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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 modelGLM 5.1 TEEZ.aiRemove
ProviderMetaZ.ai
Discovery tierOpen weightsOpen weights
AddedJul 26, 2025Apr 20, 2026
Limits
Context window8.19K202.75KBest
Max output—65.54K
Average output168 tokens675 tokens
Pricing, USD per 1M tokens
Input$0.70$0.70
Output$0.70Best$2.50
Cache read—$0.18
Performance
Request success (24h)17.1%100.0%Best
Benchmark indexes
Intelligence Index—26.1
Coding Index—55.8
Capabilities
ReasoningNoYes
ToolsNoYes
Reasoning effort—none, high

Public evaluations

Benchmarks

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

  • Llama 3 70B abliterated
  • GLM 5.1 TEE

Intelligence Index

  • Llama 3 70B abliterated—
  • GLM 5.1 TEE26.1
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Llama 3 70B abliterated: —
    • GLM 5.1 TEE: 55.8

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama 3 70B abliterated: —
    • GLM 5.1 TEE: 20.3%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama 3 70B abliterated: —
    • GLM 5.1 TEE: 3.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Llama 3 70B abliterated: —
    • GLM 5.1 TEE: 964 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Llama 3 70B abliterated: —
    • GLM 5.1 TEE: 1,117 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama 3 70B abliterated: —
    • GLM 5.1 TEE: 8.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 3 70B abliterated: —
    • GLM 5.1 TEE: 73.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 3 70B abliterated: —
    • GLM 5.1 TEE: 30.1%

  • GPQA Diamond

    Graduate-level science questions

    • Llama 3 70B abliterated: —
    • GLM 5.1 TEE: 86.8%

  • IFBench

    Following precise output instructions

    • Llama 3 70B abliterated: —
    • GLM 5.1 TEE: 76.3%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama 3 70B abliterated: —
    • GLM 5.1 TEE: 2.0%

  • SciCode

    Scientific computing in Python

    • Llama 3 70B abliterated: —
    • GLM 5.1 TEE: 44.8%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Llama 3 70B abliterated: —
    • GLM 5.1 TEE: 43.2%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Llama 3 70B abliterated: —
    • GLM 5.1 TEE: 97.7%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 3 70B abliterated: —
    • GLM 5.1 TEE: 73.7%

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

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