Abliteration's abliterated reasoning model — 1M-token context, vision, tool calling, and switchable thinking, hosted in FP8.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "abliteration-abliterated-model-large-flash",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Overview
Abliterated Large Flash is Abliteration's abliterated reasoning model, released in October 2026 and based on DeepSeek V4.1-Flash-1017. It accepts text and images, runs a 1M-token context window, and combines tool calling, structured JSON output, web search, and switchable reasoning — tuned to follow through on prompts other models decline.
On Venice, Abliterated Large Flash runs under the anonymized privacy tier: your prompts are processed transiently and never stored, profiled, or used for training. There's no subscription gate — you pay per token and can start using it permissionlessly through the app or the OpenAI-compatible Venice API. Venice serves it quantized to FP8, so you get the full 1M-context reasoning model without building a personal conversation history anywhere.
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": "abliteration-abliterated-model-large-flash",
"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": "abliteration-abliterated-model-large-flash",
"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="abliteration-abliterated-model-large-flash",
messages=[{"role": "user", "content": "Build without permission."}],
)
print(resp.choices[0].message.content)Specifications
Assessment
Use cases
Prompting
Set "thinking": false when you want a fast answer without the reasoning step — it's supported on this model, unlike Abliterated Large v2.
For hard debugging or multi-step analysis, raise reasoning effort to "max" and let the model think longer before answering.
Attach up to four images in the request history — e.g. an error screenshot plus the relevant config file photo — instead of transcribing them.
Pass a JSON Schema in response_format when you need machine-readable output; structured outputs are natively supported.
Keep long system prompts and reference documents at the front of the conversation so prompt caching bills them at the $0.13/1M cached rate.
Use the model id abliterated-model-large-flash in the API's model field — Abliteration's other model ids point to different, text-only models.
Alternatives
| Model | Best for | Context | Open weights | Price (Venice) |
|---|---|---|---|---|
| Abliterated Large Flash | Abliterated reasoning with vision & tools | 1M tokens | No | $1.25 in · $2.50 out / 1M |
| Kimi K3 | Open-weight long-context reasoning | 1M tokens | Yes | $3.75 in · $18.75 out / 1M |
| Qwen 3.7 Plus | Budget 1M-context chat | 1M tokens | No | $0.50 in · $2 out / 1M |
| Gemini 3.8 Flash | Fast multimodal assistant | 1M tokens | No | $0.94 in · $4.69 out / 1M |
Abliterated Large Flash is the right pick for long-context, tool-driven work — security research, red-teaming, code analysis — that mainstream APIs refuse, at a mid-tier price with vision and JSON-schema output included. If open weights matter more than refusal behavior, Kimi K3 is the alternative.
Pricing
Billed per token on Venice: $1.25 per 1M input tokens and $2.50 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 abliteration-abliterated-model-large-flash as the model and send the request.
FAQ
Abliterated Large Flash is Abliteration's abliterated reasoning model, released in October 2026 and listed on Venice the same day. It accepts text and image input, offers a 1M-token context window, and supports tool calling, structured JSON output, web search, and reasoning effort you can tune or switch off.
Venice bills it per token: $1.25 per 1M input tokens and $2.50 per 1M output tokens, with cached input at $0.13 per 1M. There is no subscription required — you pay only for what you use.
It is neither free-software nor open-weight: the hosted model is proprietary to Abliteration and cannot be self-hosted or fine-tuned. On Venice, new accounts include free daily prompts and welcome credits you can use to try it before paying per token.
It is abliterated: its maker removes refusal directions from the weights so the model answers a wider range of prompts — security research, red-teaming, and agent testing — without jailbreak prompts. Abliteration's own docs note some requests may still be refused, so it reduces refusals rather than eliminating them entirely.
Yes. It supports function calling, structured output via JSON schema, streaming, and built-in web search, making it suitable for agentic pipelines. It does not support video input — only text and static images.
It accepts static images alongside text, with up to four images across the request history per request. Video inputs are not supported by any model in the Abliteration family.
Venice runs it under the anonymized privacy tier: prompts are not stored, profiled, or used for training, and nothing ties your usage to a personal history. Note that inference is not TEE-isolated or end-to-end encrypted, so for hardware-level isolation requirements this isn't the strongest tier Venice offers.
Both offer 1M-token contexts and reasoning. Abliterated Large Flash is cheaper ($1.25/$2.50 vs $3.75/$18.75 per 1M tokens), adds abliterated tuning for refused workloads, and is code-optimized; Kimi K3 has open weights you can self-host. Choose Flash for private adversarial or coding work, Kimi for open-weight sovereignty.
October 23, 2026 — the same day Venice listed it. It sits alongside Abliterated Model (262K context) and the text-only Abliterated Large v2 in Abliteration's current lineup, replacing the deprecated Abliterated Large!.
One key, free to start, no credit card.