DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
Model details →DeepSeek: DeepSeek V3.2 vs Ling-3.0-flash
Ling-3.0-flash wins on 2 of 6 axes — pricing and capability skew in its favour for most workloads.
*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 →Side-by-side comparison
| Capability | DeepSeek: DeepSeek V3.2 | Ling-3.0-flash | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 164K | 131K | 🏆 DeepSeek: DeepSeek V3.2 |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $0.27 | $0.07 | 🏆 Ling-3.0-flash |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $0.40 | $0.22 | 🏆 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 DeepSeek: DeepSeek V3.2 better than Ling-3.0-flash?
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 DeepSeek: DeepSeek V3.2 and Ling-3.0-flash?
Input: $0.27 vs $0.07 per 1M tokens. Output: $0.40 vs $0.22 per 1M tokens.
What context windows do DeepSeek: DeepSeek V3.2 and Ling-3.0-flash support?
DeepSeek: DeepSeek V3.2 supports up to 164K tokens. Ling-3.0-flash supports up to 131K tokens.
Do both DeepSeek: DeepSeek V3.2 and Ling-3.0-flash support tool calling?
DeepSeek: DeepSeek V3.2: yes. Ling-3.0-flash: yes.
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