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meganova · Efficient

Manta Mini 1.0

Added Sep 20, 2025

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Use in the app

Lightweight tier optimized for speed and cost.

Input
Text
Output
Text
Discovery tier
Efficient

Pricing

USD per 1M tokens
Input
$0.02
Output
$0.16

Reference prices as published by the model provider. API example

Specifications

Context window
8.2K
Max output
—
Average output (7 days)
35 tokens
Longer than 2% of 468 models

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Manta Mini 1.0 compared

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

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Manta Mini 1.0meganovaThis modelManta Flash 1.0meganovaRemove
Providermeganovameganova
Discovery tierEfficientEfficient
AddedSep 20, 2025Sep 20, 2025
Limits
Context window8.19K16.38KBest
Average output35 tokens191 tokens
Pricing, USD per 1M tokens
Input$0.02$0.02
Output$0.16$0.16
Performance
Throughput—73.1 tok/s
Time to first token—4.1 s
Request success (24h)95.8%100.0%Best
Capabilities
Reasoning effort——

Public evaluations

Benchmarks

There are no public benchmark results for these models yet.

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)
95.8%
Reliable
Hourly request success · last 72 hoursLowest hour 80.0%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

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": "meganova-ai/manta-mini-1.0",
    "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="meganova-ai/manta-mini-1.0",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

meganova

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