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Llama 3.05 Storybreaker Ministral 70b

Added Jan 1, 2024Open weights

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Use in the app

Much more inclined to output adult content than its predecessor. Great choice for novelty roleplay scenarios.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.49
Output
$0.49

Reference prices as published by the model provider. API example

Specifications

Context window
16.4K
Max output
8.2K
Average output (7 days)
63 tokens
Longer than 6% of 465 models

Side by side

Llama 3.05 Storybreaker Ministral 70b compared

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

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Llama 3.05 Storybreaker Ministral 70bMetaThis modelMuse WonderMetaRemove
ProviderMetaMeta
Discovery tierOpen weightsFrontier
AddedJan 1, 2024Oct 12, 2026
Limits
Context window16.38K1.05MBest
Max output8.19K—
Average output63 tokens1.2K tokens
Pricing, USD per 1M tokens
Input$0.49Best$4.00
Output$0.49Best$20.00
Cache read—$0.20
Performance
Throughput—113.9 tok/s
Time to first token—1.8 s
Request success (24h)—99.7%
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 3.05 Storybreaker Ministral 70b
  • Muse Wonder

Intelligence Index

  • Llama 3.05 Storybreaker Ministral 70b—
  • Muse Wonder52.4
3.8Catalog models by score55.1

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama 3.05 Storybreaker Ministral 70b: —
    • Muse Wonder: 68.1%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama 3.05 Storybreaker Ministral 70b: —
    • Muse Wonder: 39.1%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Llama 3.05 Storybreaker Ministral 70b: —
    • Muse Wonder: 10.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Llama 3.05 Storybreaker Ministral 70b: —
    • Muse Wonder: 1,659 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Llama 3.05 Storybreaker Ministral 70b: —
    • Muse Wonder: 1,770 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama 3.05 Storybreaker Ministral 70b: —
    • Muse Wonder: 30.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 3.05 Storybreaker Ministral 70b: —
    • Muse Wonder: 80.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • Llama 3.05 Storybreaker Ministral 70b: —
    • Muse Wonder: 22.2%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 3.05 Storybreaker Ministral 70b: —
    • Muse Wonder: 50.1%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama 3.05 Storybreaker Ministral 70b: —
    • Muse Wonder: 52.0%

  • SciCode

    Scientific computing in Python

    • Llama 3.05 Storybreaker Ministral 70b: —
    • Muse Wonder: 60.3%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 3.05 Storybreaker Ministral 70b: —
    • Muse Wonder: 80.7%

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

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": "Envoid/Llama-3.05-NT-Storybreaker-Ministral-70B",
    "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="Envoid/Llama-3.05-NT-Storybreaker-Ministral-70B",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

Other Meta models, starting with the same family.