Side-by-side comparison
GLM 4.7 vs Ling-2.6-1T
| GLM 4.7 | Ling-2.6-1T | |
|---|---|---|
| Provider | Z.ai | inclusionAI |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 202,752 | 262,144 |
| 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 → | 0.4000 | 0.3000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 1.7500 | 2.5000 |
Frequently asked questions
Which is cheaper, GLM 4.7 or Ling-2.6-1T?
GLM 4.7 is cheaper than Ling-2.6-1T on a 50/50 input/output blend by about $0.325 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, GLM 4.7 or Ling-2.6-1T?
Ling-2.6-1T has the larger context window at 262k tokens versus 203k tokens for GLM 4.7. That means Ling-2.6-1T can ingest about 1.3x as much text per request.
What is the difference between GLM 4.7 and Ling-2.6-1T?
GLM 4.7 comes from Z.ai; Ling-2.6-1T comes from inclusionAI. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.