

#### Strengths

- open weights are published on Hugging Face and can be downloaded without a vendor agreement
- Apache-2.0 licence, so commercial self-hosting is permitted without a separate agreement
- at 5.1B parameters it fits on a single accelerator
- instruction-tuned checkpoint with a chat template in the repository
- speech recognition runs locally, so audio never leaves the operator premises

#### Limits

- catalogue only %E2%80%94 LLM EU does not run this model, and nothing on this card implies an LLM EU endpoint
- no evaluation has been run by LLM EU
- the published configuration records no context window, so the card carries none
- at 5.1B parameters, the weights fit on a single modest accelerator
- vision checkpoints are heavier to serve than their parameter count alone suggests, because the image encoder and pre-processing run alongside the language model

#### Tasks

[Multilingual support](/en/tasks/multilingual-support/), [Low cost](/en/tasks/cheap/), [Vision](/en/tasks/vision/)

### [Gemma 4 E4B IT](/en/models/gemma-4-e4b-it/)

#### Strengths

- native audio input in the small-model class
- Apache-2.0 licence
- runs on modest hardware
- text, image and audio in one checkpoint

#### Limits

- reasoning scores well below the larger Gemma 4 sizes
- 128K-token window, half of the medium models
- no evaluation has been run by LLM EU

#### Tasks

[Low cost](/en/tasks/cheap/), [Vision](/en/tasks/vision/), [Multilingual support](/en/tasks/multilingual-support/), [Summarisation](/en/tasks/summarization/)

**Tasks:** [Multilingual support](/en/tasks/multilingual-support/), [Low cost](/en/tasks/cheap/), [Vision](/en/tasks/vision/)

## Compare models

- [Aya 23 8B vs Gemma 4 E2B It](/en/compare/aya-23-8b-vs-gemma-4-e2b-it/)
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- [Blip2 Flan T5 Xl vs Gemma 4 E2B It](/en/compare/blip2-flan-t5-xl-vs-gemma-4-e2b-it/)
- [Blip2 Opt 2.7B vs Gemma 4 E2B It](/en/compare/blip2-opt-2-7b-vs-gemma-4-e2b-it/)
- [Bloom 3B vs Gemma 4 E2B It](/en/compare/bloom-3b-vs-gemma-4-e2b-it/)
- [NVIDIA Cosmos 3 Edge vs Gemma 4 E2B It](/en/compare/cosmos-3-edge-vs-gemma-4-e2b-it/)