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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 VLinclusionaiRemove
Providerinclusionaiinclusionai
Discovery tierEfficientOpen weights
AddedJul 23, 2026Sep 9, 2026
Limits
Context window262.14K262.14K
Max output32.77K32.77K
Average output1.1K tokens843 tokens
Pricing, USD per 1M tokens
Input$0.075$0.06Best
Output$0.22$0.18Best
Cache read$0.015$0.012Best
Performance
Throughput279.2 tok/s—
Time to first token1.7 s—
Request success (24h)99.8%Best99.5%
Benchmark indexes
Intelligence Index20.124.6Best
Coding Index50.657.0Best
Capabilities
ReasoningNoYes
ToolsYesYes
VisionNoYes
Video inputNoYes
Reasoning effort—none, low, medium, high

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 VL

Intelligence Index

  • Ling 3.0 Flash20.1
  • Ling 3.0 Flash VL24.6

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: 50.6
    • Ling 3.0 Flash VL: 57.0Top

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Ling 3.0 Flash: 3.2%
    • Ling 3.0 Flash VL: 15.7%Top

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Ling 3.0 Flash: 0.6%
    • Ling 3.0 Flash VL: 1.1%Top

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Ling 3.0 Flash: 798 Elo
    • Ling 3.0 Flash VL: 984 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Ling 3.0 Flash: 948 Elo
    • Ling 3.0 Flash VL: 1,161 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Ling 3.0 Flash: 5.4%
    • Ling 3.0 Flash VL: 8.4%Top

  • AA-LCRv1.1

    Reasoning across long documents

    • Ling 3.0 Flash: 73.0%
    • Ling 3.0 Flash VL: 78.3%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Ling 3.0 Flash: 5.0%Top
    • Ling 3.0 Flash VL: 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 VL: 22.0%

  • GPQA Diamond

    Graduate-level science questions

    • Ling 3.0 Flash: 85.5%
    • Ling 3.0 Flash VL: 86.2%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

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

  • SciCode

    Scientific computing in Python

    • Ling 3.0 Flash: 42.0%
    • Ling 3.0 Flash VL: 44.2%Top

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Ling 3.0 Flash: 73.0%
    • Ling 3.0 Flash VL: 78.3%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)

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