Side-by-side comparison
MiniMax M2.7 vs Llama 3.1 Euryale 70B v2.2
Sao10K
Llama 3.1 Euryale 70B v2.2
🔧 Tools
{} JSON
Input / 1M
$0.8500
Output / 1M
$0.8500
| MiniMax M2.7 | Llama 3.1 Euryale 70B v2.2 | |
|---|---|---|
| Provider | MiniMax | Sao10K |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 196,608 | 131,072 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | tools, json_mode | tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.2600 | 0.8500 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 1.2000 | 0.8500 |
Frequently asked questions
Which is cheaper, MiniMax M2.7 or Llama 3.1 Euryale 70B v2.2?
MiniMax M2.7 is cheaper than Llama 3.1 Euryale 70B v2.2 on a 50/50 input/output blend by about $0.12 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, MiniMax M2.7 or Llama 3.1 Euryale 70B v2.2?
MiniMax M2.7 has the larger context window at 197k tokens versus 131k tokens for Llama 3.1 Euryale 70B v2.2. That means MiniMax M2.7 can ingest about 1.5x as much text per request.
What is the difference between MiniMax M2.7 and Llama 3.1 Euryale 70B v2.2?
MiniMax M2.7 comes from MiniMax; Llama 3.1 Euryale 70B v2.2 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.