Compare
Phi 3.5 Mini Instruct vs Phi-4
Put two to four models side by side on the fields that decide a deployment: context, price, region, license, languages, hosting, and what is verified.
| Phi 3.5 Mini Instruct | Phi-4 | |
|---|---|---|
| Provider | Microsoft | Microsoft |
| Parameters | 3.8B | 14.66B |
| Context window | Not verified: the provider has not documented this claim. | 16k |
| License | mit | mit |
| Modalities | text | text |
| Release date | 16 Aug 2024 | 11 Dec 2024 |
| Languages | Strong: — Adequate: nlp | Strong: en Adequate: — |
| Open weights | yes | yes |
| Tasks | Reasoning, Summarisation, Multilingual support | Reasoning, Summarisation, Low cost, Retrieval (RAG) |
Where two models differ, this table shows the difference and stops. LLM EU publishes no winner badge without a documented score for that task, and a missing figure stays missing instead of becoming a zero.
When to use it
Phi 3.5 Mini Instruct
Strengths
- 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
- instruction-tuned checkpoint with a chat template in the repository
Limits
- catalogue only — 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
Tasks
Phi-4
Strengths
- strong MMLU and maths scores at 14B parameters
- MIT licence without user thresholds
- fits a single 40GB accelerator at BF16
Limits
- 16K-token context window
- declared English-only; EU-language performance is not documented
- training leans on synthetic data, which narrows domain robustness
Tasks
Tasks: Reasoning, Summarisation