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Meta · Open weights

Llama 3 70B abliterated

Added Jul 26, 2025Open weights

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Use in the app

An abliterated (removed restrictions and censorship) version of Llama 3.1 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
8.2K
Max output
—
Average output (7 days)
168 tokens
Longer than 17% of 465 models

Side by side

Llama 3 70B abliterated compared

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

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Llama 3 70B abliteratedMetaThis modelMuse WonderMetaRemove
ProviderMetaMeta
Discovery tierOpen weightsFrontier
AddedJul 26, 2025Oct 12, 2026
Limits
Context window8.19K1.05MBest
Average output168 tokens1.2K tokens
Pricing, USD per 1M tokens
Input$0.70Best$4.00
Output$0.70Best$20.00
Cache read—$0.20
Performance
Throughput—113.9 tok/s
Time to first token—1.8 s
Request success (24h)17.1%99.5%Best
Benchmark indexes
Intelligence Index—52.4
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Structured outputNoYes
PDF inputNoYes
Audio inputNoYes
Video inputNoYes
Reasoning effort—minimal, low, medium, high, xhigh, max

Public evaluations

Benchmarks

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

  • Llama 3 70B abliterated
  • Muse Wonder

Intelligence Index

  • Llama 3 70B abliterated—
  • Muse Wonder52.4
3.8Catalog models by score55.1

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama 3 70B abliterated: —
    • Muse Wonder: 68.1%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama 3 70B abliterated: —
    • Muse Wonder: 39.1%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Llama 3 70B abliterated: —
    • Muse Wonder: 10.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Llama 3 70B abliterated: —
    • Muse Wonder: 1,659 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Llama 3 70B abliterated: —
    • Muse Wonder: 1,770 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama 3 70B abliterated: —
    • Muse Wonder: 30.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 3 70B abliterated: —
    • Muse Wonder: 80.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • Llama 3 70B abliterated: —
    • Muse Wonder: 22.2%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 3 70B abliterated: —
    • Muse Wonder: 50.1%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama 3 70B abliterated: —
    • Muse Wonder: 52.0%

  • SciCode

    Scientific computing in Python

    • Llama 3 70B abliterated: —
    • Muse Wonder: 60.3%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 3 70B abliterated: —
    • Muse Wonder: 80.7%

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.

Request success (24h)
17.1%

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": "failspy/Meta-Llama-3-70B-Instruct-abliterated-v3.5",
    "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="failspy/Meta-Llama-3-70B-Instruct-abliterated-v3.5",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

Other Meta models, starting with the same family.