inclusionai · Efficient
Ling 3.0 Flash Thinking
Ling-3.0-flash Thinking enables visible reasoning on inclusionAI's token-efficient 124B-parameter Mixture-of-Experts model for harder coding, tool use, planning, and production-scale agent workflows.
- Reasoning
- Tools
- Input
- Text
- Output
- Text
- Reasoning effort
- none · minimal · low · medium · high · xhigh
- 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)
- 243 tokens
- Longer than 25% of 468 models
Side by side
Ling 3.0 Flash Thinking compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Ling 3.0 Flash ThinkinginclusionaiThis model | Qwen3.8 27B TEEQwenRemove | |
|---|---|---|
| Provider | inclusionai | Qwen |
| Discovery tier | Efficient | Open weights |
| Added | Jul 23, 2026 | Aug 24, 2026 |
| Limits | ||
| Context window | 262.14K | 262.14K |
| Max output | 32.77K | 262.14KBest |
| Average output | 243 tokens | 301 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.075Best | $0.40 |
| Output | $0.22Best | $3.00 |
| Cache read | $0.015Best | $0.15 |
| Performance | ||
| Request success (24h) | 100.0% | 100.0% |
| Benchmark indexes | ||
| Intelligence Index | 20.1 | 20.2Best |
| Coding Index | 50.6Best | 44.6 |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | Yes | Yes |
| Vision | No | Yes |
| Structured output | No | Yes |
| Video input | No | Yes |
| 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 Thinking
- Qwen3.8 27B TEE
Intelligence Index
- Ling 3.0 Flash Thinking20.1
- Qwen3.8 27B TEE20.2
Ling 3.0 Flash Thinking: Better than 49% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Ling 3.0 Flash Thinking: 50.6Top
- Qwen3.8 27B TEE: 44.6
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Ling 3.0 Flash Thinking: 3.2%
- Qwen3.8 27B TEE: 12.4%Top
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Ling 3.0 Flash Thinking: 0.6%
- Qwen3.8 27B TEE: 1.5%Top
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Ling 3.0 Flash Thinking: 798 Elo
- Qwen3.8 27B TEE: 898 EloTop
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Ling 3.0 Flash Thinking: 948 Elo
- Qwen3.8 27B TEE: 1,074 EloTop
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Ling 3.0 Flash Thinking: 5.4%Top
- Qwen3.8 27B TEE: 5.2%
AA-LCRv1.1
Reasoning across long documents
- Ling 3.0 Flash Thinking: 73.0%Top
- Qwen3.8 27B TEE: 69.3%
MLCR-AA
Long-context reasoning over medical records
- Ling 3.0 Flash Thinking: 5.0%
- Qwen3.8 27B TEE: 10.0%Top
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Ling 3.0 Flash Thinking: 23.7%Top
- Qwen3.8 27B TEE: 12.1%
GPQA Diamond
Graduate-level science questions
- Ling 3.0 Flash Thinking: 85.5%Top
- Qwen3.8 27B TEE: 81.8%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Ling 3.0 Flash Thinking: 0.0%Top
- Qwen3.8 27B TEE: 0.0%Top
SciCode
Scientific computing in Python
- Ling 3.0 Flash Thinking: 42.0%Top
- Qwen3.8 27B TEE: 36.2%
Legacy evaluations
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Ling 3.0 Flash Thinking: 73.0%Top
- Qwen3.8 27B TEE: 69.3%
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.
- Request success (24h)
- 100.0%
- Reliable
- 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.
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:thinking",
"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:thinking",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)inclusionai
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