abliteration · Balanced
Abliterated Model
Abliteration.ai's unrestricted multimodal reasoning model is weight-modified to reduce refusals and answer prompts the original model might reject. It supports text and image input, structured output, automatic prompt caching, and a 262K-token context window.
- Reasoning
- Vision
- Structured output
- Input
- Text, Images
- Output
- Text
- Reasoning effort
- none · minimal · low · medium · high · xhigh · max
- Discovery tier
- Balanced
Pricing
USD per 1M tokens- Input
- $1.00
- Output
- $3.00
- Cache read
- $0.10
Reference prices as published by the model provider. API example
Specifications
- Context window
- 262.1K
- Max output
- 262.1K
- Average output (7 days)
- 1.4K tokens
- Longer than 83% of 473 models
Side by side
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Public evaluations
Benchmarks
There are no public benchmark results for Abliterated Model 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
- 170.0 tok/s
- Faster than 81% of 260 models
- Time to first token
- 1.8 s
- Quicker than 81% of 260 models
- Prompt cache hits
- 44.1%
- About $0.60 per 1M input tokens with caching
- Request success (24h)
- 87.5%
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.
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
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",
"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",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)abliteration
Related models
Other abliteration models, starting with the same family.