MiniMax M2.7 with 1M context window
MiniMax M2.7 stands out as a high-performance chat model available through LLM Resayil, engineered specifically for applications demanding extensive context retention. With a massive 1,000,000-token context window, this proprietary model enables developers to process entire codebases, lengthy legal documents, or complex multi-turn conversations without losing critical information. Running at FP16 precision, it ensures robust inference quality suitable for production environments where accuracy and coherence over long sequences are paramount for reliable system behavior.
Integration is streamlined for immediate deployment, requiring only a starter tier account to access this advanced capability via our API. While the credit multiplier sits at 3.5x relative to the base rate, the trade-off delivers exceptional value for tasks requiring deep contextual understanding that smaller models simply cannot match. Developers should choose MiniMax M2.7 when building agents that need to synthesize vast amounts of input data or maintain state over extended interactions. This model represents a strategic choice for scaling AI solutions that demand both significant memory depth and high-level conversational fluency within a single request.
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-m2.7",
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-m2.7",
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-m2.7",
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-m2.7",
"messages": [
{"role": "user", "content": "Hello!"}
]
}'