Ling-1T
About Ling-1T
Ling-1T is the first flagship non-thinking model in the Ling 2.0 series, featuring 1 trillion total parameters with ≈ 50 billion active parameters per token. Built on the Ling 2.0 architecture, Ling-1T is designed to push the limits of efficient reasoning and scalable cognition. Pre-trained on 20 trillion+ high-quality, reasoning-dense tokens, Ling-1T-base supports up to 131K context length and adopts an evolutionary chain-of-thought (Evo-CoT) process across mid-training and post-training. This curriculum greatly enhances the model’s efficiency and reasoning depth, allowing Ling-1T to achieve state-of-the-art performance on multiple complex reasoning benchmarks—balancing accuracy and efficiency
Discover how Ling-1T's efficient, scalable reasoning and trillion-parameter architecture tackle complex challenges across diverse industries, balancing accuracy with unparalleled efficiency.
AI-Powered UI/UX Generation
Transform abstract design concepts and natural language into functional, aesthetically pleasing, and cross-platform front-end code, leveraging Ling-1T's visual reasoning.
Use Case Example:
"Generated a responsive React Native UI from a Figma design and natural language prompts, ensuring pixel-perfect rendering and optimal user experience across devices."
Enterprise Codebase Optimization
Analyze vast code repositories with 131K context length to identify architectural flaws, optimize performance bottlenecks, and suggest refactoring strategies with detailed reasoning.
Use Case Example:
"Pinpointed a critical race condition in a distributed Java microservices architecture across 500K lines of code, proposing a robust, thread-safe solution that improved system stability."
Automated Compliance Audits
Reason through extensive legal documents and regulatory frameworks to identify inconsistencies, potential risks, and ensure adherence to complex compliance standards.
Use Case Example:
"Audited a 100-page GDPR compliance document against a company's data handling policies, flagging five critical discrepancies and suggesting precise amendments for full adherence."
Accelerated Scientific Discovery
Analyze vast scientific literature and experimental data to formulate novel hypotheses, validate theories, and draft research findings with rigorous, step-by-step reasoning.
Use Case Example:
"Processed terabytes of genomic sequencing data to identify novel gene-disease associations, generating a statistically significant hypothesis for further experimental validation."
Intelligent Agent Orchestration
Interpret high-level goals, decompose them into sub-tasks, and orchestrate multiple specialized tools or APIs to achieve complex objectives autonomously with high tool-call accuracy.
Use Case Example:
"Coordinated a series of external APIs (CRM, marketing automation, analytics) to execute a personalized customer outreach campaign, dynamically adapting messaging based on real-time user engagement data."
Metadata
Specification
State
Deprecated
Architecture
Calibrated
No
Mixture of Experts
Yes
Total Parameters
1000B
Activated Parameters
50B
Reasoning
No
Precision
FP8
Context length
131K
Max Tokens
Compare with Other Models
See how this model stacks up against others.
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Ling-flash-2.0
Release on: Sep 18, 2025
Total Context:
131K
Max output:
131K
Input:
$
0.14
/ M Tokens
Output:
$
0.57
/ M Tokens
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Ling-mini-2.0
Release on: Sep 10, 2025
Total Context:
131K
Max output:
131K
Input:
$
0.07
/ M Tokens
Output:
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0.28
/ 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:
$
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:
$
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:
$
2.28
/ M Tokens
