Side-by-side comparison
Llama 3.1 70B Instruct vs DeepSeek V3.2 Exp
Meta
Llama 3.1 70B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.4000
Output / 1M
$0.4000
DeepSeek
DeepSeek V3.2 Exp
🔧 Tools
{} JSON
Input / 1M
$0.2700
Output / 1M
$0.4100
| Llama 3.1 70B Instruct | DeepSeek V3.2 Exp | |
|---|---|---|
| Provider | Meta | DeepSeek |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 131,072 | 163,840 |
| 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 → | 0.4000 | 0.2700 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.4000 | 0.4100 |
Frequently asked questions
Which is cheaper, Llama 3.1 70B Instruct or DeepSeek V3.2 Exp?
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, Llama 3.1 70B Instruct or DeepSeek V3.2 Exp?
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 Llama 3.1 70B Instruct and DeepSeek V3.2 Exp?
Llama 3.1 70B Instruct comes from Meta; DeepSeek V3.2 Exp comes from DeepSeek. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.