Side-by-side comparison
DeepSeek V3.2 Speciale vs Llama 3.3 Euryale 70B
DeepSeek
DeepSeek V3.2 Speciale
{} JSON
Input / 1M
$0.2870
Output / 1M
$0.4310
Sao10K
Llama 3.3 Euryale 70B
{} JSON
Input / 1M
$0.6500
Output / 1M
$0.7500
| DeepSeek V3.2 Speciale | Llama 3.3 Euryale 70B | |
|---|---|---|
| Provider | DeepSeek | Sao10K |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 163,840 | 131,072 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | json_mode | json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.2870 | 0.6500 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.4310 | 0.7500 |
Frequently asked questions
Which is cheaper, DeepSeek V3.2 Speciale or Llama 3.3 Euryale 70B?
DeepSeek V3.2 Speciale is cheaper than Llama 3.3 Euryale 70B on a 50/50 input/output blend by about $0.341 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, DeepSeek V3.2 Speciale or Llama 3.3 Euryale 70B?
DeepSeek V3.2 Speciale has the larger context window at 164k tokens versus 131k tokens for Llama 3.3 Euryale 70B. That means DeepSeek V3.2 Speciale can ingest about 1.3x as much text per request.
What is the difference between DeepSeek V3.2 Speciale and Llama 3.3 Euryale 70B?
DeepSeek V3.2 Speciale comes from DeepSeek; Llama 3.3 Euryale 70B 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.