Side-by-side comparison
Ling-2.6-flash vs Qwen3 14B
inclusionAI
Ling-2.6-flash
🔧 Tools
{} JSON
Input / 1M
$0.0800
Output / 1M
$0.2400
| Ling-2.6-flash | Qwen3 14B | |
|---|---|---|
| Provider | inclusionAI | Qwen |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 262,144 | 40,960 |
| 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 → | 0.0800 | 0.1000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.2400 | 0.2400 |
Frequently asked questions
Which is cheaper, Ling-2.6-flash or Qwen3 14B?
Ling-2.6-flash is cheaper than Qwen3 14B on a 50/50 input/output blend by about $0.01 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, Ling-2.6-flash or Qwen3 14B?
Ling-2.6-flash has the larger context window at 262k tokens versus 41k tokens for Qwen3 14B. That means Ling-2.6-flash can ingest about 6.4x as much text per request.
What is the difference between Ling-2.6-flash and Qwen3 14B?
Ling-2.6-flash comes from inclusionAI; Qwen3 14B comes from Qwen. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.