Title: Gemma 4 E4B IT vs Gemma 4 12B It: Param%C3%A8tres, Licence %E2%80%94 LLM EU
Canonical: https://llmeu.com/fr/compare/gemma-4-e4b-it-vs-gemma-4-12b-it/
Markdown mirror: https://llmeu.com/fr/compare/gemma-4-e4b-it-vs-gemma-4-12b-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 E4B IT vs Gemma 4 12B It

Placez deux %C3%A0 quatre mod%C3%A8les c%C3%B4te %C3%A0 c%C3%B4te sur les champs qui d%C3%A9cident un d%C3%A9ploiement : contexte, prix, r%C3%A9gion, licence, langues, h%C3%A9bergement et ce qui est v%C3%A9rifi%C3%A9.

Lorsque deux mod%C3%A8les diff%C3%A8rent, ce tableau montre la diff%C3%A9rence et s'arr%C3%AAte l%C3%A0. LLM EU ne publie aucun badge de gagnant sans score document%C3%A9 pour cette t%C3%A2che, et une valeur manquante reste manquante au lieu de devenir un z%C3%A9ro.

## Quand l'utiliser

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

#### Points forts

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

#### Limites

- 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

#### T%C3%A2ches

[Faible co%C3%BBt](/fr/tasks/cheap/), [Vision](/fr/tasks/vision/), [Support multilingue](/fr/tasks/multilingual-support/), [R%C3%A9sum%C3%A9s](/fr/tasks/summarization/)

### [Gemma 4 12B It](/fr/models/gemma-4-12b-it/)

#### Points forts

- 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 12B 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

#### Limites

- 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 12B parameters, self-hosting needs a single accelerator with enough memory for the weights
- vision checkpoints are heavier to serve than their parameter count alone suggests, because the image encoder and pre-processing run alongside the language model

#### T%C3%A2ches

[Support multilingue](/fr/tasks/multilingual-support/), [Vision](/fr/tasks/vision/), [R%C3%A9sum%C3%A9s](/fr/tasks/summarization/)

**T%C3%A2ches:** [Vision](/fr/tasks/vision/), [Support multilingue](/fr/tasks/multilingual-support/), [R%C3%A9sum%C3%A9s](/fr/tasks/summarization/)

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- [Deepseek Coder 7B Instruct V1.5 vs Gemma 4 E4B IT](/fr/compare/deepseek-coder-7b-instruct-v1-5-vs-gemma-4-e4b-it/)
- [DeepSeek Coder V2 Lite Instruct vs Gemma 4 E4B IT](/fr/compare/deepseek-coder-v2-lite-instruct-vs-gemma-4-e4b-it/)
- [DeepSeek R1 0528 Qwen3 8B vs Gemma 4 E4B IT](/fr/compare/deepseek-r1-0528-qwen3-8b-vs-gemma-4-e4b-it/)
- [DeepSeek R1 Distill Llama 8B vs Gemma 4 E4B IT](/fr/compare/deepseek-r1-distill-llama-8b-vs-gemma-4-e4b-it/)