MiniMax M2 Family Now Available on Amazon Bedrock

Amazon Web Services has integrated three models from MiniMax's M2 family into Amazon Bedrock, giving developers access to open-weight models optimized for software engineering and agentic use cases. The models run entirely on AWS infrastructure with data isolation guarantees.

Model Specifications

The three models differ in context windows and training focus:

MiniMax M2 (minimax.minimax-m2): 1 million token context window, 8K max output tokens, trained for multilingual text generation, reasoning, and coding.

MiniMax M2.1 (minimax.minimax-m2.1): 196K token context window, 8K max output tokens, with improvements to reasoning depth, coding accuracy, and instruction following.

MiniMax M2.5 (minimax.minimax-m2.5): 196K token context window, 8K max output tokens, 230 billion total parameters with 10 billion active per token. According to AWS, M2.5 was trained specifically for agent-native execution using reinforcement learning on agentic scaffolds.

All three models use a mixture-of-experts (MoE) architecture where only a fraction of parameters activate per token. For M2.5, this means the model delivers the capacity of a 230B parameter model while consuming compute equivalent to only 10B parameters per forward pass.

Pricing and Service Tiers

Pricing per million tokens was not disclosed in the announcement. AWS offers three service tiers for all models: Standard, Priority, and Flex. According to AWS, on-demand inference automatically scales to handle workload requirements.

Access Methods

AWS provides two endpoints for accessing the models:

bedrock-mantle endpoint (recommended): Uses the Chat Completions API, compatible with OpenAI Python and TypeScript SDK interfaces. Supports Amazon Bedrock API keys, projects, and client-side tool calling.

bedrock-runtime endpoint: Uses Converse and InvokeModel APIs via the AWS SDK. Required for native Bedrock features including Guardrails, Agents, Flows, and model evaluation.

The models are available immediately in the Amazon Bedrock console playground and via both API endpoints.

Data Protection

AWS states that prompts and completions are not used to train any models and content is not shared with MiniMax. Inference runs entirely on AWS-operated infrastructure.

What This Means

The addition of MiniMax models expands Bedrock's open-weight options beyond Meta's Llama and Mistral families. The 1 million token context window in M2 positions it for document analysis workloads, while M2.5's agent-focused training addresses the growing demand for tool-calling models. The MoE architecture's 10B active parameters per token could provide cost advantages over dense models of similar capability, though without public pricing comparisons are speculative. The dual endpoint system reflects AWS's evolution toward OpenAI-compatible APIs while maintaining backward compatibility with existing Bedrock integrations.