Meta · Open weights

Llama 3.05 Storybreaker Ministral 70b

Added Jan 1, 2024Open weights

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.

Limits
	Llama 3.05 Storybreaker Ministral 70bMetaThis model	Muse Spark 1.3 ContributorMetaRemove
Provider	Meta	Meta
Discovery tier	Open weights	Efficient
Added	Jan 1, 2024	Sep 2, 2026
Context window	16.38K	1.05MBest
Max output	8.19K	—
Average output	63 tokens	1.2K tokens
Pricing, USD per 1M tokens
Input	$0.49	$0.10Best
Output	$0.49	$0.20Best
Cache read	—	$0.002
Performance
Throughput	—	145.4 tok/s
Time to first token	—	3.9 s
Request success (24h)	—	99.4%
Capabilities
Reasoning	No	Yes
Tools	No	Yes
Vision	No	Yes
Structured output	No	Yes
PDF input	No	Yes
Audio input	No	Yes
Video input	No	Yes
Reasoning effort	—	minimal, low, medium, high, xhigh

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)