NOUMENOpen app
NOUMEN

Meta · Open weights

Llama 3.3 70B Instruct abliterated

Added Aug 8, 2025Open weights

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Use in the app

An abliterated (removed restrictions and censorship) version of Llama 3.3 70b.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.70
Output
$0.70

Reference prices as published by the model provider. API example

Specifications

Context window
32.8K
Max output
—
Average output (7 days)
129 tokens
Longer than 13% of 465 models

Side by side

Llama 3.3 70B Instruct abliterated compared

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

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Llama 3.3 70B Instruct abliteratedMetaThis modelDeepSeek V4 ProDeepSeekRemove
ProviderMetaDeepSeek
Discovery tierOpen weightsOpen weights
AddedAug 8, 2025Apr 24, 2026
Limits
Context window32.77K1.05MBest
Max output—384K
Average output129 tokens416 tokens
Pricing, USD per 1M tokens
Input$0.70$0.70
Output$0.70Best$1.40
Cache read—$0.07
Performance
Throughput29.4 tok/s53.7 tok/sBest
Time to first token2.0 sBest2.9 s
Request success (24h)20.6%99.7%Best
Benchmark indexes
Intelligence Index—36.0
Coding Index—68.8
Capabilities
ReasoningNoYes
ToolsNoYes
Structured outputNoYes
Reasoning effort—none, high, max

Public evaluations

Benchmarks

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

  • Llama 3.3 70B Instruct abliterated
  • DeepSeek V4 Pro

Intelligence Index

  • Llama 3.3 70B Instruct abliterated—
  • DeepSeek V4 Pro36.0
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Llama 3.3 70B Instruct abliterated: —
    • DeepSeek V4 Pro: 68.8

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama 3.3 70B Instruct abliterated: —
    • DeepSeek V4 Pro: 56.7%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama 3.3 70B Instruct abliterated: —
    • DeepSeek V4 Pro: 25.9%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Llama 3.3 70B Instruct abliterated: —
    • DeepSeek V4 Pro: 1,256 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Llama 3.3 70B Instruct abliterated: —
    • DeepSeek V4 Pro: 1,449 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama 3.3 70B Instruct abliterated: —
    • DeepSeek V4 Pro: 11.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 3.3 70B Instruct abliterated: —
    • DeepSeek V4 Pro: 80.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 3.3 70B Instruct abliterated: —
    • DeepSeek V4 Pro: 41.0%

  • GPQA Diamond

    Graduate-level science questions

    • Llama 3.3 70B Instruct abliterated: —
    • DeepSeek V4 Pro: 92.8%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama 3.3 70B Instruct abliterated: —
    • DeepSeek V4 Pro: 14.1%

  • SciCode

    Scientific computing in Python

    • Llama 3.3 70B Instruct abliterated: —
    • DeepSeek V4 Pro: 51.0%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 3.3 70B Instruct abliterated: —
    • DeepSeek V4 Pro: 80.3%

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.

Throughput
29.4 tok/s
Faster than 19% of 247 models
Time to first token
2.0 s
Quicker than 76% of 247 models
Prompt cache hits
0.0%
Request success (24h)
20.6%

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/Llama-3.3-70B-Instruct-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/Llama-3.3-70B-Instruct-abliterated",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

Other Meta models, starting with the same family.