Side-by-side comparison
DeepSeek V3.2 Exp vs Llama 3.1 70B Instruct
DeepSeek
DeepSeek V3.2 Exp
🔧 Tools
{} JSON
Input / 1M
$0.2700
Output / 1M
$0.4100
Meta
Llama 3.1 70B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.4000
Output / 1M
$0.4000
| DeepSeek V3.2 Exp | Llama 3.1 70B Instruct | |
|---|---|---|
| Provider | DeepSeek | Meta |
| 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). | tools, json_mode | tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.2700 | 0.4000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.4100 | 0.4000 |
Frequently asked questions
Which is cheaper, DeepSeek V3.2 Exp or Llama 3.1 70B Instruct?
DeepSeek V3.2 Exp is cheaper than Llama 3.1 70B Instruct on a 50/50 input/output blend by about $0.06 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 Exp or Llama 3.1 70B Instruct?
DeepSeek V3.2 Exp has the larger context window at 164k tokens versus 131k tokens for Llama 3.1 70B Instruct. That means DeepSeek V3.2 Exp can ingest about 1.3x as much text per request.
What is the difference between DeepSeek V3.2 Exp and Llama 3.1 70B Instruct?
DeepSeek V3.2 Exp comes from DeepSeek; Llama 3.1 70B Instruct comes from Meta. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.