Side-by-side comparison
Maestro Reasoning vs Llama 3.1 70B Hanami x1
Sao10K
Llama 3.1 70B Hanami x1
Input / 1M
$3.0000
Output / 1M
$3.0000
| Maestro Reasoning | Llama 3.1 70B Hanami x1 | |
|---|---|---|
| Provider | Arcee AI | Sao10K |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 131,072 | 16,000 |
| 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 → | 0.9000 | 3.0000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 3.3000 | 3.0000 |
Frequently asked questions
Which is cheaper, Maestro Reasoning or Llama 3.1 70B Hanami x1?
Maestro Reasoning is cheaper than Llama 3.1 70B Hanami x1 on a 50/50 input/output blend by about $0.9 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, Maestro Reasoning or Llama 3.1 70B Hanami x1?
Maestro Reasoning has the larger context window at 131k tokens versus 16k tokens for Llama 3.1 70B Hanami x1. That means Maestro Reasoning can ingest about 8.2x as much text per request.
What is the difference between Maestro Reasoning and Llama 3.1 70B Hanami x1?
Maestro Reasoning comes from Arcee AI; Llama 3.1 70B Hanami x1 comes from Sao10K. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.