Model Comparison
Qwen2.5-VL-72B-Instruct
vs
Ring-flash-2.0
Feb 28, 2026
Pricing
Input
$
0.59
/ M Tokens
$
0.14
/ M Tokens
Output
$
0.59
/ M Tokens
$
0.57
/ M Tokens
Metadata
Create on
Jan 27, 2025
Sep 19, 2025
License
-
MIT LICENSE
Provider
Qwen
inclusionAI
Specification
State
Available
Available
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.
Mixture-of-Experts (MoE) with 1/32 expert activation ratio and MTP layers, featuring low activation and high sparsity design
Calibrated
No
Yes
Mixture of Experts
No
Yes
Total Parameters
72B
100B
Activated Parameters
72B
6.1B
Reasoning
No
No
Precision
FP8
FP8
Context length
131K
131K
Max Tokens
4K
131K
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
Not supported
Not supported
Structured Outputs
Not supported
Not supported
Tools
Not supported
Not supported
Fim Completion
Not supported
Not supported
Chat Prefix Completion
Supported
Supported
Qwen2.5-VL-72B-Instruct in Comparison
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step3
