Introduction
When choosing a large language model (LLM) for your project, the context window size often determines whether it can handle your use case effectively. Models with larger windows can retain more conversation history, work with longer source documents, and manage complex multi-part prompts.
Understanding Context Windows
A context window is the maximum number of tokens a model can process in a single request. Tokens roughly map to words or word fragments. Larger windows mean:
- More complete document ingestion
- Ability to reference larger histories in conversation
- Handling multi-document or multi-file processing
Implications of Larger Windows:
- Reduced need for truncation
- Higher computational demands
- Potentially slower responses for max-size inputs
Model Comparisons
GPT-5 Family
GPT-5 offers a 200,000-token window, suited for long documents, full-length books, or integrated datasets. Ideal for research assistants or advanced data QA.
GPT-5 Codex shares the same 200,000-token limit but with capabilities optimized for code-heavy tasks: repo search, code migration, and annotated reviews.
Claude-4 Variants
Claude Sonnet 4 and Claude Sonnet 4.5 each offer 200,000 tokens, great for natural language-heavy projects with extended dialogues. The Claude Haiku 4.5 variant matches the 200,000 tokens but runs faster for iterative reasoning tasks.
Gemini-2.5 Line
Gemini-2.5 Pro and Gemini-2.5 Flash stand out with 1,000,000-token context windows. These are built for massive ingestion tasks—multiple books, entire codebases, or extensive logs—without segmentation.
Pro focuses on advanced reasoning and integrative synthesis, while Flash optimizes for streaming and rapid retrieval in huge contexts.
GLM-4.6 and Related Models
GLM-4.6 hits 200,000 tokens, offering balanced speed and context length. This makes it effective for research assistance where coherence over long spans matters.
GLM-4.5 has 128,000 tokens—less capacity but potentially faster with modestly sized workloads.
Other Notable Models
- Grok Code Fast-1: 256,000 tokens; tuned for accelerated code tasks.
- Grok-4: 256,000 tokens; robust for multi-modal and narrative-heavy inputs.
- Qwen3 Max: 256,000 tokens; high context retention with efficiency.
- DeepSeek variants: ranging from 128,000 to 131,000 tokens; aimed at agile inference rather than extreme window sizes.
Wisdom Gate Context Table
Below is a comparative table listing models, their window sizes, and API link references.
Notes:
- Larger windows often mean higher pricing tiers
- Speed may drop with extreme context sizes
- API links provided allow direct exploration
Choosing the Right Model
By Workload Type
- Massive Content Integration: Gemini-2.5 Pro/Flash
- Long-form Dialogue & Research: GPT-5, Claude Sonnet 4 series
- High-speed Code Ops: Grok Code Fast-1, Qwen3 Max
- Balanced Reasoning: GLM-4.6
- Budget-sensitive, Agile Tasks: DeepSeek models, GLM-4.5
Considerations
- Measure average token length of your inputs
- Balance speed vs context needs
- Keep in mind API cost per token
Future Proofing
If you expect data sizes to grow, select models with larger contexts today to avoid migration overhead later.
Conclusion
Models vary significantly in context capacity—from 128k to 1M tokens. Choose based on your workload's size, complexity, and processing speed needs.