*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 Qwen: Qwen3 30B A3B
Ling-3.0-flash wins on 2 of 6 axes — pricing and capability skew in its favour for most workloads.
Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique...
Model details →Side-by-side comparison
| Capability | Ling-3.0-flash | Qwen: Qwen3 30B A3B | 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 | $0.12 | 🏆 Ling-3.0-flash |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $0.22 | $0.50 | 🏆 Ling-3.0-flash |
| 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 Qwen: Qwen3 30B A3B?
Ling-3.0-flash 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 Qwen: Qwen3 30B A3B?
Input: $0.07 vs $0.12 per 1M tokens. Output: $0.22 vs $0.50 per 1M tokens.
What context windows do Ling-3.0-flash and Qwen: Qwen3 30B A3B support?
Ling-3.0-flash supports up to 131K tokens. Qwen: Qwen3 30B A3B supports up to 131K tokens.
Do both Ling-3.0-flash and Qwen: Qwen3 30B A3B support tool calling?
Ling-3.0-flash: yes. Qwen: Qwen3 30B A3B: yes.
Compare Ling-3.0-flash with other models
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