Most AI models choke at 8K-32K tokens. For tasks like analyzing an entire codebase, reviewing a legal contract, or summarizing a book, you need a model that can handle hundreds of thousands of tokens in a single call.
MiniMax-Text-01 is a 456 billion parameter model with a 1 million token context window — and it costs just $0.20 per million input tokens through NovAI.
| Content Type | Approximate Size | Cost to Process |
|---|---|---|
| A full novel (80K words) | ~100K tokens | $0.02 |
| Medium codebase (500 files) | ~300K tokens | $0.06 |
| Legal contract bundle | ~200K tokens | $0.04 |
| Research paper collection (20 papers) | ~400K tokens | $0.08 |
| Entire Git repo history + code | ~800K tokens | $0.16 |
| Model | Max Context | Input/1M | Output/1M |
|---|---|---|---|
| MiniMax-Text-01 | 1,000,000 | $0.20 | $1.60 |
| Gemini 1.5 Pro | 2,000,000 | $1.25 | $5.00 |
| GPT 3.5 Sonnet | 200,000 | $3.00 | $15.00 |
| GPT-4o | 128,000 | $2.50 | $10.00 |
MiniMax is 6x cheaper than Gemini 1.5 Pro on input and 3x cheaper on output, with a context window that handles most real-world long-document tasks.
from openai import OpenAI
client = OpenAI(
api_key="nvai-your-api-key",
base_url="https://aiapi-pro.com/v1"
)
# Load a large document
with open("large_document.txt") as f:
document = f.read()
response = client.chat.completions.create(
model="minimax-text-01",
messages=[
{"role": "system", "content": "Analyze the following document thoroughly."},
{"role": "user", "content": f"Summarize the key findings:\n\n{document}"}
],
max_tokens=4096
)
print(response.choices[0].message.content)
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