模型詳情
deepseek-v3.2
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)
模型規格
- 上下文長度
- –
- 最大輸出
- –
- 輸入/輸出模態
- –
- 發布日期
- 2025-12
02
API 端點
單一 MixRoute 閘道,OpenAI 相容
-
OpenAI-compatible
/v1/chat/completionsPOST
03
價格
固定費率,單位:/1M Tokens
輸入
$0.2800 /1M Tokens
補全
$0.4200 /1M Tokens
快取讀取
$0.0280 /1M Tokens
08
Token 成本估算
基於本頁價格的即時估算,非實際帳單
同一前綴被重複讀取的輸入占比,最高 100%
單一端點,可驗證的決策
使用相同的請求格式測試此模型與替代路由
從真實工作負載開始,再由品質、總成本與故障條件決定是否導入正式環境流量。