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.
What is MiniMax M2.5?
MiniMax M2.5 is a state-of-the-art, agent-focused large language model designed to reason efficiently, decompose tasks, and complete complex workflows under real-world time and cost constraints. It is primarily used for coding assistance, tool-using agents, and complex multi-step automation across workflows like office productivity and data processing. It also serves general-purpose chat, analysis, and long-context reasoning use cases, including document-heavy and enterprise scenarios. M2.5 is part of MiniMax’s M2 series of models, succeeding MiniMax M2 and M2.1 within the same family of agentic LLMs.
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 |
|---|---|---|---|---|---|---|
| AWS | $0.30 | $1.20 | $0.03 | — | — | 100.00% |
| Canopywave | $0.27 | $1.08 | $0.03 | — | — | — |
| Embercloud | $0.20 | $1.20 | $0.03 | — | — | — |
| MiniMax | $0.30 | $1.20 | $0.03 | ~220ms | ~40 tps | ~99.9% |
| Novita | $0.30 | $1.20 | $0.03 | — | — | — |
| Together AI | $0.30 | $1.20 | $0.03 | — | — | — |
Try this model
Test MiniMax M2.5 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-m2-5",
messages=[
{
"role": "user",
"content": "Describe this image in one sentence."
}
],
)
print(response.to_json())
{
"model": "minimax/minimax-m2-5",
"messages": [
{
"role": "user",
"content": "Describe this image in one sentence."
}
]
}
5 Core Capabilities
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Advanced Coding
Delivers state-of-the-art multilingual code generation, debugging, and full lifecycle software development across over ten programming languages.
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Agentic Tool Use
Coordinates complex multi-step tasks, calling external tools and search services efficiently for real-world automation and agent workflows.
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Long-Context Reasoning
Handles very large text contexts with efficient reasoning traces, supporting extended documents, conversations, and multi-stage problem solving.
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Business Productivity
Automates office workflows such as document drafting, summarization, reporting, and analysis to support knowledge work across business functions.
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Multilingual Text
Understands and generates text in many languages, enabling cross-lingual communication, content creation, and localization scenarios.
6 Most Valuable Use Cases
- Customer Service Chatbots
- Marketing Copywriting
- Legal Draft Assistance
- Compliance Case Monitoring
- E-commerce Product Support
- Code Generation Help
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
Automatically route each request to the best model across providers based on latency, cost, and capability—without changing your integration or redeploying.
One endpoint, every model -
Cost-Aware Orchestration
Optimize spend by mixing premium and budget models per request, enforcing hard budgets and quotas with centralized policies instead of per-provider custom logic.
Cut costs, keep quality -
Resilient Fallback Flows
Define provider-agnostic failover chains so timeouts, rate limits, or outages automatically retry against backup models—keeping production apps responsive and reliable.
No single point of failure -
Full-Stack Observability
Get unified logs, traces, metrics, and structured events for every model call, across all vendors, to debug latency, errors, and quality from one place.
See every token, everywhere -
Task-Level Abstractions
Call high-level tasks—chat, tools, retrieval, structured output—without wiring provider-specific APIs, freeing you to evolve models without refactoring application code.
Code to tasks, not models -
High-Throughput Batch Runs
Run large offline workloads—evaluations, backfills, fine-tuning prep—through a single batch API with queuing, retries, and cost controls built in.
Scale batches without chaos
When to Use — When NOT to Use
Use it if...
- You need a capable general-purpose LLM for chatbots and virtual assistants deployment.
- You need solid coding assistance for common programming languages and everyday software engineering tasks.
- Your use case involves multilingual chat or content, especially including strong Chinese language support.
- You need a balance of quality and cost for large-scale text generation workloads.
- Your use case involves summarizing or transforming moderately long business documents or knowledge articles.
- You need to prototype AI features quickly with a reasonably capable, general LLM backend.
Avoid if...
- You need frontier-level reasoning performance on complex math, logic, or scientific problems.
- Your workload requires state-of-the-art code generation and debugging on very large codebases.
- You need guaranteed, best-in-class safety filters and enterprise compliance certifications across jurisdictions.
- Your workload requires extremely long context handling for book-length documents or massive transcripts.
- You need a fully open-source, self-hostable model with transparent weights and training data.
- Your workload requires tight integration with a specific proprietary ecosystem MiniMax does not support.
Frequently Asked Questions
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What is MiniMax M2.5?
MiniMax M2.5 is a general-purpose large language model by MiniMax focused on fast, cost-efficient text generation for mainstream application workloads.
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What is the context window of MiniMax M2.5 via LLM.API?
MiniMax M2.5 supports up to a 32,768-token context window when accessed through LLM.API.
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What is MiniMax M2.5 best suited for?
MiniMax M2.5 is best for chatbots, content generation, lightweight reasoning, and other latency-sensitive, high-throughput text applications.
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How much does it cost to use MiniMax M2.5 on LLM.API?
LLM.API exposes MiniMax M2.5 with usage-based pricing per 1,000 tokens for input and output; check the LLM.API pricing page for current rates.
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How fast is MiniMax M2.5 in terms of latency and throughput?
MiniMax M2.5 is optimized for low latency and high throughput, making it suitable for real-time and large-scale concurrent request scenarios.
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What modalities does MiniMax M2.5 support on LLM.API?
On LLM.API, MiniMax M2.5 supports text input and text output; it does not natively handle images, audio, or video.
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How do I call MiniMax M2.5 through LLM.API?
You select the MiniMax M2.5 model name in LLM.API requests, send standard chat or completion payloads, and receive responses in a unified JSON schema.
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How does MiniMax M2.5 compare to similar mid-tier LLMs?
MiniMax M2.5 typically offers a tradeoff of lower cost and faster responses with somewhat weaker reasoning and coding than top-tier flagship models.
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What are the main limitations of MiniMax M2.5?
MiniMax M2.5 can hallucinate facts, struggle with very complex multi-step reasoning, and lacks up-to-date real-world knowledge beyond its training cutoff.
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Can I fine-tune or customize MiniMax M2.5 through LLM.API?
LLM.API currently exposes MiniMax M2.5 as a hosted, non-fine-tunable model, but you can steer behavior using system prompts and few-shot examples.
COMPARE
Competitive Models
-
MiniMax M2.5 (free)
MiniMax M2.5 (free) is a third-generation, open-source agentic large language model from MiniMax, offered via multiple providers with free usage tiers. It is notable for its long context window and strong coding and productivity capabilities while remaining cost-efficient.
-
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.
-
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.
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