DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 is a powerful, open-weight, code-optimized LLM with 1M context and strong agentic reasoning — now production-ready.
Overview
What is DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 is a high-performance, open-weight language model from DeepSeek-AI, released in August 2026. It supports a 1 million token context, advanced reasoning, tool use, and web search, making it ideal for complex coding and agent workflows without storing user data on Venice.
Running it privately on Venice
On Venice, DeepSeek V4 Pro 0813 runs with zero retention — your prompts are never stored or profiled. This model's open weights and MIT license let you use it freely in production, while Venice ensures your inputs remain private and uncensored. You get full sovereignty over sensitive workflows.
Assessment
Strengths and limitations
- Open weights under MIT license: fully auditable, modifiable, and suitable for commercial use.
- Exceptional reasoning and code-generation capabilities, benchmarking competitively with top proprietary models.
- Supports flexible reasoning effort: low for simple tasks, high for daily agent workflows, max for complex problems.
- Tool use, web search, and structured JSON output enable robust agentic behavior.
- Runs privately on Venice with zero data retention — ideal for sensitive or regulated workflows.
- Not uncensored: may apply content moderation filters in certain contexts.
- Higher cost per output token compared to smaller open models like DeepSeek V4 Flash.
- Requires significant compute (e.g., 8× H200/B200) for optimal performance, limiting edge deployment.
Capabilities
What it supports
- Tool use / function calling
- Reasoning with adjustable effort
- Web search integration
- Code-optimized generation
- Structured output (JSON schema)
- Input caching for cost savings
- Open weights for self-hosting
- Uncensored output
Specifications
Datasheet
- Maker
- DeepSeek-AI
- Open weights
- No
- License
- MIT
- Modes
- Non-thinking, thinking (default), with low/high/max reasoning effort
- Context window
- 1,000K tokens
- Prompt length
- 1,000K tokens
- Released
- August 13, 2026
- Architecture
- Sparse Mixture of Experts (MoE) with hybrid Compressed Sparse Attention and Heavily Compressed Attention
- Parameters
- 1.65T total, 49B active
- Max output
- 32.768K tokens
- Capabilities
- Function calling, Reasoning, Web search, Code-optimized
- Privacy on Venice
- Private — zero retention
- Available on Venice since
- Aug 2026
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-v4-pro-0813",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $1.65 per 1M input tokens and $4.95 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 | Best for | Context | Open weights | Price (Venice) |
|---|---|---|---|---|
| DeepSeek V4 Pro 0813 | Complex agent workflows, code | 1M tokens | No | $1.65 in · $4.95 out / 1M |
| Claude Fable 5 | Enterprise safety & compliance | 1M tokens | No | $12 in · $60 out / 1M |
| DeepSeek V4 Flash 0731 | High-speed inference | 1M tokens | Yes | $0.17 in · $0.35 out / 1M |
| GLM 5.1 | End-to-end encrypted workflows | 200K tokens | Yes | $1.10 in · $4.15 out / 1M |
Choose DeepSeek V4 Pro 0813 when you need open, auditable weights with top-tier reasoning and tool use — ideal for production agent systems where privacy, cost, and capability must align.
Use cases
What it is good for
- 01Enterprise coding agents that require auditability and IP control.
- 02Long-context analysis such as legal document review or codebase summarization.
- 03Security research workflows involving tool chaining and web-augmented reasoning.
- 04Private AI assistants handling sensitive data with end-to-end confidentiality.
- 05Benchmarking and research leveraging open, reproducible model weights.
Prompting
Getting better results
Use 'reasoning_effort: max' for complex problem-solving tasks requiring deep analysis.
Enable web search when responses need real-time or external data validation.
Structure JSON output with schema definitions for reliable downstream parsing.
Leverage input caching to reduce costs on repeated or templated prompts.
Use low reasoning effort for high-throughput, simple classification tasks.
Phrase tool-use requests explicitly (e.g., 'call function X with Y parameters').
Version history
Preview version with fewer optimizations.
Official release with DSpark speculative decoding and enhanced reasoning.
FAQ
Frequently asked questions
DeepSeek V4 Pro 0813 is an open-weight, production-ready language model from DeepSeek-AI, released in August 2026. It supports 1M token context, advanced reasoning, tool use, and web search, optimized for complex coding and agent workflows.
On Venice, it costs $1.65 per 1M input tokens and $4.95 per 1M output tokens. Cached input is $0.17 per 1M tokens, reducing costs for repeated prompts.
Yes, it is open source under the permissive MIT license. You can use, modify, and redistribute it freely, including in commercial applications.
Yes, it natively supports tool use and function calling, enabling it to act as an autonomous agent by invoking external tools and APIs as needed.
It supports up to 1,000,000 tokens of context, allowing it to process extremely long documents, codebases, or multi-turn conversations in a single pass.
Yes, on Venice, DeepSeek V4 Pro 0813 runs with zero retention — your prompts are never stored, logged, or used for training, ensuring full privacy and compliance.
DeepSeek V4 Pro 0813 is better for complex reasoning and agent tasks, while V4 Flash 0731 is faster and cheaper for high-throughput, simple inference. Choose Pro for depth, Flash for speed.
Run DeepSeek V4 Pro 0813 privately
No prompt logging. No data used for training.