Side-by-side comparison
GPT-4 vs Llama 3.1 8B Instruct
Meta
Llama 3.1 8B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.0200
Output / 1M
$0.0500
| GPT-4 | Llama 3.1 8B Instruct | |
|---|---|---|
| Provider | OpenAI | Meta |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 8,191 | 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 → | 30.0000 | 0.0200 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 60.0000 | 0.0500 |
Frequently asked questions
Which is cheaper, GPT-4 or Llama 3.1 8B Instruct?
Llama 3.1 8B Instruct is cheaper than GPT-4 on a 50/50 input/output blend by about $44.965 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, GPT-4 or Llama 3.1 8B Instruct?
Llama 3.1 8B Instruct has the larger context window at 16k tokens versus 8k tokens for GPT-4. That means Llama 3.1 8B Instruct can ingest about 2.0x as much text per request.
What is the difference between GPT-4 and Llama 3.1 8B Instruct?
GPT-4 comes from OpenAI; 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.