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.
Suggestions for your first prompt
Code snippet
Call the model through the OpenAI-compatible API.
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
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Conversational Chat
Supports multi-turn natural language dialogues, following instructions and maintaining context for general-purpose assistant and chatbot applications.
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Text Monitoring
Enables content analysis and safety monitoring, helping classify or filter user-generated text based on policies or business rules.
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Image Capabilities
Processes input images to assist with multimodal tasks like visual context understanding, when integrated into supported MiniMax products.
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Text Translation
Translates between multiple languages for everyday communication and application localization within MiniMax’s supported language pairs.
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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.
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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
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
COMPARE
Competitive Models
-
MiniMax M2
MiniMax M2 is an open‑weight Mixture‑of‑Experts large language model from MiniMax, designed to deliver high coding and agentic workflow performance with low latency and cost. It uses 230B total parameters with only about 10B active per token to balance strong reasoning with efficient deployment.
-
MiniMax M2.5
MiniMax M2.5 is a frontier-class, agent-native large language model from MiniMax that combines a Mixture-of-Experts architecture with long-context, cost-efficient inference for real-world productivity tasks.
-
MiniMax M2.7
MiniMax M2.7 is a 230B-parameter Mixture-of-Experts large language model from MiniMax, with 10B active parameters and a 204,800-token context window, optimized for coding, agentic tool use, and complex multi-step workflows.
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