Side-by-side comparison
Llama 3.3 70B Instruct vs Qwen3 14B
Meta
Llama 3.3 70B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.1000
Output / 1M
$0.3200
| Llama 3.3 70B Instruct | Qwen3 14B | |
|---|---|---|
| Provider | Meta | Qwen |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 131,072 | 40,960 |
| 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 → | 0.1000 | 0.1000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.3200 | 0.2400 |
Frequently asked questions
Which is cheaper, Llama 3.3 70B Instruct or Qwen3 14B?
Qwen3 14B is cheaper than Llama 3.3 70B Instruct on a 50/50 input/output blend by about $0.04 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 3.3 70B Instruct or Qwen3 14B?
Llama 3.3 70B Instruct has the larger context window at 131k tokens versus 41k tokens for Qwen3 14B. That means Llama 3.3 70B Instruct can ingest about 3.2x as much text per request.
What is the difference between Llama 3.3 70B Instruct and Qwen3 14B?
Llama 3.3 70B Instruct comes from Meta; Qwen3 14B 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.