DeepSeek's October 17 open-weight MoE workhorse with refusal behaviour surgically removed at the weight level — 1M-token context, strong coding and agentic benchmarks, MIT licensed.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "e2ee-deepseek-v4-1-flash-1017-abliterated",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Overview
DeepSeek V4.1 Flash 1017 Abliterated is DeepSeek's October 17, 2026 open-weight mixture-of-experts model with refusal behaviour removed at the weight level by Dealign AI. It keeps the base model's 1M-token context, reasoning, tool use and coding strength, and on Venice it runs privately with zero prompt retention.
On Venice, this abliterated DeepSeek runs inside an attested trusted execution environment with end-to-end encryption and zero retention — your prompts, logs and code are never stored, profiled, or used for training. That matters double here: security research, red-teaming and sensitive code review are exactly the workloads you don't want sitting in a Big-Tech account history. You also pay per token — $0.38 in / $1.50 out per 1M — instead of renting a surveillance-priced subscription.
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": "e2ee-deepseek-v4-1-flash-1017-abliterated",
"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": "e2ee-deepseek-v4-1-flash-1017-abliterated",
"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="e2ee-deepseek-v4-1-flash-1017-abliterated",
messages=[{"role": "user", "content": "Build without permission."}],
)
print(resp.choices[0].message.content)Specifications
Assessment
Use cases
Prompting
Paste the entire file, log or trace — the 1M-token window exists to be used, and cached input drops re-reads to $0.01 per 1M tokens.
Turn reasoning on for hard debugging and architecture decisions, off for mechanical edits — reasoning tokens are billed as output at $1.50 per 1M.
Define tools with strict JSON schemas; the model is code-optimized and follows structured function-calling formats reliably.
Ask for complete, runnable projects rather than fragments — the 1017 checkpoint was RL-trained on end-to-end project building.
Keep long system prompts and multi-turn histories in the same session to hit the cached-input rate instead of paying full input price.
Don't assume every refusal is gone — abliteration reduces refusals but doesn't guarantee zero, so state your legitimate context clearly in the prompt.
Alternatives
| Model | Best for | Context | Open weights | Price (Venice) |
|---|---|---|---|---|
| DeepSeek V4.1 Flash 1017 Abliterated | Refusal-reduced coding & long-context analysis | 1M tokens | Yes | $0.38 in · $1.50 out / 1M |
| Gemini 3.8 Flash | Fast general-purpose assistant | 1M tokens | No | $0.94 in · $4.69 out / 1M |
| Qwen 3.7 Plus | Balanced long-context work | 1M tokens | No | $0.50 in · $2 out / 1M |
| Venice Uncensored 1.2 | Fully uncensored chat | 128K tokens | Yes | $0.20 in · $0.90 out / 1M |
Pick DeepSeek V4.1 Flash 1017 Abliterated when you need frontier-class coding and reasoning over very long codebases or evidence files, without refusal friction and without Big-Tech pricing. For short-form uncensored chat on a budget, Venice Uncensored 1.2 is the leaner pick.
Pricing
Billed per token on Venice: $0.38 per 1M input tokens and $1.50 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 e2ee-deepseek-v4-1-flash-1017-abliterated as the model and send the request.
FAQ
It is DeepSeek's V4.1 Flash 1017 checkpoint (released October 17, 2026) with its refusal behaviour removed at the weight level by Dealign AI. The underlying model is a 552B-parameter open-weight mixture-of-experts with a 1M-token context window, and the abliterated build keeps its reasoning, tool use and coding ability intact.
Venice bills it per token: $0.38 per 1M input tokens, $1.50 per 1M output tokens, and just $0.01 per 1M for cached input. There is no subscription requirement — you pay for what you use.
The weights are open under the MIT licence, published by DeepSeek on Hugging Face with the abliterated FP8 build available from Dealign AI. Open weights mean you can self-host or fine-tune it; using it through Venice is a paid per-token service, though new accounts include free credits to try it.
Abliteration removes the refusal direction from the model's weights without retraining. In this build the edit is surgical and weight-level — a drop-in checkpoint with no runtime hooks — but it is a modification method, not a guarantee: capability drops slightly (about four MMLU points per the publisher) and some refusals can survive.
Yes. On Venice it supports function calling, a reasoning mode and web search, and it is code-optimized. It is text-only on Venice, though — the base model's vision tower is not exposed through this endpoint.
Choose the abliterated DeepSeek when you need frontier-class coding, reasoning and a 1M-token context for long documents or codebases. Venice Uncensored 1.2 is cheaper per token and built specifically for uncensored conversation, but it caps out at 128K tokens and is not a coding specialist.
Yes. It runs in Venice's private tier with zero retention: prompts are processed inside an attested trusted execution environment with end-to-end encryption and are never stored, profiled, or used for training.
Not on Venice. The underlying V4.1 Flash architecture includes a vision tower, but the Venice endpoint for this model accepts text input only, so image understanding is not available here.
One key, free to start, no credit card.