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meganova · Efficient

Manta Flash 1.0

Added Sep 20, 2025

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Use in the app

The flagship model for balanced reasoning and context-rich dialogue. Perfect for AI roleplay, storytelling, and assistant tasks with a 16K window.

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
16.4K
Max output
—
Average output (7 days)
191 tokens
Longer than 20% of 468 models

Side by side

Manta Flash 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 Flash 1.0meganovaThis modelSolar Pro 4upstageRemove
Providermeganovaupstage
Discovery tierEfficientEfficient
AddedSep 20, 2025Aug 11, 2026
Limits
Context window16.38K524.29KBest
Max output—131.07K
Average output191 tokens546 tokens
Pricing, USD per 1M tokens
Input$0.02Best$0.03
Output$0.16$0.12Best
Cache read—$0.006
Performance
Throughput73.1 tok/s123.2 tok/sBest
Time to first token4.1 sBest4.2 s
Request success (24h)100.0%Best99.9%
Benchmark indexes
Intelligence Index—26.6
Capabilities
ToolsNoYes
Structured outputNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Manta Flash 1.0
  • Solar Pro 4

Intelligence Index

  • Manta Flash 1.0—
  • Solar Pro 426.6
3.8Catalog models by score55.1

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Manta Flash 1.0: —
    • Solar Pro 4: 13.6%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Manta Flash 1.0: —
    • Solar Pro 4: 2.4%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Manta Flash 1.0: —
    • Solar Pro 4: 1,093 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Manta Flash 1.0: —
    • Solar Pro 4: 1,106 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Manta Flash 1.0: —
    • Solar Pro 4: 7.0%

  • AA-LCRv1.1

    Reasoning across long documents

    • Manta Flash 1.0: —
    • Solar Pro 4: 70.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Manta Flash 1.0: —
    • Solar Pro 4: 29.8%

  • GPQA Diamond

    Graduate-level science questions

    • Manta Flash 1.0: —
    • Solar Pro 4: 89.1%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Manta Flash 1.0: —
    • Solar Pro 4: 0.0%

  • SciCode

    Scientific computing in Python

    • Manta Flash 1.0: —
    • Solar Pro 4: 48.0%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Manta Flash 1.0: —
    • Solar Pro 4: 70.7%

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.

Throughput
73.1 tok/s
Faster than 48% of 263 models
Time to first token
4.1 s
Quicker than 42% of 263 models
Request success (24h)
100.0%
Reliable
Hourly request success · last 72 hoursLowest hour 92.3%
  • 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-flash-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-flash-1.0",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

meganova

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