MiniMax · Text

Hermes Agent x MiniMax

MiniMax M-series text models are sparse MoE LLMs with 200K–1M context, delivering frontier coding scores at a fraction of the cost of dense models. Through RunAPI they share a single API key with pay-as-you-go token billing, callable from the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages surfaces. These are MiniMax's text models, distinct from MiniMax Hailuo video generation.

Public versions and endpoints

Model IDVersionEndpointsStarting priceCatalog
MiniMax-M2230B / 10B active; 200K context; baseline M-series coding model
/v1/chat/completions
$0.0002Model detail
MiniMax-M2.1200K context; polyglot programming; 74% SWE-bench Verified
/v1/chat/completions
$0.0002Model detail
MiniMax-M2.5200K context; stronger agentic tool calling and search
/v1/chat/completions
$0.0002Model detail
MiniMax-M2.5-highspeedM2.5 at ~100 tokens/sec; same weights, higher throughput
/v1/chat/completions
$0.0004Model detail
MiniMax-M2.7200K context; 56.2% SWE-bench Pro; self-evolving training
/v1/chat/completions
$0.0002Model detail
MiniMax-M2.7-highspeedM2.7 at ~100 tokens/sec; same weights, higher throughput
/v1/chat/completions
$0.0004Model detail
MiniMax-M31M context; 80.5% SWE-bench Verified; frontier open-weight coding + long context
/v1/chat/completions
$0.0002Model detail

Configuration

Guide endpoint: <endpoint>

Select <model-id> to generate a request with the endpoint's public input contract.

Verify

Select <model-id> to generate verification commands.

How it works

  1. 1. Choose a model ID

    Select a public catalog model ID and review its endpoint and current starting price.

  2. 2. Configure RunAPI

    Add the provider configuration for this agent.

  3. 3. Verify the result

    Run the agent status and model-selection commands shown below.

FAQ

Which model ID should I use?

Choose a public model ID from the version table. Each ID exposes the endpoints shown for that version.

Does this guide configure a chat model?

Yes. This Model Line supports a client-facing LLM protocol.