DeepSeek V4 Flash
DeepSeek's fast 284B-parameter MoE text model with 1M context, 13B active params, and open weights for coding, reasoning, and agentic workflows.
Overview
What is DeepSeek V4 Flash
DeepSeek V4 Flash is a 284B-parameter Mixture-of-Experts text model from DeepSeek-AI, released in April 2026. It activates 13B parameters per token, handles up to one million tokens of context, and is optimized for fast reasoning, coding, and agentic tasks with fully open weights.
Running it privately on Venice
On Venice, DeepSeek V4 Flash runs inside a TEE with end-to-end encryption and zero retention — your prompts are never stored, profiled, or used for training. You get its full tool-use, reasoning, web search, and code-optimized capabilities with the sovereignty of open weights, while paying a fraction of what closed alternatives charge.
Assessment
Strengths and limitations
- 1M-token context window with highly efficient long-context attention, using only ~27% of the inference FLOPs and ~10% of the KV cache of DeepSeek V3.2 at that scale.
- Strong reasoning and coding performance, with multiple thinking modes for complex problem-solving.
- Native support for tool use, web search, and code-optimized generation, making it ideal for agentic workflows.
- Fully open weights under MIT license, enabling self-hosting, fine-tuning, and auditing without vendor lock-in.
- Extremely low per-token cost on Venice for a model with this context length and capability.
- Venice caps max output at 8.192K tokens, well below the model's native 384K limit, so very long generations must be chunked.
- Not fully uncensored: expect content refusals on sensitive topics.
- As a preview release, final benchmark standings and weight stability may evolve.
- MoE efficiency gains are most pronounced at batch scale; small local quantized runs may not match the speed of dense models with similar active parameters.
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
- Open weights
- Yes — MIT License
- License
- MIT
- Context window
- 1,000K tokens
- Released
- April 24, 2026 (preview)
- Architecture
- Mixture-of-Experts (MoE) with Hybrid Attention (CSA + HCA)
- Parameters
- 284B total · 13B active per token
- Max output
- 8K tokens
- Capabilities
- Function calling, Reasoning, Web search, Code-optimized
- Privacy on Venice
- Private — zero retention
- Available on Venice since
- Jul 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": "e2ee-deepseek-v4-flash",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $0.18 per 1M input tokens and $0.37 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 | Venice price / 1M | Open weights | Best for |
|---|---|---|---|---|
| DeepSeek V4 Flash | 1M tokens | $0.18 in · $0.37 out / 1M | Yes | Fast agentic coding & reasoning |
| DeepSeek V3.2 | 160K tokens | $0.33 in · $0.48 out / 1M | Yes | General open-source workloads |
| Claude Sonnet 4.6 | 1M tokens | $3.60 in · $18 out / 1M | No | Enterprise safety & compliance |
| Kimi K2.6 | 256K tokens | $0.75 in · $3.50 out / 1M | Yes | Long-document analysis |
The open-weights efficiency leader: 1M context at the lowest price in its class, with tool use and reasoning built in.
Use cases
What it is good for
- 01Long-document RAG and summarization over books, legal briefs, or codebases using the 1M context.
- 02Agentic coding with tool calling and reasoning to plan, execute, and debug multi-file projects.
- 03Web-grounded research assistants that search, reason, and cite sources.
- 04High-volume, cost-sensitive text processing pipelines.
- 05Self-hosted deployments via vLLM, SGLang, or Ollama for data sovereignty.
Prompting
Getting better results
For 1M-context tasks, place key instructions at both the start and end of the prompt to improve retrieval.
Enable reasoning mode for math, logic, and complex coding problems; use non-reasoning mode for simple Q&A to save tokens.
Use function calling to let the model search the web or execute code during agentic workflows.
Break tasks requiring outputs longer than 8K tokens into multiple chained calls on Venice.
Version history
Predecessor MoE model with 160K context.
Current preview — fast, efficient, 1M context.
Larger 1.6T-parameter sibling with 49B active params.
FAQ
Frequently asked questions
DeepSeek V4 Flash is a 284B-parameter Mixture-of-Experts text model from DeepSeek-AI, released in April 2026. It activates 13B parameters per token, supports a one-million-token context window, and is optimized for fast reasoning, coding, and agentic workflows with open weights.
Venice bills per token at $0.18 per 1M input tokens and $0.37 per 1M output tokens. Cached input is $0.04 per 1M tokens. There is no subscription required.
Yes. DeepSeek V4 Flash is released with open weights under the MIT license, so you can download, self-host, fine-tune, and audit it freely.
Yes. On Venice it supports function calling, reasoning, web search, and code-optimized generation, making it well-suited for agentic workflows.
Choose V4 Flash for 1M-token context, lower per-token pricing, and stronger reasoning. Choose V3.2 only if you specifically need a smaller model footprint for local hardware with limited VRAM.
No. While it is open-weights and private on Venice, it is not fully uncensored and may refuse certain sensitive or restricted topics.
Yes. The open weights are available on Hugging Face and can be run with vLLM, SGLang, or quantized for Ollama and LM Studio. Venice also hosts it privately if you prefer not to manage infrastructure.
Venice limits output to 8.192K tokens per response. If you need longer generations, break the task into multiple chained prompts.
Run DeepSeek V4 Flash privately
No prompt logging. No data used for training.