DeepSeek V3.2
DeepSeek's open-weight reasoning model with sparse attention, native tool-use thinking, and GPT-5-level performance at a fraction of frontier pricing.
Overview
What is DeepSeek V3.2
DeepSeek V3.2 is an open-weight Mixture-of-Experts large language model released by DeepSeek-AI in December 2025. It features DeepSeek Sparse Attention, integrates reasoning directly into tool-use and function calling, and is designed to deliver GPT-5-level performance efficiently across a 160K context window.
Running it privately on Venice
On Venice, DeepSeek V3.2 runs under a private, zero-retention policy — your prompts are not stored, profiled, or used for training. Because the weights are open, you retain full sovereignty to audit and deploy the model yourself, while Venice handles private inference with native tool use, reasoning, and web search. Input pricing starts at $0.33 per million tokens, a fraction of closed frontier rivals.
Assessment
Strengths and limitations
- Open weights under MIT license: deploy and fine-tune with full sovereignty.
- Reasoning-first architecture with native tool-use integration (thinking in both thinking and non-thinking modes).
- DeepSeek Sparse Attention reduces computational complexity for long-context tasks.
- Matches GPT-5-level performance on key benchmarks at a much lower cost.
- Supports function calling, reasoning, web search, and structured JSON output on Venice.
- Not uncensored: maintains standard safety alignment that may refuse certain requests.
- 160K context window is smaller than some rivals offering 1M or 256K tokens.
- As a text model, it lacks native vision or image-generation capabilities.
- Real-world performance on niche or highly creative tasks can lag behind larger closed frontier models.
Capabilities
What it supports
- Tool use / function calling
- Vision (image input)
- Reasoning
- Web search
- Code-optimized
- Structured output (JSON schema)
- Audio input
- Video input
- Multiple image inputs
- Log probabilities
Specifications
Datasheet
- Maker
- DeepSeek-AI
- Released
- December 1, 2025
- Modality
- Text generation, reasoning, agentic tool-use
- Architecture
- Mixture-of-Experts with DeepSeek Sparse Attention (DSA) and Multi-head Latent Attention (MLA)
- Parameters
- 671B total, ~37B active per token
- Open weights
- Yes — MIT license
- Context window
- 160K tokens
- Max output
- 32.768K tokens
- Capabilities
- Function calling, Reasoning, Web search
- Privacy on Venice
- Private — zero retention
- Available on Venice since
- Dec 2025
API
Call it from your code
Venice exposes an OpenAI-compatible API. Point your base URL at Venice and pass the model id.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v3.2",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $0.33 per 1M input tokens and $0.48 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Alternatives
How it compares
| Model | Context window | Best for | Open weights | Price (Venice) |
|---|---|---|---|---|
| DeepSeek V3.2 | 160K tokens | Reasoning & agentic tool-use | Yes | $0.33 / $0.48 |
| Kimi K2.6 | 256K tokens | Long-context tasks | Yes | $0.85 / $4.66 |
| GPT-5.5 | 1M tokens | General frontier performance | No | $6.25 / $37.50 |
| GLM 4.7 Flash Heretic | 200K tokens | Fast, cheap inference | Yes | $0.14 / $0.80 |
The open-weight reasoning leader that integrates thinking directly into tool-use, undercutting closed rivals on cost.
Use cases
What it is good for
- 01Agentic workflows that require iterative reasoning and tool calling.
- 02Complex coding, mathematics, and competitive programming assistance.
- 03Long-document analysis and synthesis within a 160K token window.
- 04Cost-efficient deployment of frontier-level reasoning for startups and teams.
- 05Structured data extraction and API integration via JSON schema output.
Prompting
Getting better results
Enable thinking mode for multi-step logic, math, and coding problems.
Leverage function calling for agentic tasks — the model is trained to reason before invoking tools.
Use structured output (JSON schema) when you need reliably parseable responses.
For long contexts, take advantage of sparse attention efficiency to reduce inference costs.
Version history
Experimental predecessor that introduced DeepSeek Sparse Attention.
Current release — reasoning-first with native tool-use integration.
High-compute variant with maxed-out reasoning; API-only and no tool calls.
FAQ
Frequently asked questions
DeepSeek V3.2 is an open-weight Mixture-of-Experts large language model released by DeepSeek-AI in December 2025. It features sparse attention, integrates reasoning into tool-use, and is designed to match GPT-5-level performance efficiently.
Venice bills per token at $0.33 per 1M input tokens and $0.48 per 1M output tokens. Cached input is $0.16 per 1M tokens. There is no subscription required.
You can try DeepSeek V3.2 on Venice using the free tier or welcome credits. Beyond that, usage is billed per token in credits.
Yes. DeepSeek V3.2 is released with open weights under the MIT license, allowing self-hosting and fine-tuning. Venice runs the official weights under a private, zero-retention inference layer.
Yes. On Venice, DeepSeek V3.2 supports function calling, reasoning, web search, and structured JSON output. It is specifically trained to integrate thinking directly into tool-use scenarios.
No. DeepSeek V3.2 maintains standard safety alignment and is not an uncensored model. However, on Venice it runs under a private, zero-retention policy, so your conversations are not stored or used for training.
DeepSeek V3.2 matches GPT-5-level performance on many benchmarks at a fraction of the cost and with open-weight sovereignty. GPT-5.5 offers a larger 1M token context and may edge ahead on niche frontier tasks, but it is closed and significantly more expensive.
The model supports a 160K token context window and up to 32,768 tokens of output per request.
Yes. DeepSeek V3.2 includes a reasoning mode (thinking mode) trained with scaled reinforcement learning, making it strong at math, coding, and multi-step problem solving.
Run DeepSeek V3.2 privately
No prompt logging. No data used for training.