Side-by-side comparison
Llama 3 8B Lunaris vs Phi 4
| Llama 3 8B Lunaris | Phi 4 | |
|---|---|---|
| Provider | Sao10K | Microsoft |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 8,192 | 16,384 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | json_mode | json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.0400 | 0.0650 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.0500 | 0.1400 |
Frequently asked questions
Which is cheaper, Llama 3 8B Lunaris or Phi 4?
Llama 3 8B Lunaris is cheaper than Phi 4 on a 50/50 input/output blend by about $0.0575 per 1M tokens. Exact savings depend on your input-vs-output ratio — use the cost calculator on this page for a workload-specific estimate.
Which has a larger context window, Llama 3 8B Lunaris or Phi 4?
Phi 4 has the larger context window at 16k tokens versus 8k tokens for Llama 3 8B Lunaris. That means Phi 4 can ingest about 2.0x as much text per request.
What is the difference between Llama 3 8B Lunaris and Phi 4?
Llama 3 8B Lunaris comes from Sao10K; Phi 4 comes from Microsoft. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.