LLM Context Window

• Tokens: Byte Pair Encoding (BPE) — ~0.75 word per token in English, 0.5 in Russian
• Context budget: input + output ≤ window. With output = 4k, max input = (window - 4k)
• Pricing: per token. 1M context × $3 per 1M = $3 per call
• Lost in the middle: LLMs remember middle of long context worse (2023 Stanford research)
• Caching: Anthropic prompt cache, OpenAI automatic cache — reduce cost for repeat context

# Claude 1M context in Claude Agent SDK
from anthropic import Anthropic
client = Anthropic()

# Full codebase in context
with open('codebase.txt') as f:
  codebase = f.read()  # 500k tokens

response = client.messages.create(
  model='claude-opus-4-7[1m]',  # 1M context variant
  max_tokens=4096,
  system='You review code.',
  messages=[{'role':'user','content':f'Review:
{codebase}'}]
)

• LLM
• Transformer
• RAG