Model Comparison
Ling-flash-2.0
vs
Qwen2.5-VL-72B-Instruct
Feb 28, 2026
Pricing
Input
$
0.14
/ M Tokens
$
0.59
/ M Tokens
Output
$
0.57
/ M Tokens
$
0.59
/ M Tokens
Metadata
Create on
Sep 17, 2025
Jan 27, 2025
License
MIT LICENSE
-
Provider
inclusionAI
Qwen
Specification
State
Available
Available
Architecture
Ling 2.0 MoE architecture
Vision-Language Model (VLM) with a Streamlined and Efficient Vision Encoder (ViT with window attention, SwiGLU, RMSNorm) aligned with the Qwen2.5 LLM structure. Features include Dynamic Resolution and Frame Rate Training for video understanding, mRoPE for temporal sequence and speed, and YaRN for long text context length extrapolation.
Calibrated
No
No
Mixture of Experts
Yes
No
Total Parameters
100B
72B
Activated Parameters
6.1B
72B
Reasoning
No
No
Precision
FP8
FP8
Context length
131K
131K
Max Tokens
131K
4K
Supported Functionality
Serverless
Supported
Supported
Serverless LoRA
Not supported
Not supported
Fine-tuning
Not supported
Not supported
Embeddings
Not supported
Not supported
Rerankers
Not supported
Not supported
Support image input
Not supported
Not supported
JSON Mode
Supported
Not supported
Structured Outputs
Not supported
Not supported
Tools
Supported
Not supported
Fim Completion
Not supported
Not supported
Chat Prefix Completion
Supported
Supported
Ling-flash-2.0 in Comparison
See how Ling-flash-2.0 compares with other popular models across key dimensions.
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