Side-by-side comparison
Llama 3.1 8B Instruct vs Qwen2.5 7B Instruct
Meta
Llama 3.1 8B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.0200
Output / 1M
$0.0500
Qwen
Qwen2.5 7B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.0400
Output / 1M
$0.1000
| Llama 3.1 8B Instruct | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | Meta | Qwen |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 16,384 | 32,768 |
| 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 | 0.0400 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.0500 | 0.1000 |
Frequently asked questions
Which is cheaper, Llama 3.1 8B Instruct or Qwen2.5 7B Instruct?
Llama 3.1 8B Instruct is cheaper than Qwen2.5 7B Instruct on a 50/50 input/output blend by about $0.035 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 Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct has the larger context window at 33k tokens versus 16k tokens for Llama 3.1 8B Instruct. That means Qwen2.5 7B Instruct can ingest about 2.0x as much text per request.
What is the difference between Llama 3.1 8B Instruct and Qwen2.5 7B Instruct?
Llama 3.1 8B Instruct comes from Meta; Qwen2.5 7B Instruct 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.