DeepSeek: 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)
Pricing per 1M Tokens
| Input (Prompt) | $0.26 |
| Output (Completion) | $0.38 |
| Cache Read | $0.13 |
| Cache Write | Free |
| Image | N/A |
Specifications
| Context Length | 164K |
| Max Output Tokens | N/A |
| Input Modalities | Text |
| Output Modalities | Text |
| Tokenizer | DeepSeek |
| Instruct Type | N/A |
| Top Provider Context | 164K |
| Top Provider Max Output | N/A |
| Moderated | No |
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Last updated: March 23, 2026
First tracked: March 23, 2026