MiniMax M2.5: The AI Model Powering MaxClaw

Technical Specifications

MiniMax M2.5 at a Glance

M2.5 is a Mixture-of-Experts (MoE) model that activates only a fraction of its total parameters for each token, achieving high intelligence at dramatically lower compute cost. Below are the key specifications.

Specification	MiniMax M2.5
Architecture	Mixture of Experts (MoE)
Total Parameters	229 Billion
Active Parameters per Token	~10 Billion
Context Window	200K – 1M Tokens
Inference Speed	Up to 100 Tokens/s
Cost vs Claude 3.5 Sonnet	1/7 to 1/20
Primary Strengths	Code generation, multi-step tool calling, logical reasoning
Coding Performance	Comparable to Claude 3.5 Sonnet

Core Innovation

Lightning Attention: Linear Scaling for Long Context

Traditional Transformer models suffer from quadratic complexity in their attention mechanism -- as context length doubles, compute cost quadruples. Lightning Attention is MiniMax's solution: a linear attention mechanism that scales proportionally with sequence length, enabling context windows that would be prohibitively expensive with standard SoftMax attention.

How Lightning Attention Works

Lightning Attention replaces the standard quadratic-cost attention computation with a linear approximation that preserves the model's ability to attend to relevant information across the full context window. The key insight is that most attention patterns in practice are sparse -- tokens attend strongly to only a small subset of other tokens -- and Lightning Attention exploits this sparsity to reduce the computational graph.

Why This Matters for Agentic Tasks

AI agents need to maintain context across long, multi-step interactions -- tracking conversation history, tool outputs, intermediate reasoning, and user preferences across an entire session. Lightning Attention makes this feasible without the exponential cost increase that would normally accompany such long context requirements. For MaxClaw, this translates to persistent memory and document analysis capabilities that remain responsive even as sessions grow long.

Architecture Deep Dive

Mixture of Experts: 229B Parameters, ~10B Active

The Mixture-of-Experts architecture is the second major innovation that defines M2.5. Rather than activating all 229 billion parameters for every token (as a dense model would), M2.5 uses a learned routing mechanism to activate only the most relevant subset -- approximately 10 billion parameters -- for each token processed.

How MoE Delivers Efficiency

In a MoE architecture, the model contains many specialized "expert" subnetworks. A gating mechanism evaluates each incoming token and routes it to the experts best suited to process it. The result is that the model has the total knowledge capacity of a 229B-parameter model but the computational cost of a ~10B-parameter model per inference step.

Intelligence Without the Compute Bill

The fundamental trade-off of MoE is well understood: sparse models require more total parameters to match the quality of dense models, but the compute cost per token is dramatically lower. M2.5 demonstrates that a well-designed 229B MoE model can achieve coding performance comparable to Claude 3.5 Sonnet -- a much denser architecture -- while running at a fraction of the cost. This is the core economic insight that makes MaxClaw's pricing model viable for high-frequency automation.

Practical Impact

How M2.5 Powers MaxClaw

MaxClaw launched on February 25, 2026, as the cloud-hosted AI agent built by MiniMax. M2.5 is not just the underlying model -- it is specifically optimized for the agentic workloads that MaxClaw handles. Here is how each M2.5 capability maps to a MaxClaw feature.

Long Context for Persistent Memory

The 200K to 1M token context window enables MaxClaw to maintain persistent memory across extended sessions. Agents can reference earlier parts of long conversations, analyze uploaded documents, and accumulate context about user preferences and workflows without losing track. Combined with Lightning Attention's linear scaling, this long context capability remains cost-effective even as sessions grow to tens of thousands of exchanges.

Complex Reasoning

M2.5's logical reasoning capabilities allow MaxClaw agents to handle tasks that require multi-step deduction, conditional logic, and structured problem-solving. This goes beyond simple question-answering into genuine task completion -- planning sequences of actions, evaluating outcomes, and adapting strategies based on intermediate results.

Competitive Context

M2.5 Against Other Frontier Models

Understanding where M2.5 sits relative to other leading models helps clarify its strengths and trade-offs. The following comparisons are based on the model's published capabilities and pricing.

Dimension	MiniMax M2.5	Claude 3.5 Sonnet	Kimi K2.5
Architecture	229B MoE (~10B active)	Dense (undisclosed size)	1T MoE
Context Window	200K – 1M tokens	200K tokens	128K tokens
Coding	Comparable to Claude 3.5	Frontier-level	Strong
Cost (Relative)	1x (baseline)	7x – 20x	Higher (1T params)
Inference Speed	Up to 100 tok/s	Moderate	Moderate
Agentic Optimization	Primary focus	General purpose	General purpose

vs Claude 3.5 Sonnet

M2.5 achieves coding performance comparable to Claude 3.5 Sonnet while costing between 1/7 and 1/20 as much per token. The trade-off is that Claude 3.5 is a more general-purpose model with broader coverage across creative, analytical, and conversational tasks, while M2.5 is specifically optimized for agentic and coding workloads. For MaxClaw's use case -- autonomous agent execution -- this specialization is an advantage, not a limitation.

vs GPT-4o

The MiniMax-01 series surpasses GPT-4o in long-context capabilities, with context windows extending up to 4M tokens compared to GPT-4o's 128K. M2.5 inherits this lineage and carries a 200K-1M token window that comfortably exceeds GPT-4o's context capacity, making it better suited for tasks requiring extensive document analysis or long-running conversation memory.

vs Kimi K2.5

Kimi K2.5 is a 1-trillion-parameter MoE model -- substantially larger than M2.5's 229B parameters. However, larger is not always better: the additional parameters mean higher inference cost and more complex infrastructure requirements. M2.5's leaner architecture translates to lower cost per token and faster inference speeds, which are critical advantages in high-frequency agentic workloads where MaxClaw operates. M2.5 was co-launched with MaxClaw on February 26, 2026, reflecting MiniMax's strategy of optimizing model and agent deployment together.

Model Lineage

The MiniMax Model Family

M2.5 is the latest in a series of models from MiniMax that share a common architectural philosophy: hybrid attention mechanisms combined with Mixture-of-Experts for efficient, long-context intelligence.