heabsy · Frontier

CyberHeabsy

Added Sep 27, 2026

A low-refusal text model for authorized security research, roleplay, and creative writing. It preserves tool calling after refusal removal, supports optional reasoning, and has a 1M-token context window.

Reasoning
Tools

Input: Text
Output: Text
Reasoning effort: none · low · medium · high
Discovery tier: Frontier

Pricing

USD per 1M tokens

Input: $1.50
Output: $4.50
Cache read: $0.28

Reference prices as published by the model provider. API example

Specifications

Context window: 1M
Max output: 65.5K
Average output (7 days): 1.3K tokens; Longer than 81% of 473 models

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Public evaluations

Benchmarks

There are no public benchmark results for CyberHeabsy 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: 63.5 tok/s; Faster than 42% of 259 models
Time to first token: 0.9 s; Quicker than 96% of 259 models
Prompt cache hits: 8.3%; About $1.40 per 1M input tokens with caching
Request success (24h): 99.1%; 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 0.0%

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": "heabsy/cyberheabsy",
    "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="heabsy/cyberheabsy",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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