Ling-mini-2.0
About Ling-mini-2.0
Ling-mini-2.0 is a small yet high-performance large language model built on the MoE architecture. It has 16B total parameters, but only 1.4B are activated per token (non-embedding 789M), enabling extremely fast generation. Thanks to the efficient MoE design and large-scale high-quality training data, despite having only 1.4B activated parameters, Ling-mini-2.0 still delivers top-tier downstream task performance comparable to sub-10B dense LLMs and even larger MoE models
Explore how Ling-mini-2.0's fast generation, long context, and strong reasoning can solve complex, real-world problems efficiently.
Codebase Analysis & Refactoring
Rapidly analyze large codebases (128K context) for architectural flaws, security vulnerabilities, and refactoring opportunities, providing instant, context-aware suggestions.
Use Case Example:
"Identified and suggested refactoring for a complex microservices architecture written in Go, improving maintainability and reducing potential deadlocks across 50+ files."
Real-time Content Generation
Generate or summarize extensive reports, articles, or marketing copy in real-time, adapting to user input and maintaining coherence over long documents.
Use Case Example:
"Automatically generated daily market summaries from 100+ news articles and financial reports, delivering concise, actionable insights to traders within minutes."
Legal & Regulatory Compliance
Quickly review lengthy legal contracts, regulatory documents, and policy manuals to identify clauses, ensure compliance, and flag potential risks.
Use Case Example:
"Scanned a 500-page merger agreement, highlighting all clauses related to intellectual property transfer and identifying potential conflicts with existing patent licenses in under a minute."
Dynamic Customer Support
Power intelligent chatbots and virtual assistants that understand complex queries, access extensive knowledge bases, and provide fast, accurate, personalized support.
Use Case Example:
"Integrated into a customer service platform, it resolved 85% of common technical support issues by quickly analyzing user logs and product manuals, reducing agent workload."
Scientific Hypothesis Generation
Analyze vast scientific datasets and research papers to identify patterns, generate novel hypotheses, and assist in experimental design with rapid, logical deductions.
Use Case Example:
"Processed genomic sequencing data and related research literature for a drug discovery project, suggesting potential gene targets and experimental pathways that accelerated lead identification."
Metadata
Specification
State
Deprecated
Architecture
Calibrated
Yes
Mixture of Experts
Yes
Total Parameters
16B
Activated Parameters
1.4B
Reasoning
No
Precision
FP8
Context length
131K
Max Tokens
131K
Compare with Other Models
See how this model stacks up against others.
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Ling-mini-2.0
Release on: Sep 10, 2025
Total Context:
131K
Max output:
131K
Input:
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0.07
/ M Tokens
Output:
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0.28
/ M Tokens
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Ling-flash-2.0
Release on: Sep 18, 2025
Total Context:
131K
Max output:
131K
Input:
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0.14
/ M Tokens
Output:
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0.57
/ M Tokens
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Ring-flash-2.0
Release on: Sep 29, 2025
Total Context:
131K
Max output:
131K
Input:
$
0.14
/ M Tokens
Output:
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0.57
/ M Tokens
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Ling-1T
Release on: Oct 11, 2025
Total Context:
131K
Max output:
Input:
$
0.57
/ M Tokens
Output:
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2.28
/ M Tokens
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Ring-1T
Release on: Oct 14, 2025
Total Context:
131K
Max output:
Input:
$
0.57
/ M Tokens
Output:
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2.28
/ M Tokens
