Topic
Context Window & Context Length
10 posts on context window & context length — part of glossary on the n4n AI blog.
What is a context window in large language models?
A precise technical definition of context windows in LLMs, covering token limits, attention mechanics, practical implications for engineers, and common misconceptions.
How much text fits in a 128k token context window?
A practical guide to estimating how much text fits in a 128k token context window, with runnable code for measuring and verifying token counts across models.
How context window size shapes RAG system design
How context window size drives chunking strategy, retrieval depth, and prompt architecture in production RAG systems.
How context window size affects response quality
Analysis of how context window size impacts LLM response quality, covering failure modes, diminishing returns, and practical strategies for engineers.
GPT-4o vs Claude Sonnet 4.5 vs Gemini 1.5 Pro: context sizes
Technical comparison of GPT-4o, Claude Sonnet 4.5, and Gemini 1.5 Pro context windows with pricing, latency, and real-world tradeoffs for engineers.
Gemini 1.5 Pro's 1 million token context window, explained
Gemini 1.5 Pro's 1 million token context window explained — how it works, why it matters, and what engineers get wrong about long-context LLMs.
Context window vs memory: what's the difference?
Understand the difference between context window and memory in LLMs — what each does, how they interact, and when to use which approach for your application.
Context window sizes by model: a 2026 comparison
A practical 2026 reference for context window size by model across OpenAI, Anthropic, Google, Meta, and open weights — with token limits, pricing context, and routing guidance.
Context rot: why models lose track in long prompts
Why models lose track in long prompts — attention dilution, positional decay, and practical mitigations for engineers building with large context windows.
Claude Sonnet 4.5's 200k token context window, explained
A technical breakdown of Claude Sonnet 4.5's 200k token context window — what it means, how it works, and what engineers get wrong about it.
More topics in glossary
- Structured Outputs & JSON Mode19
- AI Agents Fundamentals12
- Hallucination in LLMs11
- Sampling Parameters: Top-p, Top-k & Penalties11
- Fine-Tuning Fundamentals9
- Foundation Models: Base vs Instruct vs Chat9
- Model Families & Naming Conventions: GPT-5, Claude, Gemini 3, Llama 4, Mistral, DeepSeek, Qwen, Grok9
- Grounding & Fact-Checking in AI8
- LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88
- Chain-of-Thought Prompting7