NOUMENOpen app

Meta · Open weights

Llama 3.05 Storybreaker Ministral 70b

Added Jan 1, 2024Open weights

Compare models

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

Loading models…

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.

Compare models

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

Loading models…

Llama 3.05 Storybreaker Ministral 70bMetaThis modelMS3.2 24B Magnum DiamondMistralRemove
ProviderMetaMistral
Discovery tierOpen weightsOpen weights
AddedJan 1, 2024Nov 24, 2025
Limits
Context window16.38K32.77KBest
Max output8.19K—
Average output63 tokens97 tokens
Pricing, USD per 1M tokens
Input$0.49$0.49
Output$0.49$0.49
Performance
Request success (24h)—64.0%
Capabilities
Reasoning effort——

Public evaluations

Benchmarks

There are no public benchmark results for these models yet.

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.