Side-by-side comparison
GPT-3.5 Turbo vs Llama 3.1 Euryale 70B v2.2
Sao10K
Llama 3.1 Euryale 70B v2.2
🔧 Tools
{} JSON
Input / 1M
$0.8500
Output / 1M
$0.8500
| GPT-3.5 Turbo | Llama 3.1 Euryale 70B v2.2 | |
|---|---|---|
| Provider | OpenAI | Sao10K |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 16,385 | 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 → | 0.5000 | 0.8500 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 1.5000 | 0.8500 |
Frequently asked questions
Which is cheaper, GPT-3.5 Turbo or Llama 3.1 Euryale 70B v2.2?
Llama 3.1 Euryale 70B v2.2 is cheaper than GPT-3.5 Turbo on a 50/50 input/output blend by about $0.15 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-3.5 Turbo or Llama 3.1 Euryale 70B v2.2?
Llama 3.1 Euryale 70B v2.2 has the larger context window at 131k tokens versus 16k tokens for GPT-3.5 Turbo. That means Llama 3.1 Euryale 70B v2.2 can ingest about 8.0x as much text per request.
What is the difference between GPT-3.5 Turbo and Llama 3.1 Euryale 70B v2.2?
GPT-3.5 Turbo comes from OpenAI; Llama 3.1 Euryale 70B v2.2 comes from Sao10K. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.