Long-Context Processing
Handles inputs up to 160,000 tokens, enabling analysis of lengthy documents, codebases, or multi-turn conversations in a single context window.
DeepSeek
DeepSeek V3.2
DeepSeek-V3.2 is an open-weight large language model developed by DeepSeek and released on December 1, 2025. It uses a Mixture-of-Experts architecture combined with a novel sparse attention mechanism called DeepSeek Sparse Attention (DSA), which reduces computational complexity to near-linear scale (O(kL)) for long-context tasks. The model supports a 160,000-token context window and is available under the MIT License on Hugging Face. DeepSeek-V3.2 introduces three notable technical advances: a scalable reinforcement learning training framework, a large-scale agentic task synthesis pipeline covering over 1,800 environments and 85,000+ complex instructions, and native support for Thinking in Tool-Use — the ability to reason while invoking external tools in both thinking and non-thinking modes. It is best suited for complex multi-step reasoning, agentic workflows involving search and code execution, long-context document processing, and developers building AI applications that require integrated reasoning and tool use.
Dec 01, 2025
160,000 context
8,000 tokens output
Long-Context Processing
Advanced Reasoning
Thinking in Tool Use
Agentic Task Execution
Code Generation
Mathematical Problem Solving
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
DeepSeek
Model ID
The routed model identifier exposed by upstream providers.
deepseek/deepseek-v3.2
Input Context Window
The number of tokens supported by the input context window.
160,000
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
8,000 tokens
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Dec 01, 2025
5 months ago
Knowledge Cut-off Date
When the model's knowledge was last updated.
December 2025
API Providers
The providers that offer this model. This is not an exhaustive list.
Baidu, SiliconFlow, DeepInfra, AtlasCloud, Novita, Parasail, Alibaba, Friendli, Google, SambaNova
Modalities
Types of data this model can process.
Text
Code
What is DeepSeek V3.2
A fuller summary of positioning, capabilities, and source-specific details for DeepSeek V3.2.
DeepSeek-V3.2 is an open-weight large language model developed by DeepSeek and released on December 1, 2025. It uses a Mixture-of-Experts architecture combined with a novel sparse attention mechanism called DeepSeek Sparse Attention (DSA), which reduces computational complexity to near-linear scale (O(kL)) for long-context tasks. The model supports a 160,000-token context window and is available under the MIT License on Hugging Face.
DeepSeek-V3.2 introduces three notable technical advances: a scalable reinforcement learning training framework, a large-scale agentic task synthesis pipeline covering over 1,800 environments and 85,000+ complex instructions, and native support for Thinking in Tool-Use — the ability to reason while invoking external tools in both thinking and non-thinking modes. It is best suited for complex multi-step reasoning, agentic workflows involving search and code execution, long-context document processing, and developers building AI applications that require integrated reasoning and tool use.
Capabilities
What DeepSeek V3.2 supports
Advanced Reasoning
Trained with a scalable reinforcement learning framework that extends post-training compute, supporting multi-step logical and mathematical reasoning tasks.
Thinking in Tool Use
Supports integrated reasoning during tool invocation, allowing the model to think through problems while calling external tools in both thinking and non-thinking modes.
Agentic Task Execution
Trained on a synthesis pipeline covering 1,800+ environments and 85,000+ complex instructions, enabling reliable performance on search, code, and general agent workflows.
Code Generation
Generates, explains, and debugs code across multiple programming languages, with demonstrated performance at competitive programming benchmarks including IOI and ICPC.
Mathematical Problem Solving
Achieves gold-medal-level results on the 2025 IMO, CMO, and ICPC World Finals benchmarks, reflecting strong symbolic and numerical reasoning capabilities.
Sparse Attention Efficiency
Uses DeepSeek Sparse Attention (DSA) to reduce attention computation to near-linear complexity (O(kL)), lowering resource requirements for long-context inference.
Open Weights Access
Released under the MIT License with full model weights available on Hugging Face, allowing local deployment and fine-tuning without usage restrictions.
Pricing for DeepSeek V3.2
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$0.26
Per million tokens
Output tokens
$0.38
Per million tokens
Price Comparison
Additional usage-cost dimensions synced into the project for this model.
Cache read
$0.03
maxTemperature
1
maxResponseSize
8,000 tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
Baidu
SiliconFlow
DeepInfra
AtlasCloud
Novita
Parasail
Alibaba
Friendli
Google
SambaNova
Provider Endpoints
Endpoint-level provider data currently available for this model.
Baidu
SiliconFlow
DeepInfra
AtlasCloud
Novita
Parasail
Alibaba
Friendli
SambaNova
Model Performance
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
|
AIME 2025
American math olympiad problems (2025)
|
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
|
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HLE
Questions that challenge frontier models across many domains
|
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LiveCodeBench
Real-world coding tasks from recent competitions
|
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MMLU-Pro
Expert knowledge across 14 academic disciplines
|
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SciCode
Scientific research coding and numerical methods
|
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SWE-bench Verified
Real GitHub issues requiring multi-file code fixes
|
Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
Community discussion
What people think about DeepSeek V3.2
DeepSeek V3.2 discussions are most active in r/LocalLLaMA, r/SillyTavernAI, r/DeepSeek. Top Reddit threads cluster around benchmark and model-comparison threads, coding workflow discussions.
The strongest match in this snapshot has 1914 upvotes and 312 comments.
I’ve been using deepseek v3.2 via open router it’s been great my only gripe is it doesn’t want to introduce swears or more mature themes all that well.
I’ve tried various qwen3 models but their outputs result in writing that doesn’t make very much cohesive sense.
I am seeking a deepseek v3.2 alternative for around the same price and outputs just as well
Or am i using it wrong? Idk, it feels like it either rushes the scenes or has nothing to add, anyone got a prompt to balance it?
DeepSeek V3.2 is still used more than DeepSeek V4
Does anyone know why?
It looks like DeepSeek V4 more expensive, but DeepSeek V3.2 better than DeepSeek V4
it practically has 90k request every time i look. why is blowing up out of the sudden? like, it has 10 intances and still gets to 99% utility and doesn't go lower than 95%.
I just switched over to V3.2 from V3.0324, and I like both models a lot, but I'm wondering if V3.2 struggles with some things compared to the latter, because I've had a bit of trouble.
(I use my models through OR, on chub) and since using V3.2 I've noticed that by default, it's answers are very short. Now, I know this can be fixed with prompting. The model seems VERY sensitive however because It will go from short, to overly long paragraphs whenever I edit the prompt, by this I mean I could say "2-3 paragraphs, 120-130 words per message" And it's still relatively short, and then I change it to: "125-130 words" And suddenly it generates extremely long replies. I don't know why it can't find an inbetween, maybe I need to tweak my prompt again.
Also, I have to put that in Assistant Prefill to even get it to listen, because sometimes it likes to ignore what I have in post/pre history so I literally have to force it. Additionally, I've been having some error replies, or it won't respond the first time and I have to resend my message. I don't know if maybe chub or OR is just down or having problems, but the message generation also seems a fair bit slower compared to DSV3.0324.
It also doesn't go into detail about a lorebook entry when I activate one, so I wonder if they're compatible, or if they are but it just ignores it. It also likes to end scenes a little too quickly, the two models are definitely both pretty different.
Personally I do prefer V3.2 overall, I just need to figure out how to tweak some of these things out of it so it works a little better.
FAQ
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, making it suitable for long-document processing, extended conversations, and large codebase analysis.
Is DeepSeek-V3.2 open source?
Yes. DeepSeek-V3.2 is released as an open-weight model under the MIT License. The model weights are publicly available on Hugging Face at huggingface.co/deepseek-ai/DeepSeek-V3.2.
What is the training data cutoff for DeepSeek-V3.2?
Based on the metadata provided, DeepSeek-V3.2 has a training date of December 2025. Specific knowledge cutoff details are documented in the official technical report.
What makes DeepSeek-V3.2 different from earlier DeepSeek models?
DeepSeek-V3.2 introduces three new capabilities not present in earlier versions: DeepSeek Sparse Attention (DSA) for near-linear attention complexity, a scalable reinforcement learning post-training framework, and a large-scale agentic task synthesis pipeline covering 1,800+ environments. It is also the first DeepSeek model to support Thinking in Tool-Use.
Can DeepSeek-V3.2 be run locally?
Yes. Because the model weights are openly available under the MIT License on Hugging Face, developers can download and run DeepSeek-V3.2 locally. Community users have demonstrated running it on hardware configurations such as 16x AMD MI50 32GB GPUs using vLLM.
What types of tasks is DeepSeek-V3.2 best suited for?
DeepSeek-V3.2 is designed for complex reasoning tasks, agentic workflows (including search and code agents), long-context retrieval, mathematical problem solving, and applications that require the model to reason while using external tools.
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