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deepseek/deepseek-v3-2
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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)

12/1/2025
163,840 tokens

Specifications

Modalities

Input
text
Output
text

Supported Parameters

frequency_penalty
include_reasoning
logit_bias
logprobs
max_tokens
min_p
presence_penalty
reasoning
repetition_penalty
response_format
seed
stop
structured_outputs
temperature
tool_choice
tools
top_k
top_logprobs
top_p

Max Output Tokens

163,840

Reasoning Configuration

Default
Thinking off
Turning thinking off
Possible (reasoning.enabled: false)

How to control thinking, and what it costs

Frequently asked questions

Is DeepSeek V3.2 available on FastMetal?
Not at the moment. DeepSeek V4.1 Flash, from the same lab, is available on the FastMetal API today.
What is the context window of DeepSeek V3.2?
163,840 tokens, shared between the prompt and the response.