Context Window

A token is a unit of text. Typically, one token ≈ 4 characters. "Local" is 1 token; "citation audit" is 2 tokens. An entire blog post might be 2,000–3,000 tokens. A context window is the total number of tokens the model can hold in a single request — input plus output. If a model has a 200K token window, you can fit 200,000 tokens of combined user message, system instructions, tool definitions, chat history, and the model's response. Models trained in 2023–2024 typically offered 4K–32K tokens. Models in 2026 commonly offer 200K, 500K, or even 1M tokens. This expansion directly enables agentic workflows on local SEO datasets that would have been impossible in 2023.

An AI agent performing a citation audit needs context about the business, the audit results, the directory guidelines, and past corrections. A 4K model could barely hold the audit results. A 200K model can hold the audit, all directory standards, competitor citation patterns, and historical corrections in a single request. The agent reasons more accurately with more context. It catches patterns that smaller windows would miss. An agent in a large context window can also maintain conversation history across dozens of turns without forgetting prior results or decisions. This is critical for workflows like: "Audit this client. Check competitor citations. Identify gaps. Propose a 6-month plan." Each step builds on prior results. With a small context window, the agent has to summarize and hand off between steps. With a large window, it reasons holistically.

Local SEO work is data-heavy. A full citation audit for a single business returns data from 20+ directories, each with multiple fields (name, address, phone, hours, categories). A multi-location client might have 50–100 locations; a geogrid scan returns hundreds of ranking data points. A review velocity analysis can span years of review data. In 2023–2024, fitting this into a 4K–32K window meant chunking the data across multiple API calls, losing context between chunks. With a 200K+ window, an agent can ingest an entire audit, all competitor data, and all historical context in one shot. Example: A 200-location franchise audit generates ~15,000 tokens of raw data. Citation guidelines: ~2,000 tokens. Prior year audit: ~3,000 tokens. Agent reasoning and response: ~5,000 tokens. Total: ~25,000 tokens. A 4K window: impossible. A 32K window: tight, risky. A 200K window: comfortable, with room for follow-up questions and refinement.

In 2026, the model-context trade-off is clear. Claude 3.5 Sonnet offers 200K tokens at moderate cost. Claude 3.5 Opus (if available) offers 1M tokens for complex analysis. GPT-4o offers 128K–200K. Smaller models like Haiku or GPT-4o Mini offer 128K but process faster and cost less. For local SEO agents, the choice depends on workload: Single-client audits, review monitoring, or real-time queries: 128K–200K is sufficient. Multi-location audits, geogrid analysis, or competitive intelligence: 200K–500K recommended. Large-scale batch processing (100+ locations, years of history): 500K–1M. The larger the context, the slower the response and the higher the cost. Most local SEO workflows fit comfortably in 200K; only very data-dense tasks need 500K+.