Ling 3.0 Flash

Ling 3.0 Flash

InclusionAI • text#250 of 704 in intelligence

Data source: Artificial Analysis · ID AA: e33dc369-ae81-40e0-8c4e-28b002d02197 · Snapshot published: 10/27/2026, 6:00:26 PM · Methodology version: 4.3

API Available

Specifications

AA Intelligence Index

20.1

Artificial Analysis · 4.3 · 10/27/2026, 6:00:26 PM

Context Window

—

Input Price/1M

$0.07

#43 of 445

Output Price/1M

$0.22

Parameters

—

Cost per Task

$0.0277

USD / task · lower is better · Artificial Analysis · 4.3 · 10/27/2026, 6:00:26 PM

Benchmarks

Artificial Analysis metrics for Ling 3.0 Flash. Higher is better for capability indexes; lower is better for cost per task.

Agentic

BenchmarkScoreMaximumMethodologySource / version / date
AA Agentic Index19.3100.0Artificial Analysis official APIArtificial Analysis · 4.3 · 10/27/2026

Coding

BenchmarkScoreMaximumMethodologySource / version / date
AA Coding Index50.6100.0Artificial Analysis official APIArtificial Analysis · 4.3 · 10/27/2026

Cost

BenchmarkScoreMaximumMethodologySource / version / date
AA Cost Per Task0.0100.0Artificial Analysis official APIArtificial Analysis · 4.3 · 10/27/2026

overall

BenchmarkScoreMaximumMethodologySource / version / date
AA Intelligence Index20.1100.0Artificial Analysis official APIArtificial Analysis · 4.3 · 10/27/2026

Information

Release date
August 04, 2026
Tool Calling
❌ Not supported
Vision
❌ Not supported
Audio
❌ Not supported

Full Analysis: Ling 3.0 Flash

What is Ling 3.0 Flash?

Ling 3.0 Flash is an AI model developed by InclusionAI, classified as a text model. It focuses on text processing and natural language generation. As a proprietary model, it is available via InclusionAI's cloud API.

Pricing & Costs in 2026

Ling 3.0 Flash is usage-based, priced at $0.075/1M input tokens and $0.22/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.

Benchmarks & Performance

Ling 3.0 Flash was evaluated on 4 different benchmarks, covering categories like Agentic, Coding, Cost, overall. Results show moderate performance across available evaluations.

It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.

Recommended Use Cases

Ling 3.0 Flash specializes in text, offering advanced capabilities for creating and processing text content.

Comparison with Alternatives

In the 2026 AI model ecosystem, Ling 3.0 Flash competes directly with similarly capable models. InclusionAI competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.

For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.

Frequently Asked Questions

What is Ling 3.0 Flash?

Ling 3.0 Flash is an AI model developed by InclusionAI. It is a text model.

How much does Ling 3.0 Flash cost?

Ling 3.0 Flash costs $0.075/1M input tokens and $0.22/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.

How does Ling 3.0 Flash compare with other models?

In available benchmarks, Ling 3.0 Flash scored: AA Agentic Index: 19.3/100, AA Coding Index: 50.6/100, AA Cost Per Task: 0.0277/100. See the full table above for a detailed comparison.

Is Ling 3.0 Flash open source?

No, Ling 3.0 Flash is a proprietary model from InclusionAI. It is available via cloud API. For open source alternatives, check our open source model ranking.

What is Ling 3.0 Flash best for?

Ling 3.0 Flash excels at general-purpose language tasks.

Last updated: October 27, 2026 • View methodology →