Title: Gemma 4 E4B IT vs Gemma 4 31B: Parametri, Licen%C8%9B%C4%83 %E2%80%94 LLM EU
Canonical: https://llmeu.com/ro/compare/gemma-4-e4b-it-vs-gemma-4-31b/
Markdown mirror: https://llmeu.com/ro/compare/gemma-4-e4b-it-vs-gemma-4-31b.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 E4B IT vs Gemma 4 31B

Pune%C8%9Bi dou%C4%83 p%C3%A2n%C4%83 la patru modele al%C4%83turi pe c%C3%A2mpurile care decid o implementare: context, pre%C8%9B, regiune, licen%C8%9B%C4%83, limbi, g%C4%83zduire %C8%99i ce este verificat.

Acolo unde dou%C4%83 modele difer%C4%83, acest tabel arat%C4%83 diferen%C8%9Ba %C8%99i se opre%C8%99te. LLM EU nu public%C4%83 nicio insign%C4%83 de c%C3%A2%C8%99tig%C4%83tor f%C4%83r%C4%83 un punctaj documentat pentru acea sarcin%C4%83, iar o cifr%C4%83 lips%C4%83 r%C4%83m%C3%A2ne lips%C4%83 %C3%AEn loc s%C4%83 devin%C4%83 zero.

## C%C3%A2nd se folose%C8%99te

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

#### Puncte forte

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

#### Limit%C4%83ri

- 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

#### Sarcini

[Cost redus](/ro/tasks/cheap/), [Viziune](/ro/tasks/vision/), [Suport multilingv](/ro/tasks/multilingual-support/), [Sumarizare](/ro/tasks/summarization/)

### [Gemma 4 31B](/ro/models/gemma-4-31b/)

#### Puncte forte

- 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

#### Limit%C4%83ri

- 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 32.7B parameters, self-hosting needs a high-memory 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

#### Sarcini

[Suport multilingv](/ro/tasks/multilingual-support/)

**Sarcini:** [Suport multilingv](/ro/tasks/multilingual-support/)

## Compara%C8%9Bi modele

- [Aya 23 8B vs Gemma 4 E4B IT](/ro/compare/aya-23-8b-vs-gemma-4-e4b-it/)
- [Aya Expanse 8B vs Gemma 4 E4B IT](/ro/compare/aya-expanse-8b-vs-gemma-4-e4b-it/)
- [Command R (08-2024) vs Gemma 4 31B](/ro/compare/command-r-08-2024-vs-gemma-4-31b/)
- [Deepseek Coder 7B Instruct V1.5 vs Gemma 4 E4B IT](/ro/compare/deepseek-coder-7b-instruct-v1-5-vs-gemma-4-e4b-it/)
- [DeepSeek Coder V2 Lite Instruct vs Gemma 4 E4B IT](/ro/compare/deepseek-coder-v2-lite-instruct-vs-gemma-4-e4b-it/)
- [DeepSeek R1 0528 Qwen3 8B vs Gemma 4 E4B IT](/ro/compare/deepseek-r1-0528-qwen3-8b-vs-gemma-4-e4b-it/)