DeepSeek: DeepSeek V3.2
Server-rendered model summary page for indexing/share previews. Use the interactive explorer for full filtering and comparison.
Identifiers & provenance
- Primary ID
- deepseek/deepseek-v3.2
- OpenRouter ID
- deepseek/deepseek-v3.2
- Canonical slug
- deepseek/deepseek-v3.2-20251201
Source semantics
- Arena rank is a human-preference leaderboard signal, not a universal truth metric.
- OpenRouter usage/popularity reflects adoption/traffic, not benchmark quality.
- Pricing fields may differ by provider and can include extra modes beyond prompt/completion.
Read more on Methodology & data sources.
Description
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)
Raw fields snapshot
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"__detail_source": "model_snapshot",
"__raw_snapshot": {
"model": {
"id": "deepseek/deepseek-v3.2",
"slug": "deepseek/deepseek-v3.2-20251201",
"display_name": "DeepSeek: DeepSeek V3.2",
"provider": "deepseek",
"description": "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.\n\nUsers 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)",
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"updated_at": "2026-03-01T02:42:37.525066+00:00",
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