*Ling-3.0-flash* is a *124B-parameter Mixture-of-Experts (MoE) model*, with approximately *5.1B parameters activated per token*. The model is designed with *token efficiency and production-scale agentic inference* as key priorities, enablin…
Model details →Ling-3.0-flash vs OpenAI: gpt-oss-120b
OpenAI: gpt-oss-120b wins on 2 of 6 axes — pricing and capability skew in its favour for most workloads.
gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimize…
Model details →Side-by-side comparison
| Capability | Ling-3.0-flash | OpenAI: gpt-oss-120b | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 131K | 131K | Tie |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $0.07 | $0.04 | 🏆 OpenAI: gpt-oss-120b |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $0.22 | $0.17 | 🏆 OpenAI: gpt-oss-120b |
| Tool / function calling First-class support for emitting structured tool calls. | Yes | Yes | Tie |
| Vision input Accepts image inputs alongside text. | — | — | Tie |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | Yes | Yes | Tie |
Frequently asked questions
Is Ling-3.0-flash better than OpenAI: gpt-oss-120b?
OpenAI: gpt-oss-120b wins on 2 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between Ling-3.0-flash and OpenAI: gpt-oss-120b?
Input: $0.07 vs $0.04 per 1M tokens. Output: $0.22 vs $0.17 per 1M tokens.
What context windows do Ling-3.0-flash and OpenAI: gpt-oss-120b support?
Ling-3.0-flash supports up to 131K tokens. OpenAI: gpt-oss-120b supports up to 131K tokens.
Do both Ling-3.0-flash and OpenAI: gpt-oss-120b support tool calling?
Ling-3.0-flash: yes. OpenAI: gpt-oss-120b: yes.
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