Title: Gemma 4 31B IT vs Gemma 4 31B: Par%C3%A2metros, Licen%C3%A7a %E2%80%94 LLM EU
Canonical: https://llmeu.com/pt/compare/gemma-4-31b-it-vs-gemma-4-31b/
Markdown mirror: https://llmeu.com/pt/compare/gemma-4-31b-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 31B IT vs Gemma 4 31B

Coloque dois a quatro modelos lado a lado nos campos que decidem uma implementa%C3%A7%C3%A3o: contexto, pre%C3%A7o, regi%C3%A3o, licen%C3%A7a, idiomas, alojamento e o que est%C3%A1 verificado.

Onde dois modelos diferem, esta tabela mostra a diferen%C3%A7a e para. A LLM EU n%C3%A3o publica um selo de vencedor sem uma pontua%C3%A7%C3%A3o documentada para essa tarefa, e um valor em falta permanece em falta em vez de se tornar zero.

## Quando usar

### [Gemma 4 31B IT](/pt/models/gemma-4-31b-it/)

#### Pontos fortes

- Apache-2.0 licence, unlike earlier Gemma generations
- 256K-token context window
- text and image input with variable resolution
- vendor reports multilingual coverage in over 140 languages

#### Limita%C3%A7%C3%B5es

- all published scores are self-reported by the vendor
- 31B parameters need a large-memory accelerator at BF16
- no evaluation has been run by LLM EU

#### Tarefas

[Vis%C3%A3o](/pt/tasks/vision/), [Racioc%C3%ADnio](/pt/tasks/reasoning/), [Suporte multilingue](/pt/tasks/multilingual-support/), [Codifica%C3%A7%C3%A3o](/pt/tasks/coding/), [Sumariza%C3%A7%C3%A3o](/pt/tasks/summarization/)

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

#### Pontos fortes

- 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

#### Limita%C3%A7%C3%B5es

- 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

#### Tarefas

[Suporte multilingue](/pt/tasks/multilingual-support/)

**Tarefas:** [Suporte multilingue](/pt/tasks/multilingual-support/)

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- [Diffusiongemma 26B A4B It vs Gemma 4 31B IT](/pt/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b-it/)