Speed
Output tokens / second
Custom pricing for production workloads
MiniMax-M2.5 is a general-purpose large language model by MiniMax designed for text generation and conversational AI use cases.
A production-ready large language model built for text generation, conversational A now with a high-speed variant for real-time applications.
minimax/m2-5-20260218https://api.aimlapi.com/v1MiniMax-M2.5 is a general-purpose large language model developed by MiniMax, designed to power a wide spectrum of natural language applications from intelligent chatbots and virtual assistants to automated content generation and document analysis pipelines.
The flagship general-purpose language model from MiniMax. Delivers superior instruction-following, nuanced reasoning, and high-fidelity content generation. Designed for workloads where response quality and contextual depth are the primary objectives.
A throughput-optimized variant engineered for latency-sensitive applications. Achieves significantly faster time-to-first-token and higher requests-per-second capacity, making it the go-to choice for live user interactions and high-traffic services.
Power multi-turn, context-aware conversations for customer service, support automation, and virtual assistant platforms with natural, coherent dialogue management.
Automate the creation of articles, marketing copy, product descriptions, social media posts, and long-form editorial content at scale without sacrificing quality.
Summarize, classify, extract key information from, and answer questions about contracts, reports, research papers, and enterprise documents using extended context.
MiniMax M2.5 is documented as suited to coding and software engineering assistance, including SWE-bench-style tasks.
It is also documented for search and tool-calling workflows, including agentic tool use and information-retrieval tasks.
A third documented use case is office productivity work, such as document- and spreadsheet-oriented workflows.
It is also described as appropriate for general conversational assistant use.
See how MiniMax-M2.5 compares in generation speed and latency. Data retrieved Nov 3, 2026.
Output tokens / second
Compare 10 models available on AI/ML API across 5 independent benchmarks.
Expert-written questions across academic subjects.
Graduate-level science questions written to resist web search.
Reasoning across long documents of about 100k tokens.
Following precise instructions about the shape of the answer.
Customer support agent tasks with tool calls.
| Usage | Price | Unit |
|---|---|---|
| Input | $0.3 | 1M tokens |
| Output | $1.2 | 1M tokens |
| Cached input | $0.3 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
AI/ML API input and output rates for 5 models, per million tokens.
| Model | Input | Output |
|---|---|---|
| $0.3 | $1.2 | |
| $0.15 | $0.6 | |
| $0.3 | $1.2 | |
| $0.6 | $2.4 | |
| $0.6 | $2.4 |
Give your agent the model details, code and documentation.
export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"curl 'https://api.aimlapi.com/v1/chat/completions' \
-H "Authorization: Bearer $AIMLAPI_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "minimax/m2-5-20260218",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "minimax/m2-5-20260218",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}
response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "minimax/m2-5-20260218",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());MiniMax-M2.5 has a 204,800 tokens context window and can return up to 204,800 tokens.
MiniMax-M2.5 takes image, text as input and returns text.
Use minimax/m2-5-20260218 as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.
MiniMax-M2.5 costs $0.3 input and $1.2 output per 1M tokens on AI/ML API, billed as you go with no subscription.
Yes, MiniMax-M2.5 can stream responses as they are generated.
Yes, MiniMax-M2.5 accepts image input alongside text.
MiniMax-M2.5 was built by MiniMax.
Yes, it supports function calling, tool use, and parallel tool calls, along with structured output formatting.