Phi-4
Phi-4 by Microsoft: 1 offers. From €0.063 per 1M input tokens. No offer with EU processing is listed.
Calculate costs for this modelCompare offersView price history
Profile
| Profile | |
|---|---|
| Developer | Microsoft |
| Type | Language models |
| Origin | US |
| Input types | text |
| Context (tokens) | 16,384 |
| Max. output (tokens) | 14,745 |
| Reasoning | ohne |
| Parameters (bn) | 14.7 |
| Open weights | ✓ yes |
| Licence | MIT |
| VRAM at 4-bit (GB) | 13 (RTX PRO 4000) |
| Released | – |
Offers with processing in the EU (0)
No offers in this group.
Offers outside the EU (1)
| Provider | Country | Variant | Input | Output | Source | As of |
|---|---|---|---|---|---|---|
| DeepInfra | US | standard | €0.063 | €0.13 | deepinfra.com | 14 Oct 2026 |
€ per 1M tokens. Source: providers' pricing pages, read and checked by searchit. As of 14 Oct 2026.
AI Act
Whether the developer has signed the Code of Practice for general-purpose AI models: unknown. What this means is explained under “EU and AI Act”. EU processing and AI Act
Benchmarks
| Benchmarks | |
|---|---|
| GPQA Diamond (Artificial Analysis) | 57.5 |
| Humanity's Last Exam (Artificial Analysis) | 3.8 |
| Berkeley Function-Calling Leaderboard V4 | 28.8 |
| OTIS Mock AIME 2024-2025 (Epoch AI) | 13.8 |
| Vectara HHEM-2.3 | 3.7 |
| Vectara HHEM-2.3 – antwortrate | 80.7 |
| Vectara HHEM-2.3 – halluzination | 3.7 |
| LLMStructBench: Benchmarking Large Language Model Structured Data Extraction (arXiv:2602.14743) | 0.66 |
| LLMStructBench: Benchmarking Large Language Model Structured Data Extraction (arXiv:2602.14743) – doc micro | 0.38 |
| LLMStructBench: Benchmarking Large Language Model Structured Data Extraction (arXiv:2602.14743) – f1 micro | 0.94 |
| LLMStructBench: Benchmarking Large Language Model Structured Data Extraction (arXiv:2602.14743) – score | 0.66 |
| MÖVE (Bundesdruckerei), arXiv 2606.13111 | 0.77 |
| MÖVE (Bundesdruckerei), arXiv 2606.13111 – qa | 0.69 |
| MÖVE (Bundesdruckerei), arXiv 2606.13111 – zusammenfassung | 0.77 |
| Structured Output Benchmark (SOB), arXiv 2604.25359 | 0.97 |
| Structured Output Benchmark (SOB), arXiv 2604.25359 – json pass | 0.97 |
| Structured Output Benchmark (SOB), arXiv 2604.25359 – wertgenauigkeit | 0.8 |
| MMLU-Pro (TIGER-Lab Leaderboard) | 70.4 |