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DeepSeek · Open weights

DeepSeek R1 Qwen Abliterated

Added Jan 1, 2024Open weights

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Use in the app

Uncensored version of the Deepseek R1 Qwen 32B model

  • Reasoning
Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$1.40
Output
$1.40

Reference prices as published by the model provider. API example

Specifications

Context window
16.4K
Max output
8.2K
Average output (7 days)
200 tokens
Longer than 21% of 468 models

Side by side

DeepSeek R1 Qwen Abliterated compared

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

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DeepSeek R1 Qwen AbliteratedDeepSeekThis modelKimi K2.6 TEEMoonshotRemove
ProviderDeepSeekMoonshot
Discovery tierOpen weightsOpen weights
AddedJan 1, 2024Apr 21, 2026
Limits
Context window16.38K262.14KBest
Max output8.19K65.54KBest
Average output200 tokens3.1K tokens
Pricing, USD per 1M tokens
Input$1.40Best$1.50
Output$1.40Best$5.25
Cache read—$0.38
Performance
Request success (24h)58.0%100.0%Best
Benchmark indexes
Intelligence Index—27.0
Coding Index—61.8
Capabilities
ReasoningYesNo
ToolsNoYes
VisionNoYes
Reasoning effort——

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • DeepSeek R1 Qwen Abliterated
  • Kimi K2.6 TEE

Intelligence Index

  • DeepSeek R1 Qwen Abliterated—
  • Kimi K2.6 TEE27.0
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 61.8

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 13.0%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 1.8%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 819 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 1,038 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 13.0%

  • AA-LCRv1.1

    Reasoning across long documents

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 81.0%

  • MLCR-AA

    Long-context reasoning over medical records

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 11.1%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 37.5%

  • GPQA Diamond

    Graduate-level science questions

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 91.1%

  • IFBench

    Following precise output instructions

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 76.0%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 0.5%

  • SciCode

    Scientific computing in Python

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 51.5%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 43.9%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 95.9%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • DeepSeek R1 Qwen Abliterated: —
    • Kimi K2.6 TEE: 81.0%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

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.

Prompt cache hits
0.0%
Request success (24h)
58.0%

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.

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": "huihui-ai/DeepSeek-R1-Distill-Qwen-32B-abliterated",
    "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="huihui-ai/DeepSeek-R1-Distill-Qwen-32B-abliterated",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

DeepSeek

Other DeepSeek models, starting with the same family.