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abliteration · Efficient

Abliterated Large Flash

Added Oct 23, 2026

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Use in the app

Abliterated Large Flash is an uncensored reasoning model based on DeepSeek V4.1-Flash-1017. It accepts text and images and returns text, with tool calling, JSON mode, JSON Schema output, and automatic prompt caching. Its shared input/output context is 1,048,576 tokens, with up to 262,144 output tokens when space permits. Reasoning defaults to high and can be turned off or set to low, high, or max. Supports up to four images across the request history.

  • Reasoning
  • Tools
  • Vision
  • Structured output
  • Parallel tools
Input
Text, Images
Output
Text
Reasoning effort
none · minimal · low · medium · high · xhigh · max
Discovery tier
Efficient

Pricing

USD per 1M tokens
Input
$1.00
Output
$2.00
Cache read
$0.10

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
262.1K
Average output (7 days)
1.1K tokens
Longer than 73% of 473 models

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Abliterated Large Flash compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Abliterated Large FlashabliterationThis modelAbliterated ModelabliterationRemove
Providerabliterationabliteration
Discovery tierEfficientBalanced
AddedOct 23, 2026Aug 27, 2026
Limits
Context window1.05MBest262.14K
Max output262.14KBest262.13K
Average output1.1K tokens1.4K tokens
Pricing, USD per 1M tokens
Input$1.00$1.00
Output$2.00Best$3.00
Cache read$0.10$0.10
Performance
Throughput329.9 tok/sBest170.0 tok/s
Time to first token3.0 s1.8 sBest
Request success (24h)98.9%Best87.4%
Capabilities
ReasoningYesYes
ToolsYesNo
VisionYesYes
Structured outputYesYes
Parallel toolsYesNo
Reasoning effortnone, minimal, low, medium, high, xhigh, maxnone, minimal, low, medium, high, xhigh, max

Public evaluations

Benchmarks

There are no public benchmark results for these models yet.

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Throughput
329.9 tok/s
Faster than 93% of 260 models
Time to first token
3.0 s
Quicker than 54% of 260 models
Prompt cache hits
96.2%
About $0.13 per 1M input tokens with caching
Request success (24h)
98.9%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 65.5%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "abliteration-ai/abliterated-model-large-flash",
    "reasoning_effort": "medium",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="abliteration-ai/abliterated-model-large-flash",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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