Title: Phi 4 Mini Reasoning vs Phi-4: Parametre, Licencia %E2%80%94 LLM EU
Canonical: https://llmeu.com/sk/compare/phi-4-mini-reasoning-vs-phi-4/
Markdown mirror: https://llmeu.com/sk/compare/phi-4-mini-reasoning-vs-phi-4.md
Machine-readable: https://llmeu.com/llms.txt (index) - https://llmeu.com/llms-full.txt (full catalogue) - https://llmeu.com/llms-catalogue.json

# Phi 4 Mini Reasoning vs Phi-4

Postavte dva a%C5%BE %C5%A1tyri modely ved%C4%BEa seba pod%C4%BEa pol%C3%AD, ktor%C3%A9 rozhoduj%C3%BA o nasaden%C3%AD: kontext, cena, regi%C3%B3n, licencia, jazyky, hos%C5%A5ovanie a %C4%8Do je overen%C3%A9.

Ak sa dva modely l%C3%AD%C5%A1ia, t%C3%A1to tabu%C4%BEka zobraz%C3%AD rozdiel a skon%C4%8D%C3%AD. LLM EU nezverej%C5%88uje odznak v%C3%AD%C5%A5aza bez zdokumentovan%C3%A9ho sk%C3%B3re pre dan%C3%BA %C3%BAlohu a ch%C3%BDbaj%C3%BAci %C3%BAdaj zost%C3%A1va ch%C3%BDbaj%C3%BAci, namiesto toho, aby sa stal nulou.

## Kedy ho pou%C5%BEi%C5%A5

### [Phi 4 Mini Reasoning](/sk/models/phi-4-mini-reasoning/)

#### Siln%C3%A9 str%C3%A1nky

- open weights are published on Hugging Face and can be downloaded without a vendor agreement
- mit licence, so commercial self-hosting is permitted without a separate agreement
- at 3.8B parameters it fits on a single accelerator
- the model page lists en
- instruction-tuned checkpoint with a chat template in the repository

#### Obmedzenia

- 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 3.8B parameters, the weights fit on a single modest accelerator

#### %C3%9Alohy

[Uva%C5%BEovanie](/sk/tasks/reasoning/)

### [Phi-4](/sk/models/phi-4/)

#### Siln%C3%A9 str%C3%A1nky

- strong MMLU and maths scores at 14B parameters
- MIT licence without user thresholds
- fits a single 40GB accelerator at BF16

#### Obmedzenia

- 16K-token context window
- declared English-only; EU-language performance is not documented
- training leans on synthetic data, which narrows domain robustness

#### %C3%9Alohy

[Uva%C5%BEovanie](/sk/tasks/reasoning/), [Zhrnutie](/sk/tasks/summarization/), [N%C3%ADzke n%C3%A1klady](/sk/tasks/cheap/), [Vyh%C4%BEad%C3%A1vanie (RAG)](/sk/tasks/rag/)

**%C3%9Alohy:** [Uva%C5%BEovanie](/sk/tasks/reasoning/)

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