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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 modelOpenAI o3OpenAIRemove
ProviderinclusionaiOpenAI
Discovery tierEfficientBalanced
AddedJul 23, 2026Apr 16, 2025
Limits
Context window262.14KBest200K
Max output32.77K100KBest
Average output1.1K tokens205 tokens
Pricing, USD per 1M tokens
Input$0.075Best$2.00
Output$0.22Best$8.00
Cache read$0.015Best$1.00
Performance
Throughput279.2 tok/s—
Time to first token1.7 s—
Request success (24h)99.8%—
Benchmark indexes
Intelligence Index20.120.2Best
Coding Index50.6—
Math Index—88.3
Capabilities
ReasoningNoYes
ToolsYesNo
Reasoning effort—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
  • OpenAI o3

Intelligence Index

  • Ling 3.0 Flash20.1
  • OpenAI o320.2

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
    • OpenAI o3: —

  • Math Index

    Composite of the math evaluations

    • Ling 3.0 Flash: —
    • OpenAI o3: 88.3

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Ling 3.0 Flash: 3.2%
    • OpenAI o3: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Ling 3.0 Flash: 0.6%
    • OpenAI o3: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Ling 3.0 Flash: 798 Elo
    • OpenAI o3: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Ling 3.0 Flash: 948 Elo
    • OpenAI o3: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Ling 3.0 Flash: 5.4%
    • OpenAI o3: —

  • AA-LCRv1.1

    Reasoning across long documents

    • Ling 3.0 Flash: 73.0%
    • OpenAI o3: 74.7%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Ling 3.0 Flash: 5.0%
    • OpenAI o3: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Ling 3.0 Flash: 23.7%Top
    • OpenAI o3: 20.1%

  • GPQA Diamond

    Graduate-level science questions

    • Ling 3.0 Flash: 85.5%Top
    • OpenAI o3: 82.7%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Ling 3.0 Flash: —
    • OpenAI o3: 85.3%

  • IFBench

    Following precise output instructions

    • Ling 3.0 Flash: —
    • OpenAI o3: 71.4%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Ling 3.0 Flash: 0.0%
    • OpenAI o3: —

  • SciCode

    Scientific computing in Python

    • Ling 3.0 Flash: 42.0%
    • OpenAI o3: —

  • LiveCodeBench

    Recent competitive programming problems

    • Ling 3.0 Flash: —
    • OpenAI o3: 80.8%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Ling 3.0 Flash: —
    • OpenAI o3: 88.3%

  • AIME

    Competition mathematics

    • Ling 3.0 Flash: —
    • OpenAI o3: 90.3%

  • MATH-500

    Competition math problems

    • Ling 3.0 Flash: —
    • OpenAI o3: 99.2%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Ling 3.0 Flash: —
    • OpenAI o3: 37.1%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Ling 3.0 Flash: —
    • OpenAI o3: 80.7%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Ling 3.0 Flash: 73.0%
    • OpenAI o3: 74.7%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
77.6%
About $0.0284 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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