Side-by-side comparison
Qwen3 235B A22B Thinking 2507 vs Llama 3.1 Euryale 70B v2.2
Qwen
Qwen3 235B A22B Thinking 2507
🔧 Tools
{} JSON
Input / 1M
$0.1495
Output / 1M
$1.4950
Sao10K
Llama 3.1 Euryale 70B v2.2
🔧 Tools
{} JSON
Input / 1M
$0.8500
Output / 1M
$0.8500
| Qwen3 235B A22B Thinking 2507 | Llama 3.1 Euryale 70B v2.2 | |
|---|---|---|
| Provider | Qwen | Sao10K |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 131,072 | 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 → | 0.1495 | 0.8500 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 1.4950 | 0.8500 |
Frequently asked questions
Which is cheaper, Qwen3 235B A22B Thinking 2507 or Llama 3.1 Euryale 70B v2.2?
Qwen3 235B A22B Thinking 2507 is cheaper than Llama 3.1 Euryale 70B v2.2 on a 50/50 input/output blend by about $0.0278 per 1M tokens. Exact savings depend on your input-vs-output ratio — use the cost calculator on this page for a workload-specific estimate.
What is the difference between Qwen3 235B A22B Thinking 2507 and Llama 3.1 Euryale 70B v2.2?
Qwen3 235B A22B Thinking 2507 comes from Qwen; 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.