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inclusionai · Efficient

Ling 3.0 Flash

Added Jul 23, 2026

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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 modelMiniMax M2.1MiniMaxRemove
ProviderinclusionaiMiniMax
Discovery tierEfficientOpen weights
AddedJul 23, 2026Jan 20, 2025
Limits
Context window262.14KBest200K
Max output32.77K131.07KBest
Average output1.1K tokens359 tokens
Pricing, USD per 1M tokens
Input$0.075Best$0.30
Output$0.22Best$1.20
Cache read$0.015Best$0.03
Performance
Throughput279.2 tok/s—
Time to first token1.7 s—
Request success (24h)99.8%—
Benchmark indexes
Intelligence Index20.120.9Best
Coding Index50.6—
Math Index—82.7
Capabilities
ReasoningNoYes
ToolsYesYes
Structured outputNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Ling 3.0 Flash
  • MiniMax M2.1

Intelligence Index

  • Ling 3.0 Flash20.1
  • MiniMax M2.120.9

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
    • MiniMax M2.1: —

  • Math Index

    Composite of the math evaluations

    • Ling 3.0 Flash: —
    • MiniMax M2.1: 82.7

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Ling 3.0 Flash: 3.2%
    • MiniMax M2.1: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Ling 3.0 Flash: 0.6%
    • MiniMax M2.1: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Ling 3.0 Flash: 798 Elo
    • MiniMax M2.1: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Ling 3.0 Flash: 948 Elo
    • MiniMax M2.1: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Ling 3.0 Flash: 5.4%
    • MiniMax M2.1: —

  • AA-LCRv1.1

    Reasoning across long documents

    • Ling 3.0 Flash: 73.0%Top
    • MiniMax M2.1: 67.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • Ling 3.0 Flash: 5.0%
    • MiniMax M2.1: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Ling 3.0 Flash: 23.7%Top
    • MiniMax M2.1: 23.2%

  • GPQA Diamond

    Graduate-level science questions

    • Ling 3.0 Flash: 85.5%Top
    • MiniMax M2.1: 83.0%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Ling 3.0 Flash: —
    • MiniMax M2.1: 87.5%

  • IFBench

    Following precise output instructions

    • Ling 3.0 Flash: —
    • MiniMax M2.1: 69.9%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Ling 3.0 Flash: 0.0%
    • MiniMax M2.1: —

  • SciCode

    Scientific computing in Python

    • Ling 3.0 Flash: 42.0%
    • MiniMax M2.1: —

  • LiveCodeBench

    Recent competitive programming problems

    • Ling 3.0 Flash: —
    • MiniMax M2.1: 81.0%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Ling 3.0 Flash: —
    • MiniMax M2.1: 82.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Ling 3.0 Flash: —
    • MiniMax M2.1: 28.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Ling 3.0 Flash: —
    • MiniMax M2.1: 85.4%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Ling 3.0 Flash: 73.0%Top
    • MiniMax M2.1: 67.7%

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
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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