Side-by-side comparison
Llama 3.1 70B Hanami x1 vs Sonar Deep Research
Sao10K
Llama 3.1 70B Hanami x1
Input / 1M
$3.0000
Output / 1M
$3.0000
| Llama 3.1 70B Hanami x1 | Sonar Deep Research | |
|---|---|---|
| Provider | Sao10K | Perplexity |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 16,000 | 128,000 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | text-only | text-only |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 3.0000 | 2.0000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 3.0000 | 8.0000 |
Frequently asked questions
Which is cheaper, Llama 3.1 70B Hanami x1 or Sonar Deep Research?
Llama 3.1 70B Hanami x1 is cheaper than Sonar Deep Research on a 50/50 input/output blend by about $2 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 3.1 70B Hanami x1 or Sonar Deep Research?
Sonar Deep Research has the larger context window at 128k tokens versus 16k tokens for Llama 3.1 70B Hanami x1. That means Sonar Deep Research can ingest about 8.0x as much text per request.
What is the difference between Llama 3.1 70B Hanami x1 and Sonar Deep Research?
Llama 3.1 70B Hanami x1 comes from Sao10K; Sonar Deep Research comes from Perplexity. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.