Side-by-side comparison
Llama 3.1 8B Instruct vs Voxtral Small 24B 2507
Meta
Llama 3.1 8B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.0200
Output / 1M
$0.0500
Mistral
Voxtral Small 24B 2507
🔧 Tools
{} JSON
Input / 1M
$0.1000
Output / 1M
$0.3000
| Llama 3.1 8B Instruct | Voxtral Small 24B 2507 | |
|---|---|---|
| Provider | Meta | Mistral |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 16,384 | 32,000 |
| 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 | 0.1000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.0500 | 0.3000 |
Frequently asked questions
Which is cheaper, Llama 3.1 8B Instruct or Voxtral Small 24B 2507?
Llama 3.1 8B Instruct is cheaper than Voxtral Small 24B 2507 on a 50/50 input/output blend by about $0.165 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 Voxtral Small 24B 2507?
Voxtral Small 24B 2507 has the larger context window at 32k tokens versus 16k tokens for Llama 3.1 8B Instruct. That means Voxtral Small 24B 2507 can ingest about 2.0x as much text per request.
What is the difference between Llama 3.1 8B Instruct and Voxtral Small 24B 2507?
Llama 3.1 8B Instruct comes from Meta; Voxtral Small 24B 2507 comes from Mistral. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.