Ling-2.6-flash vs gpt-oss-safeguard-20b
Ling-2.6-flash
gpt-oss-safeguard-20b
| Ling-2.6-flash | gpt-oss-safeguard-20b | |
|---|---|---|
| Provider | inclusionAI | OpenAI |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary β | 262,144 | 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 β | 0.0800 | 0.0750 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3β5Γ pricier than input. Glossary β | 0.2400 | 0.3000 |
Frequently asked questions
Which is cheaper, Ling-2.6-flash or gpt-oss-safeguard-20b?
Ling-2.6-flash is cheaper than gpt-oss-safeguard-20b on a 50/50 input/output blend by about $0.0275 per 1M tokens. Exact savings depend on your input-vs-output ratio β use the cost calculator on this page for a workload-specific estimate.
Which has a larger context window, Ling-2.6-flash or gpt-oss-safeguard-20b?
Ling-2.6-flash has the larger context window at 262k tokens versus 131k tokens for gpt-oss-safeguard-20b. That means Ling-2.6-flash can ingest about 2.0x as much text per request.
What is the difference between Ling-2.6-flash and gpt-oss-safeguard-20b?
Ling-2.6-flash comes from inclusionAI; gpt-oss-safeguard-20b comes from OpenAI. They differ in pricing, context window, and supported capabilities β see the side-by-side table on this page for the exact figures, refreshed nightly.