Qwen · Open weights

Qwen 3.8 27B Antislop

Added Oct 5, 2026Open weights

Qwen 3.8 27B Antislop is a creative finetune served by Aoru in NVFP4 with a 262,144-token context window. It supports image understanding, tool calling, and optional reasoning.

Reasoning
Tools
Vision

Input: Text, Images
Output: Text
Discovery tier: Open weights

Pricing

USD per 1M tokens

Input: $0.10
Output: $0.60
Cache read: $0.05

Reference prices as published by the model provider. API example

Specifications

Context window: 262.1K
Max output: 32.8K
Average output (7 days): 299 tokens; Longer than 31% of 465 models

Side by side

Compare models

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TryGemma 4 31B Scarlet GLM 5.3 Flash LFM2.5 2.6B All 3

Public evaluations

Benchmarks

There are no public benchmark results for Qwen 3.8 27B Antislop 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: 34.4 tok/s; Faster than 25% of 250 models
Time to first token: 1.9 s; Quicker than 78% of 250 models
Prompt cache hits: 60.8%; About $0.0696 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 96.7%

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": "qwen/qwen3.8-27b-antislop",
    "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="qwen/qwen3.8-27b-antislop",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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