MiniMax M2.1 long-context model
MiniMax M2.1 stands out as a premier long-context solution within the LLM Resayil ecosystem, engineered for developers requiring extensive data processing without performance degradation. With a massive 1,000,000-token context window and FP16 quantization, this model handles complex document analysis and multi-turn conversations effortlessly. Integration is streamlined for immediate deployment; you can execute your first API call within minutes using our standard endpoints. The architecture ensures low-latency responses even when processing large volumes of text, making it ideal for building robust applications that demand high fidelity and retention over extended interactions.
For researchers and enterprise leaders, MiniMax M2.1 delivers competitive performance across bilingual tasks, demonstrating strong capabilities in both Arabic and English understanding. While specific benchmark tables are available in our detailed documentation, early testing confirms its suitability for production pipelines requiring nuanced language processing. The model operates on a 2.5x credit multiplier relative to our base rate, accessible from the starter tier. This transparent pricing structure allows for accurate cost forecasting in your currency without needing sales consultations. Whether optimizing for research accuracy or commercial scalability, MiniMax M2.1 provides the reliability and linguistic depth necessary for serious deployment.
from openai import OpenAI
client = OpenAI(
base_url="https://llmapi.resayil.io/v1/",
api_key="YOUR_API_KEY"
)
response = client.chat.completions.create(
model="minimax-m2.1",
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(response.choices[0].message.content)import anthropic
client = anthropic.Anthropic(
base_url="https://llmapi.resayil.io/v1",
api_key="YOUR_API_KEY"
)
message = client.messages.create(
model="minimax-m2.1",
max_tokens=1024,
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(message.content[0].text)const response = await fetch(
"https://llmapi.resayil.io/v1/chat/completions",
{
method: "POST",
headers: {
"Content-Type": "application/json",
"Authorization": "Bearer YOUR_API_KEY"
},
body: JSON.stringify({
model: "minimax-m2.1",
messages: [
{ role: "user", content: "Hello!" }
]
})
}
);
const data = await response.json();
console.log(data.choices[0].message.content);curl https://llmapi.resayil.io/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "minimax-m2.1",
"messages": [
{"role": "user", "content": "Hello!"}
]
}'