Side-by-side comparison
Llama 3.1 Euryale 70B v2.2 vs Qwen3 235B A22B Thinking 2507
Sao10K
Llama 3.1 Euryale 70B v2.2
🔧 Tools
{} JSON
Input / 1M
$0.8500
Output / 1M
$0.8500
Qwen
Qwen3 235B A22B Thinking 2507
🔧 Tools
{} JSON
Input / 1M
$0.1495
Output / 1M
$1.4950
| Llama 3.1 Euryale 70B v2.2 | Qwen3 235B A22B Thinking 2507 | |
|---|---|---|
| Provider | Sao10K | Qwen |
| 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 → | 0.8500 | 0.1495 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.8500 | 1.4950 |
Frequently asked questions
Which is cheaper, Llama 3.1 Euryale 70B v2.2 or Qwen3 235B A22B Thinking 2507?
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 Llama 3.1 Euryale 70B v2.2 and Qwen3 235B A22B Thinking 2507?
Llama 3.1 Euryale 70B v2.2 comes from Sao10K; Qwen3 235B A22B Thinking 2507 comes from Qwen. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.