DeepSeek V3.2
DeepSeek V3.2 is a text generation model from DeepSeek with a 160,000-token context window, released in December 2025.
DeepSeek
DeepInfraLarge-scale text generation with extended context
DeepSeek V3.2 is a large language model developed by DeepSeek, a Chinese AI research company. It is a chat-oriented text generation model identified as deepseek-ai/DeepSeek-V3.2 and served through DeepInfra. The model supports a context window of 160,000 tokens, allowing it to process and respond to long documents, extended conversations, and complex multi-part prompts within a single session.
DeepSeek V3.2 is the successor to DeepSeek V3 and continues the company's focus on dense transformer-based language models trained at scale. It is suited for tasks such as long-document summarization, multi-turn dialogue, code generation, and instruction following. With a maximum response size of 8,000 tokens, it can produce detailed, structured outputs across a wide range of text-based applications.
What DeepSeek V3.2 supports
Long Context Processing
Handles up to 160,000 tokens in a single context window, enabling analysis of long documents, codebases, or extended conversation histories without truncation.
Text Generation
Generates coherent, instruction-following text responses across a variety of formats, with a maximum response size of 8,000 tokens per output.
Code Generation
Produces and explains code across multiple programming languages, consistent with DeepSeek's published emphasis on coding tasks in the V3 model family.
Instruction Following
Responds to structured prompts and multi-step instructions in a chat interface, supporting both single-turn and multi-turn dialogue formats.
Summarization
Condenses long-form content into concise summaries, leveraging the 160,000-token context window to process full documents in a single pass.
Reasoning Tasks
Applies multi-step reasoning to answer complex questions, analyze arguments, or work through logical problems presented in natural language.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 83.7% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 75.1% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 59.3% |
| HLE | Questions that challenge frontier models across many domains | 10.5% |
| SciCode | Scientific research coding and numerical methods | 38.7% |
| AIME 2025 | American math olympiad problems (2025) | 96.0% |
| SWE-bench Verified | Real GitHub issues requiring multi-file code fixes | 77.2% |
Common questions about DeepSeek V3.2
What is the context window size for DeepSeek V3.2?
DeepSeek V3.2 supports a context window of 160,000 tokens, which means it can process up to 160,000 tokens of combined input and conversation history in a single session.
What is the maximum response length DeepSeek V3.2 can produce?
The model has a maximum response size of 8,000 tokens per output, which is sufficient for detailed explanations, long-form writing, and structured documents.
Who developed DeepSeek V3.2 and when was it released?
DeepSeek V3.2 was developed by DeepSeek, a Chinese AI research company. It was released in December 2025 and is served on MindStudio via DeepInfra.
Does DeepSeek V3.2 support image or video inputs?
Based on the available metadata, DeepSeek V3.2 does not have confirmed support for image or video inputs. It is classified as a text generation model with text-based inputs.
What is the pricing for using DeepSeek V3.2 on MindStudio?
Pricing information for DeepSeek V3.2 is not published in the available metadata. You can check MindStudio's pricing page or the DeepInfra provider page for current rate details.
What is the knowledge cutoff date for DeepSeek V3.2?
The specific training data cutoff date for DeepSeek V3.2 is not included in the available metadata. Given its December 2025 release date, the cutoff is likely sometime in 2025, but DeepSeek's official documentation should be consulted for a confirmed date.
What people think about DeepSeek V3.2
Community reception on r/LocalLLaMA was largely positive at launch, with the Hugging Face release announcement thread receiving over 1,000 upvotes and 210 comments, reflecting strong interest in the open-weight release. Users highlighted the model's agentic capabilities and its MIT License as notable attributes.
Some users raised questions about real-world quality shortly after release, as reflected in a thread titled "is the new Deepseek v3.2 that bad?" with 68 comments discussing early impressions. A separate thread documented community experimentation with local hardware, including a 16x AMD MI50 setup achieving 10 tokens per second for text generation using vLLM.
is the new Deepseek v3.2 that bad?
deepseek-ai/DeepSeek-V3.2 · Hugging Face
16x AMD MI50 32GB at 10 t/s (tg) & 2k t/s (pp) with Deepseek v3.2 (vllm-gfx906)
Parameters & options
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