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Meta · Open weights

Llama 3 70B abliterated

Added Jul 26, 2025Open weights

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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 Glimmer 30BMetaRemove
ProviderMetaMeta
Discovery tierOpen weightsOpen weights
AddedJul 26, 2025Aug 11, 2026
Limits
Context window8.19K131.07KBest
Average output168 tokens682 tokens
Pricing, USD per 1M tokens
Input$0.70$0.35Best
Output$0.70Best$1.50
Cache read—$0.04
Performance
Throughput—70.8 tok/s
Time to first token—1.1 s
Request success (24h)17.1%100.0%Best
Benchmark indexes
Intelligence Index—17.5
Coding Index—49.0
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Structured outputNoYes
Reasoning effort—minimal, low, medium, high, xhigh

Public evaluations

Benchmarks

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

  • Llama 3 70B abliterated
  • Muse Glimmer 30B

Intelligence Index

  • Llama 3 70B abliterated—
  • Muse Glimmer 30B17.5
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Llama 3 70B abliterated: —
    • Muse Glimmer 30B: 49.0

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama 3 70B abliterated: —
    • Muse Glimmer 30B: 6.8%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama 3 70B abliterated: —
    • Muse Glimmer 30B: 0.8%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Llama 3 70B abliterated: —
    • Muse Glimmer 30B: 477 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Llama 3 70B abliterated: —
    • Muse Glimmer 30B: 783 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama 3 70B abliterated: —
    • Muse Glimmer 30B: 10.0%

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 3 70B abliterated: —
    • Muse Glimmer 30B: 83.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 3 70B abliterated: —
    • Muse Glimmer 30B: 22.0%

  • GPQA Diamond

    Graduate-level science questions

    • Llama 3 70B abliterated: —
    • Muse Glimmer 30B: 83.5%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama 3 70B abliterated: —
    • Muse Glimmer 30B: 0.5%

  • SciCode

    Scientific computing in Python

    • Llama 3 70B abliterated: —
    • Muse Glimmer 30B: 44.9%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 3 70B abliterated: —
    • Muse Glimmer 30B: 83.3%

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.

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.