ChatGPT token limits in 2025 vs 2026: how context windows changed from GPT-5 to GPT-5.6
Updated: Sep 16
Context-window tables from 2025 are now historical material. The GPT-5 era established large-context workflows, but the 2026 GPT-5.6 family substantially changed the published API ceiling and the relationship between model capacity, reasoning effort, and product access. This page preserves the 2025 comparison while clearly separating historical limits from current specifications.
WHY THE 2025 TABLES AGED QUICKLY
Model names, retirement schedules, plan access, and context specifications changed repeatedly between 2025 and 2026. A number printed beside GPT-5 or another 2025 model can still describe that historical release, but it should not be presented as the current ChatGPT limit.
The correct question is now whether the reader needs a historical GPT-5 specification, a current GPT-5.6 API window, or the practical context available inside the ChatGPT product.
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THE 2026 GPT-5.6 BASELINE
OpenAI's current API documentation lists GPT-5.6 Sol and GPT-5.6 Terra with 1,050,000 tokens of context and maximum output of 128,000 tokens.
Those figures establish the current API model ceiling for those variants, while ChatGPT can apply separate product constraints and does not automatically expose the raw API maximum in every plan or workflow.
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MODEL CONTEXT AND CHATGPT ACCESS DIVERGED
By 2026 the ChatGPT product uses plan-specific models and reasoning controls. GPT-5.6 Sol powers Instant and higher reasoning levels on eligible paid plans, while Free and Go use GPT-5.6 Luna.
A historical context table that ignores the selected product, plan, model, tool, and reasoning path can therefore mislead even when its old numbers were once accurate.
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OUTPUT LIMITS MATTER TOO
Long-context analysis needs room for the answer. Current GPT-5.6 Sol and Terra API documentation allows up to 128,000 output tokens, but actual useful output is usually far smaller because latency, cost, readability, and task structure become limiting first.
A model's maximum output should be treated as an engineering boundary rather than a recommended response length.
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DATA STUDIOS NORMALIZATION
If a workflow consumes 800,000 tokens of a 1,050,000-token window, the remaining theoretical headroom is 250,000 tokens, or about 23.8% of the window. That remainder must absorb instructions, system context, retrieval, and output.
This calculation illustrates why published context size is not equivalent to usable source-document capacity.
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WHEN HISTORICAL GPT-5 LIMITS ARE STILL USEFUL
Historical limits matter when reproducing an older benchmark, understanding why a 2025 conversation behaved differently, or maintaining software pinned to an older API model.
For a current implementation, however, use the live model documentation and current ChatGPT plan behavior rather than copying an archived table.
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HOW TO READ CONTEXT TABLES IN 2026
Always label the model, product, date, input/output distinction, and whether the number is a published API specification or an observed ChatGPT behavior.
Without those five dimensions, a context-window number can be technically true and still operationally useless.
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