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.
Get API key- Provider
- DeepSeek-AI
- Price
- $0.33 in · $0.48 out / 1M
- Context window
- 160K tokens
- Released
- December 1, 2025
- License
- MIT
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.
Use 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.
What can it do?
- •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.
DeepSeek V3.2 capabilities
- 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
How to use it via API
Venice exposes an OpenAI-compatible API. Swap your base URL and call deepseek-v3.2.
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." }]
}'Specifications
Pricing
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.
DeepSeek V3.2 vs alternatives
| 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.
What is it good for?
- •Agentic workflows that require iterative reasoning and tool calling.
- •Complex coding, mathematics, and competitive programming assistance.
- •Long-document analysis and synthesis within a 160K token window.
- •Cost-efficient deployment of frontier-level reasoning for startups and teams.
- •Structured data extraction and API integration via JSON schema output.
Prompting tips
- •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.
CurrentHigh-compute variant with maxed-out reasoning; API-only and no tool calls.
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. Free to start — no credit card.
