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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 that reduces training and inference cost while preserving quality in long-context scenarios. A scalable reinforcement learning post-training framework further improves reasoning, with reported performance in the GPT-5 class, and the model has demonstrated gold-medal results on the 2025 IMO and IOI. V3.2 also uses a large-scale agentic task synthesis pipeline to better integrate reasoning into tool-use settings, boosting compliance and generalization in interactive environments. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)
| 信号 | 强度 | 权重 | 影响 |
|---|---|---|---|
| Benchmarksjust now | 70 | 30% | +20.9 |
| Recencyjust now | 100 | 15% | +15.0 |
| Capabilitiesjust now | 67 | 20% | +13.3 |
| Context Windowjust now | 83 | 10% | +8.3 |
| Output Capacityjust now | 20 | 10% | +2.0 |
| Pricingjust now | 0 | 15% | +0.1 |
社区和从业者反馈在基准测试和价格之上增加了真实世界的信号。
Share your experience with DeepSeek V3.2 and help the community make better decisions.
成本估算器
每月比类别平均节省$39.58
来自已验证的来源。