A community-abliterated, FP8 build of DeepSeek's 552B multimodal MoE — vision, tools, and controllable reasoning with the refusal circuitry surgically removed.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v4-1-flash-uncensored",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Overview
DeepSeek V4.1 Flash Uncensored is a community-abliterated build of DeepSeek's V4.1-Flash, a 552B-parameter multimodal Mixture-of-Experts model with 8B/16B active parameters, native vision, tool use, and controllable reasoning. The dealignai FP8 checkpoint removes refusal circuitry at the weight level while preserving capability, and it runs on Venice with zero prompt retention.
On Venice, this abliterated DeepSeek build runs under the private tier: your prompts are never stored, profiled, or used for training — zero retention, no personal history tied to your requests. That matters double here, because an uncensored model is exactly the kind of workload Big-Tech APIs log, flag, and de-platform. You also get it at $0.33 per 1M input tokens with cached input at just $0.01, permissionless via the app or the OpenAI-compatible API.
Agent quickstart
The API is OpenAI-compatible: change the base URL and the model id and existing client code works unchanged.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v4-1-flash-uncensored",
"stream": true,
"messages": [{ "role": "user", "content": "Draft the release note." }]
}'curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v4-1-flash-uncensored",
"messages": [{ "role": "user", "content": "Find the rate limits." }],
"tools": [{
"type": "function",
"function": {
"name": "search_docs",
"description": "Search the API documentation.",
"parameters": {
"type": "object",
"properties": { "query": { "type": "string" } },
"required": ["query"]
}
}
}],
"tool_choice": "auto"
}'import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["VENICE_API_KEY"],
base_url="https://api.venice.ai/api/v1",
)
resp = client.chat.completions.create(
model="deepseek-v4-1-flash-uncensored",
messages=[{"role": "user", "content": "Build without permission."}],
)
print(resp.choices[0].message.content)Specifications
Assessment
Use cases
Prompting
Enforce a JSON schema in your request when you need machine-readable output — structured output is supported natively.
Attach multiple images in one turn for comparisons; the vision tower handles several inputs per request.
Dial reasoning effort explicitly: leave it at the abliterated build's reasoning-max default for hard problems, lower it for snappy chat.
Keep your system prompt and reference documents stable across calls so they hit the $0.01/M cached-input rate.
Front-load long context — the 8B-active prefill makes input-heavy prompts disproportionately cheap relative to output.
Use function calling for agent loops rather than parsing free text; the model is code-optimized and tool-trained.
Alternatives
| Model | Best for | Context | Open weights | Price (Venice) |
|---|---|---|---|---|
| DeepSeek V4.1 Flash Uncensored | Cheap private agentic work with vision & tools | 524K tokens | Yes | $0.33 in · $1.32 out / 1M |
| Gemini 3.8 Flash | Fast general multimodal chat | 1M tokens | No | $0.94 in · $4.69 out / 1M |
| Qwen 3.7 Plus | Balanced long-context assistant work | 1M tokens | No | $0.50 in · $2 out / 1M |
| Kimi K3 | Maximum-context research & agents | 1M tokens | Yes | $3.75 in · $18.75 out / 1M |
Pick DeepSeek V4.1 Flash Uncensored when you need an uncensored, open-weights workhorse with vision, tools, and reasoning at the lowest output price in its class — and accept 512K of context instead of 1M. Kimi K3 is the upgrade path when maximum context justifies 14× the cost.
Pricing
Billed per token on Venice: $0.33 per 1M input tokens and $1.32 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Getting a key
Create a key in API settings. Nothing else is required to start.
Export it as VENICE_API_KEY so the snippets above run unedited.
Point an existing OpenAI client at https://api.venice.ai/api/v1. The scheme is part of the value: an OpenAI client given a bare host does not resolve it.
Pass deepseek-v4-1-flash-uncensored as the model and send the request.
FAQ
It is a community-abliterated build of DeepSeek's V4.1-Flash, a 552B-parameter multimodal Mixture-of-Experts model with 8B/16B active parameters. The dealignai FP8 checkpoint removes the model's refusal circuitry at the weight level while preserving vision, reasoning, tool use, and multi-turn coherence. On Venice it runs with zero prompt retention.
Venice bills it per token: $0.33 per 1M input tokens, $1.32 per 1M output tokens, and just $0.01 per 1M cached input tokens. There is no subscription required — you pay for what you use.
Yes. The base model, DeepSeek V4.1-Flash, is released under the MIT license, and the uncensored FP8 checkpoint is published on Hugging Face. You can self-host or fine-tune it, though the deepseek_v41 architecture is not yet in mainline transformers/vLLM and needs DeepSeek's reference runtime tooling.
Yes — the refusal circuitry is removed permanently at the weight level via abliteration, not via a system prompt or runtime filter that can be re-enabled. The trade-off is that it has no guardrails of its own, so outputs on sensitive topics deserve scrutiny, and self-hosters must add their own moderation.
Yes. It natively accepts RGB images (multiple per request, up to 1,024 image tokens per image) and supports function calling, structured JSON output, web search, and continuously controllable reasoning effort. It is also code-optimized for programming workloads.
Venice serves it with a 512K-token context window and up to 128K output tokens. The upstream checkpoint supports up to 1M tokens, so Venice's deployment trades some maximum context for efficient serving at low prices.
Choose the DeepSeek build when you need uncensored outputs, open MIT-licensed weights, and the lowest cost — $0.33/$1.32 per 1M versus Gemini's $0.94/$4.69. Choose Gemini 3.8 Flash when you want a polished, moderated general assistant and a full 1M-token context and don't mind closed weights.
Venice runs it under its private tier with zero retention: prompts and outputs are not stored, profiled, or used for training, and requests are not tied to a personal behavioral history. Note that inference is not end-to-end encrypted in a TEE — the privacy guarantee is Venice's no-storage policy.
DeepSeek published the MIT-licensed base model on September 10, 2026, but released no uncensored version itself. The FP8 checkpoint Venice hosts was produced by the dealignai research team, one of several community abliteration builds that appeared within weeks of the base release.
One key, free to start, no credit card.