Side-by-side comparison
Qwen2.5 7B Instruct vs Llama 3.1 8B Instruct
Qwen
Qwen2.5 7B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.0400
Output / 1M
$0.1000
Meta
Llama 3.1 8B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.0200
Output / 1M
$0.0500
| Qwen2.5 7B Instruct | Llama 3.1 8B Instruct | |
|---|---|---|
| Provider | Qwen | Meta |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 32,768 | 16,384 |
| 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 → | 0.0400 | 0.0200 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.1000 | 0.0500 |
Frequently asked questions
Which is cheaper, Qwen2.5 7B Instruct or Llama 3.1 8B 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, Qwen2.5 7B Instruct or Llama 3.1 8B 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 Qwen2.5 7B Instruct and Llama 3.1 8B Instruct?
Qwen2.5 7B Instruct comes from Qwen; Llama 3.1 8B 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.