Side-by-side comparison
Llama 3.1 Euryale 70B v2.2 vs GLM 4.7
Sao10K
Llama 3.1 Euryale 70B v2.2
🔧 Tools
{} JSON
Input / 1M
$0.8500
Output / 1M
$0.8500
| Llama 3.1 Euryale 70B v2.2 | GLM 4.7 | |
|---|---|---|
| Provider | Sao10K | Z.ai |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 131,072 | 202,752 |
| 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 → | 0.8500 | 0.4000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.8500 | 1.7500 |
Frequently asked questions
Which is cheaper, Llama 3.1 Euryale 70B v2.2 or GLM 4.7?
Llama 3.1 Euryale 70B v2.2 is cheaper than GLM 4.7 on a 50/50 input/output blend by about $0.225 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 Euryale 70B v2.2 or GLM 4.7?
GLM 4.7 has the larger context window at 203k tokens versus 131k tokens for Llama 3.1 Euryale 70B v2.2. That means GLM 4.7 can ingest about 1.5x as much text per request.
What is the difference between Llama 3.1 Euryale 70B v2.2 and GLM 4.7?
Llama 3.1 Euryale 70B v2.2 comes from Sao10K; GLM 4.7 comes from Z.ai. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.