DeepSeek's open-weight MoE workhorse for agentic coding and long-horizon tasks — 1M-token context, tool use, reasoning, and web search, served ultrafast with zero retention.
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-1017-ultrafast",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Overview
DeepSeek V4.1 Flash 1017 Ultrafast is DeepSeek's October 2026 point-release of its open-weight V4.1 Flash model — a 552B-parameter Mixture-of-Experts (16B active) tuned for agentic coding, reasoning, and tool use, with a 1M-token context. The Ultrafast variant on Venice serves it text-only at accelerated speed with zero prompt retention.
On Venice, DeepSeek V4.1 Flash 1017 Ultrafast runs under the private tier with zero retention: your prompts are never stored, profiled, or used for training — a meaningful difference when you're pasting proprietary code into an agentic workflow. The weights are open under MIT, so you can audit the model itself while Venice handles the inference. Note that this Venice route is text-only and served in fp8; it is not an uncensored or TEE-isolated deployment.
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-1017-ultrafast",
"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-1017-ultrafast",
"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-1017-ultrafast",
messages=[{"role": "user", "content": "Build without permission."}],
)
print(resp.choices[0].message.content)Specifications
Assessment
Use cases
Prompting
Enable reasoning mode for hard multi-step tasks; disable it on simple lookups to cut latency and output tokens.
Define your tools once and lean on function calling — the model is RL-trained for end-to-end tool-driven task completion.
Pass a JSON schema in the request when you need machine-readable output instead of parsing free text.
Keep your system prompt and reference docs stable across calls so the $0.06/1M cached-input rate kicks in.
Ask for terse answers explicitly — the 1017 revision is tuned toward shorter outputs, and terse prompts compound the speed gain.
For repo-level work, name the files and let it plan before editing; it performs best when given an open-ended goal plus terminal access rather than micromanaged steps.
Alternatives
| Model | Best for | Context | Open weights | Price (Venice) |
|---|---|---|---|---|
| DeepSeek V4.1 Flash 1017 Ultrafast | Agentic coding & long tasks | 1M tokens | Yes | $3 in · $12 out / 1M |
| Grok 4.7 | General reasoning & chat | 500K tokens | No | $2.27 in · $6.80 out / 1M |
| Muse Spark Open | Open-weight generalist | 1M tokens | Yes | $1.56 in · $5.31 out / 1M |
| Gemini 3.8 Flash | Speed on a budget | 1M tokens | No | $0.94 in · $4.69 out / 1M |
Pick DeepSeek V4.1 Flash 1017 Ultrafast when an autonomous agent is doing the driving — multi-file coding, terminal work, long tool chains — and you want open weights with a 1M-token context behind zero-retention inference.
Pricing
Billed per token on Venice: $3 per 1M input tokens and $12 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-1017-ultrafast as the model and send the request.
FAQ
It is the October 17, 2026 point-release of DeepSeek's open-weight V4.1 Flash, a 552B-parameter Mixture-of-Experts model (16B active) with a 1M-token context, reasoning mode, function calling, and web search. The Ultrafast designation refers to the accelerated serving route Venice uses for it.
Venice bills it per token: $3 per 1M input tokens and $12 per 1M output tokens, with cached input at just $0.06 per 1M tokens. There is no subscription required — you pay only for what you use.
The weights are open under the MIT licence, published on Hugging Face and ModelScope, so anyone can self-host or fine-tune them. Using it through Venice is a paid per-token service, though new Venice accounts get free daily prompts and welcome credits to try it.
Yes. Function calling, reasoning mode, web search, and structured JSON-schema output are all supported on Venice, which is why the model is aimed at agentic coding harnesses rather than plain chat.
The base open-weight model accepts image input, but the Ultrafast route Venice serves is text-only — image input is not available on this variant. If you need vision, use a multimodal model on Venice instead.
Choose DeepSeek for agentic coding, open MIT weights, and a 1M-token context at $3/$12 per 1M tokens; choose Grok 4.7 for a closed generalist with a 500K context at slightly lower output pricing. DeepSeek is the stronger fit for autonomous, tool-heavy workflows.
Venice runs it under its private tier with zero retention: prompts and outputs are not stored, profiled, or used for training. This variant is not TEE-isolated or end-to-end encrypted, but nothing you send is kept.
1,000K tokens of context with up to 128K tokens of output per response on Venice — enough to hold large codebases or long document sets in a single session.
Independent reporting flags it as more prone to hallucination than proprietary competitors, particularly in long agentic runs. It trades some reliability for a much lower running cost, so verify critical outputs — especially in multi-step autonomous tasks.
One key, free to start, no credit card.