Side-by-side comparison
Llama 3.1 8B Instruct vs Qwen-Turbo
Meta
Llama 3.1 8B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.0200
Output / 1M
$0.0500
| Llama 3.1 8B Instruct | Qwen-Turbo | |
|---|---|---|
| Provider | Meta | Qwen |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 16,384 | 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 → | 0.0200 | 0.0325 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.0500 | 0.1300 |
Frequently asked questions
Which is cheaper, Llama 3.1 8B Instruct or Qwen-Turbo?
Llama 3.1 8B Instruct is cheaper than Qwen-Turbo on a 50/50 input/output blend by about $0.0463 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 8B Instruct or Qwen-Turbo?
Qwen-Turbo has the larger context window at 131k tokens versus 16k tokens for Llama 3.1 8B Instruct. That means Qwen-Turbo can ingest about 8.0x as much text per request.
What is the difference between Llama 3.1 8B Instruct and Qwen-Turbo?
Llama 3.1 8B Instruct comes from Meta; Qwen-Turbo 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.