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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 468 models

Side by side

Ling 3.0 Flash compared

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

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Ling 3.0 FlashinclusionaiThis modelLing 3.0 Flash ThinkinginclusionaiRemove
Providerinclusionaiinclusionai
Discovery tierEfficientEfficient
AddedJul 23, 2026Jul 23, 2026
Limits
Context window262.14K262.14K
Max output32.77K32.77K
Average output1.1K tokens243 tokens
Pricing, USD per 1M tokens
Input$0.075$0.075
Output$0.22$0.22
Cache read$0.015$0.015
Performance
Throughput279.2 tok/s—
Time to first token1.7 s—
Request success (24h)99.8%100.0%Best
Benchmark indexes
Intelligence Index20.120.1
Coding Index50.650.6
Capabilities
ReasoningNoYes
ToolsYesYes
Reasoning effort—none, minimal, low, medium, high, xhigh

Public evaluations

Benchmarks

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

  • Ling 3.0 Flash
  • Ling 3.0 Flash Thinking

Intelligence Index

  • Ling 3.0 Flash20.1
  • Ling 3.0 Flash Thinking20.1

Ling 3.0 Flash: Better than 49% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Ling 3.0 Flash: 50.6Top
    • Ling 3.0 Flash Thinking: 50.6Top

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Ling 3.0 Flash: 3.2%Top
    • Ling 3.0 Flash Thinking: 3.2%Top

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Ling 3.0 Flash: 0.6%Top
    • Ling 3.0 Flash Thinking: 0.6%Top

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Ling 3.0 Flash: 798 EloTop
    • Ling 3.0 Flash Thinking: 798 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Ling 3.0 Flash: 948 EloTop
    • Ling 3.0 Flash Thinking: 948 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Ling 3.0 Flash: 5.4%Top
    • Ling 3.0 Flash Thinking: 5.4%Top

  • AA-LCRv1.1

    Reasoning across long documents

    • Ling 3.0 Flash: 73.0%Top
    • Ling 3.0 Flash Thinking: 73.0%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Ling 3.0 Flash: 5.0%Top
    • Ling 3.0 Flash Thinking: 5.0%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Ling 3.0 Flash: 23.7%Top
    • Ling 3.0 Flash Thinking: 23.7%Top

  • GPQA Diamond

    Graduate-level science questions

    • Ling 3.0 Flash: 85.5%Top
    • Ling 3.0 Flash Thinking: 85.5%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Ling 3.0 Flash: 0.0%Top
    • Ling 3.0 Flash Thinking: 0.0%Top

  • SciCode

    Scientific computing in Python

    • Ling 3.0 Flash: 42.0%Top
    • Ling 3.0 Flash Thinking: 42.0%Top

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Ling 3.0 Flash: 73.0%Top
    • Ling 3.0 Flash Thinking: 73.0%Top

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
279.2 tok/s
Faster than 92% of 254 models
Time to first token
1.7 s
Quicker than 87% of 254 models
Prompt cache hits
77.0%
About $0.0288 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)

inclusionai

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