Title: Phi Mini MoE Instruct vs Phi-4: Parametri, Licence %E2%80%94 LLM EU
Canonical: https://llmeu.com/lv/compare/phi-mini-moe-instruct-vs-phi-4/
Markdown mirror: https://llmeu.com/lv/compare/phi-mini-moe-instruct-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 Mini MoE Instruct vs Phi-4

Sal%C4%ABdziniet divus l%C4%ABdz %C4%8Detrus mode%C4%BCus blakus p%C4%93c laukiem, kas nosaka izvieto%C5%A1anu: konteksts, cena, re%C4%A3ions, licence, valodas, mitin%C4%81%C5%A1ana un tas, kas ir verific%C4%93ts.

Ja divi mode%C4%BCi at%C5%A1%C4%B7iras, %C5%A1%C4%AB tabula par%C4%81da at%C5%A1%C4%B7ir%C4%ABbu un apst%C4%81jas. LLM EU nepublic%C4%93 uzvar%C4%93t%C4%81ja z%C4%ABmi bez dokument%C4%93ta rezult%C4%81ta %C5%A1im uzdevumam, un tr%C5%ABksto%C5%A1s skaitlis paliek tr%C5%ABksto%C5%A1s, nevis k%C4%BC%C5%ABst par nulli.

## Kad to izmantot

### [Phi Mini MoE Instruct](/lv/models/phi-mini-moe-instruct/)

#### Stipr%C4%81s puses

- 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 7.6B parameters it fits on a single accelerator
- the model page lists en
- instruction-tuned checkpoint with a chat template in the repository

#### Ierobe%C5%BEojumi

- 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 7.6B parameters, the weights fit on a single modest accelerator

#### Uzdevumi

[Sprie%C5%A1ana](/lv/tasks/reasoning/), [Kopsavilk%C5%A1ana](/lv/tasks/summarization/)

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

#### Stipr%C4%81s puses

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

#### Ierobe%C5%BEojumi

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

#### Uzdevumi

[Sprie%C5%A1ana](/lv/tasks/reasoning/), [Kopsavilk%C5%A1ana](/lv/tasks/summarization/), [Zemas izmaksas](/lv/tasks/cheap/), [Ieg%C5%AB%C5%A1ana (RAG)](/lv/tasks/rag/)

**Uzdevumi:** [Sprie%C5%A1ana](/lv/tasks/reasoning/), [Kopsavilk%C5%A1ana](/lv/tasks/summarization/)

## Sal%C4%ABdzin%C4%81t mode%C4%BCus

- [Aya 23 8B vs Phi Mini MoE Instruct](/lv/compare/aya-23-8b-vs-phi-mini-moe-instruct/)
- [Aya Expanse 8B vs Phi Mini MoE Instruct](/lv/compare/aya-expanse-8b-vs-phi-mini-moe-instruct/)
- [DeepSeek Coder V2 Lite Instruct vs Phi-4](/lv/compare/deepseek-coder-v2-lite-instruct-vs-phi-4/)
- [DeepSeek R1 0528 Qwen3 8B vs Phi Mini MoE Instruct](/lv/compare/deepseek-r1-0528-qwen3-8b-vs-phi-mini-moe-instruct/)
- [DeepSeek R1 Distill Llama 8B vs Phi Mini MoE Instruct](/lv/compare/deepseek-r1-distill-llama-8b-vs-phi-mini-moe-instruct/)
- [DeepSeek R1 Distill Qwen 14B vs Phi Mini MoE Instruct](/lv/compare/deepseek-r1-distill-qwen-14b-vs-phi-mini-moe-instruct/)