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MiniMax M2-her

MiniMax M2-her is a dialogue-first large language model from MiniMax, optimized for immersive roleplay, character-driven chat, and expressive multi-turn conversations.

What is MiniMax M2-her?

MiniMax M2-her is a specialized variant of the MiniMax M2 large language model designed primarily for dialogue and character-focused interaction. It is mainly used for immersive roleplay, storytelling, and companion-style chat experiences that require strong character consistency and emotional nuance. It also serves creative writing, interactive fiction, and other conversational applications where maintaining long, coherent, multi-turn dialogues is important. M2-her belongs to the MiniMax M2 model family, which includes other general-purpose and high-speed variants such as M2, M2.1, and M2.5.


Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).

Provider Input Output Cache read /M Latency Throughput Uptime
MiniMax ~$0.70 ~$0.70 ~220ms ~60 tps ~99.9%
OpenAI (GPT-4.1 Mini-equivalent) ~$0.50 ~$1.50 ~250ms ~80 tps ~99.9%
Anthropic (Claude 3 Haiku-equivalent) ~$0.40 ~$1.20 ~260ms ~70 tps ~99.9%
Azure AI (MiniMax-compatible deployment) ~$0.60 ~$0.60 ~240ms ~65 tps ~99.9%

Try this model

Test MiniMax M2-her right here — free to start.

MiniMax M2-her
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call the model through the OpenAI-compatible API.

python
                                        from openai import OpenAI
                                            
                                            client = OpenAI(
                                                api_key="YOUR_API_KEY",
                                                base_url="https://inference.example.com/v1"
                                            )
                                            
                                            response = client.chat.completions.create(
                                                model="minimax/minimax-m2-her",
                                                messages=[
                                                    {
                                                        "role": "user",
                                                        "content": "Describe this image in one sentence."
                                                    }
                                                ],
                                            )
                                            
                                            print(response.to_json())
                                        
                                    
                                        {
                                                "model": "minimax/minimax-m2-her",
                                                "messages": [
                                                    {
                                                        "role": "user",
                                                        "content": "Describe this image in one sentence."
                                                    }
                                                ]
                                            }
                                        
                                    

5 Core Capabilities

  • Conversational Chat

    Supports multi-turn natural language dialogues, following instructions and maintaining context for general-purpose assistant and chatbot applications.

  • Text Monitoring

    Enables content analysis and safety monitoring, helping classify or filter user-generated text based on policies or business rules.

  • Image Capabilities

    Processes input images to assist with multimodal tasks like visual context understanding, when integrated into supported MiniMax products.

  • Text Translation

    Translates between multiple languages for everyday communication and application localization within MiniMax’s supported language pairs.

  • Optical Character Recognition

    Extracts machine-readable text from images or screenshots when paired with MiniMax tooling that exposes OCR functionality.

6 Most Valuable Use Cases

  • Immersive Roleplay Chat
  • Story Co-writing
  • AI Companion Chatbots
  • Interactive Fiction Games
  • Language Practice Partner
  • Emotional Dialogue Testing

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Unified AI Routing

    Dynamically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.

    Smart routing, one API
  • Cost-Aware Orchestration

    Automatically balance premium and budget models using configurable policies, so you control spend while keeping performance and user experience predictable at scale.

    Optimize spend, not code
  • Resilient Fallback Flows

    Define provider- and model-level fallback chains that trigger on errors, timeouts, or bad responses—keeping your production workloads online when vendors fail.

    Never ship single-vendor
  • Deep Observability

    Get centralized traces, metrics, and logs across all LLM calls—latency, errors, cost, and model behavior—so you can debug, tune, and prove reliability to stakeholders.

    See every token, everywhere
  • Task-Level Abstractions

    Describe tasks like chat, generation, tools, or structured output once and let LLM.API map them to the right provider-specific APIs under the hood.

    Think tasks, not vendors
  • High-Throughput Batching

    Submit large batches of prompts in a single request with concurrency control and vendor-aware limits, cutting latency and API costs for bulk workloads.

    Scale up, pay less

When to Use — When NOT to Use

Use it if...

  • You need a general-purpose Chinese-centric model from a major Chinese AI provider.
  • You need cost-effective text generation for chatbots serving primarily Chinese-speaking users.
  • Your use case involves everyday assistant tasks like Q&A, drafting, and rewriting text.
  • Your use case involves integrating a mainstream Chinese LLM into an existing local tech stack.
  • You need moderate reasoning and conversation quality without requiring state-of-the-art performance.
  • You need to experiment with a variety of MiniMax-hosted models within one ecosystem.

Avoid if...

  • You need cutting-edge reasoning performance comparable to the very best frontier models available.
  • Your workload requires strong support, optimization, and documentation primarily in English environments.
  • You need guaranteed world-class performance on advanced coding, math, or complex scientific tasks.
  • Your workload requires strict US or EU compliance guarantees and detailed public security attestations.
  • You need highly specialized domain models, such as finance-grade or medical-grade tuned systems.
  • Your workload requires seamless interoperability with leading Western foundation-model platforms and tooling.

Frequently Asked Questions

  • What is MiniMax M2-her?

    MiniMax M2-her is a large language model from MiniMax designed for fast, cost-efficient text generation and reasoning via the LLM.API gateway.

  • What is the context window of MiniMax M2-her?

    MiniMax M2-her supports multi-thousand token prompts, suitable for moderately long conversations, documents, and tool-augmented workflows through LLM.API.

  • What modalities does MiniMax M2-her support via LLM.API?

    Via LLM.API, MiniMax M2-her currently operates as a text-in, text-out model for chat, completion, and tool-calling style interactions.

  • How does MiniMax M2-her pricing work on LLM.API?

    MiniMax M2-her is billed per 1,000 input and output tokens according to LLM.API’s MiniMax-specific pricing, visible in your LLM.API dashboard.

  • What latency should I expect from MiniMax M2-her on LLM.API?

    Typical end-to-end latency is on the order of a few hundred milliseconds to a couple of seconds, depending on prompt size and load.

  • How do I call MiniMax M2-her through LLM.API?

    You select the MiniMax M2-her model name in the LLM.API completions or chat endpoint and authenticate using your LLM.API key.

  • What is MiniMax M2-her particularly good at?

    MiniMax M2-her is well-suited for general chatbots, drafting, rewriting, basic code assistance, and domain-specific reasoning when provided with clear instructions.

  • How does MiniMax M2-her compare to similar models?

    MiniMax M2-her typically offers a balance of speed and quality comparable to mid-sized general-purpose LLMs, often at a lower per-token cost.

  • What are the main limitations of MiniMax M2-her?

    MiniMax M2-her can hallucinate facts, lacks guaranteed up-to-date knowledge, and may struggle with very long-context reasoning or highly specialized expert domains.

  • Can I use MiniMax M2-her for streaming responses?

    Yes, when enabled in LLM.API, MiniMax M2-her can stream partial tokens to reduce perceived latency in interactive applications.

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