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inclusionai · Efficient

Ling 3.0 Flash

Added Jul 23, 2026

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Use in the app

Ling-3.0-flash is a 124B-parameter Mixture-of-Experts model with approximately 5.1B parameters active per token. It prioritizes token efficiency and production-scale agentic inference, helping coding and tool-using agents complete more work within constrained latency and serving budgets.

  • Tools
Input
Text
Output
Text
Discovery tier
Efficient

Pricing

USD per 1M tokens
Input
$0.075
Output
$0.22
Cache read
$0.015

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
32.8K
Average output (7 days)
1.1K tokens
Longer than 75% of 465 models

Side by side

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TryQwen3.8 27B TEEOpenAI o3MiniMax M2.1All 3

Public evaluations

Benchmarks

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

Intelligence Index

20.1

Better than 49% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    50.6

    Composite of the coding evaluations

    Better than 48% of 168 scored models

Agentic work

  • AutomationBench-AA

    3.2%

    Workflow automation, penalized for guardrail violations

    Better than 20% of 173 scored models

  • AutomationBench-AA tasks completed

    0.6%

    Workflows finished end to end without a guardrail violation

    Better than 29% of 173 scored models

  • AA-Briefcasev1.1

    798 Elo

    Agentic knowledge work, rated head to head

    Better than 28% of 166 scored models

  • GDPval-AAv2.1

    948 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 31% of 187 scored models

Document reasoning

  • GDP.pdf

    5.4%

    Professional reasoning over PDF documents, all-pass rate

    Better than 23% of 171 scored models

  • AA-LCRv1.1

    73.0%

    Reasoning across long documents

    Better than 54% of 309 scored models

  • MLCR-AA

    5.0%

    Long-context reasoning over medical records

    Better than 13% of 121 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    23.7%

    Expert-level questions across academic fields

    Better than 54% of 323 scored models

  • GPQA Diamond

    85.5%

    Graduate-level science questions

    Better than 61% of 291 scored models

Coding

  • Terminal-Benchv4.0

    0.0%

    Practical software engineering in a terminal

    Better than 0% of 171 scored models

  • SciCode

    42.0%

    Scientific computing in Python

    Better than 25% of 174 scored models

Legacy evaluations

  • AA-LCR (unversioned)

    73.0%

    Long-context reasoning, earlier grading

    Better than 54% of 309 scored models

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
299.6 tok/s
Faster than 93% of 250 models
Time to first token
1.9 s
Quicker than 80% of 250 models
Prompt cache hits
77.9%
About $0.0283 per 1M input tokens with caching
Request success (24h)
99.8%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 33.3%
  • 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": "inclusionai/ling-3.0-flash",
    "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="inclusionai/ling-3.0-flash",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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