Title: Gemma 4 12B It vs Gemma 4 31B: Param%C3%A8tres, Licence %E2%80%94 LLM EU
Canonical: https://llmeu.com/fr/compare/gemma-4-12b-it-vs-gemma-4-31b/
Markdown mirror: https://llmeu.com/fr/compare/gemma-4-12b-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 12B It vs Gemma 4 31B

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 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/)

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

#### 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

#### 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 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

#### T%C3%A2ches

[Support multilingue](/fr/tasks/multilingual-support/)

**T%C3%A2ches:** [Support multilingue](/fr/tasks/multilingual-support/)

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- [Aya Expanse 8B vs Gemma 4 12B It](/fr/compare/aya-expanse-8b-vs-gemma-4-12b-it/)
- [Command R (08-2024) vs Gemma 4 31B](/fr/compare/command-r-08-2024-vs-gemma-4-31b/)
- [Diffusiongemma 26B A4B It vs Gemma 4 31B](/fr/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b/)
- [EuroLLM-22B-Instruct (2512) vs Gemma 4 12B It](/fr/compare/eurollm-22b-instruct-2512-vs-gemma-4-12b-it/)
- [EuroLLM-9B-Instruct (2512) vs Gemma 4 12B It](/fr/compare/eurollm-9b-instruct-2512-vs-gemma-4-12b-it/)