Model Comparison
MiniMax-M2
vs
Ring-flash-2.0
Feb 28, 2026
Pricing
Input
$
0.3
/ M Tokens
$
0.14
/ M Tokens
Output
$
1.2
/ M Tokens
$
0.57
/ M Tokens
Metadata
Create on
Oct 22, 2025
Sep 19, 2025
License
MIT
MIT LICENSE
Provider
MiniMaxAI
inclusionAI
Specification
State
Deprecated
Available
Architecture
Mixture of Experts
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
Yes
Yes
Total Parameters
230B
100B
Activated Parameters
10B
6.1B
Reasoning
No
No
Precision
FP8
FP8
Context length
197K
131K
Max Tokens
131K
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
Supported
Not supported
Structured Outputs
Not supported
Not supported
Tools
Supported
Not supported
Fim Completion
Not supported
Not supported
Chat Prefix Completion
Supported
Supported
MiniMax-M2 in Comparison
See how MiniMax-M2 compares with other popular models across key dimensions.
VS
MiniMax-M2.5
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Step-3.5-Flash
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MiniMax-M2.1
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Qwen3-VL-235B-A22B-Instruct
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Qwen3-VL-235B-A22B-Thinking
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Ring-flash-2.0
VS
Qwen3-Next-80B-A3B-Instruct
VS
Qwen3-Next-80B-A3B-Thinking
VS
gpt-oss-120b
VS
step3
VS
Qwen3-235B-A22B-Thinking-2507
VS
Qwen3-Coder-480B-A35B-Instruct
VS
Qwen3-235B-A22B-Instruct-2507
VS
Qwen2.5-VL-72B-Instruct
VS
Qwen2.5-72B-Instruct
VS
Qwen2.5-72B-Instruct-128K
VS
VS
