Phi-4 vs Llama 3.1 8B
Phi-4 (14B) beats Llama 3.1 8B on quality despite being slightly larger. If VRAM allows, Phi-4 is the better SLM.
Microsoft Phi-4 (14B, $0.07/M input) vs Meta Llama 3.1 8B ($0.05/M input). Phi-4 is 40% more expensive but 20-30% better on reasoning benchmarks. If VRAM allows both (14GB vs 5GB at Q4), Phi-4 is worth the premium. If VRAM is severely constrained (mobile / edge), Llama 3.1 8B is your ceiling.
Side-by-side pricing
| Phi-4 | Llama 3.1 8B | |
|---|---|---|
| Provider | Microsoft | Meta |
| Input price/1M | $0.070 | $0.050 |
| Output price/1M | $0.14 | $0.080 |
| Cache read/1M | — | — |
| Context window | 16K | 128K |
| Vision | ✗ | ✗ |
| Tool use | ✗ | ✓ |
| Prompt caching | ✗ | ✗ |
| Speed tier | Fast | Fast |
Prices verified 2026-08-20. See changelog for history.
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Frequently Asked Questions
What makes Phi-4 good for its size?
Microsoft trained Phi-4 on a curated synthetic dataset specifically designed to maximize reasoning quality. Unlike larger models trained on web-crawled data, Phi-4's training data was filtered for quality and educational value. This "textbook learning" approach gives it disproportionate reasoning ability relative to its 14B parameters.
Prices verified 2026-08-20. See methodology for calculation details.