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inclusionai · Open weights

Ling 3.0 Flash VL

Added Sep 9, 2026Open weights

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Use in the app

Ling 3.0 Flash VL is inclusionAI's native multimodal Mixture-of-Experts model with 124B total parameters and 5.5B active parameters per token. It combines image and video understanding with reasoning and tool use for document analysis, charts, visual verification, and interface-based agent tasks. Thinking is enabled by default and can be turned off in settings.

  • Reasoning
  • Tools
  • Vision
  • Video input
Input
Text, Images, Video
Output
Text
Reasoning effort
none · low · medium · high
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.06
Output
$0.18
Cache read
$0.012

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
32.8K
Average output (7 days)
843 tokens
Longer than 66% of 465 models

Side by side

Ling 3.0 Flash VL 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 Flash VLinclusionaiThis modelClaude Sonnet 4.6AnthropicRemove
ProviderinclusionaiAnthropic
Discovery tierOpen weightsFrontier
AddedSep 9, 2026Feb 17, 2026
Limits
Context window262.14K1MBest
Max output32.77K128KBest
Average output843 tokens401 tokens
Pricing, USD per 1M tokens
Input$0.06Best$3.00
Output$0.18Best$15.00
Cache read$0.012Best$0.30
Performance
Throughput—79.9 tok/s
Time to first token—1.6 s
Request success (24h)99.5%99.6%Best
Benchmark indexes
Intelligence Index24.624.7Best
Coding Index57.0—
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputNoYes
PDF inputNoYes
Video inputYesNo
Reasoning effortnone, low, medium, highlow, medium, high, xhigh, max

Public evaluations

Benchmarks

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

  • Ling 3.0 Flash VL
  • Claude Sonnet 4.6

Intelligence Index

  • Ling 3.0 Flash VL24.6
  • Claude Sonnet 4.624.7

Ling 3.0 Flash VL: Better than 59% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Ling 3.0 Flash VL: 57.0
    • Claude Sonnet 4.6: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Ling 3.0 Flash VL: 15.7%
    • Claude Sonnet 4.6: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Ling 3.0 Flash VL: 1.1%
    • Claude Sonnet 4.6: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Ling 3.0 Flash VL: 984 Elo
    • Claude Sonnet 4.6: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Ling 3.0 Flash VL: 1,161 Elo
    • Claude Sonnet 4.6: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Ling 3.0 Flash VL: 8.4%
    • Claude Sonnet 4.6: —

  • AA-LCRv1.1

    Reasoning across long documents

    • Ling 3.0 Flash VL: 78.3%Top
    • Claude Sonnet 4.6: 68.3%

  • MLCR-AA

    Long-context reasoning over medical records

    • Ling 3.0 Flash VL: 5.0%
    • Claude Sonnet 4.6: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Ling 3.0 Flash VL: 22.0%Top
    • Claude Sonnet 4.6: 13.3%

  • GPQA Diamond

    Graduate-level science questions

    • Ling 3.0 Flash VL: 86.2%Top
    • Claude Sonnet 4.6: 79.9%

  • IFBench

    Following precise output instructions

    • Ling 3.0 Flash VL: —
    • Claude Sonnet 4.6: 41.2%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Ling 3.0 Flash VL: 0.0%
    • Claude Sonnet 4.6: —

  • SciCode

    Scientific computing in Python

    • Ling 3.0 Flash VL: 44.2%
    • Claude Sonnet 4.6: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Ling 3.0 Flash VL: —
    • Claude Sonnet 4.6: 46.2%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Ling 3.0 Flash VL: —
    • Claude Sonnet 4.6: 79.5%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Ling 3.0 Flash VL: 78.3%Top
    • Claude Sonnet 4.6: 68.3%

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.

Prompt cache hits
0.0%
About $0.06 per 1M input tokens with caching
Request success (24h)
99.5%
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 87.5%
  • 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-vl",
    "reasoning_effort": "medium",
    "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-vl",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

inclusionai

Other inclusionai models, starting with the same family.