OpenAI quietly cut GPT-5.6's usable context to 272k tokens, a quarter of what it advertises, and developers are calling it a 'severe regression'

OpenAI Advertises 1.05 Million Tokens for GPT-5.6. Codex Delivers 272,000.

GPT-5.6 Sol lists a 1,050,000-token context window on OpenAI's official model page 1. Inside Codex, OpenAI's coding tool, the same model now serves 272,000 raw tokens. After Codex applies its own 95-percent effective-context policy, the usable window falls to 258,400 tokens, about a quarter of the advertised spec 2.

The change shipped without an announcement. A pull request merged into Codex's stable 0.144 release line on July 18 updated GPT-5.6 model metadata, including context-window settings, for the next hotfix 3. The PR description discussed catalog synchronization and test validation. It did not mention a capacity reduction 3. Developers first detected the server-side catalog change on July 13, when the model's context_window value dropped from 372,000 to 272,000 in the runtime data Codex fetches from OpenAI's servers 2. The PR merged five days later, propagating the updated metadata to the stable client 3.

This is the second restriction imposed on GPT-5.6 within Codex. A prior GitHub issue documented the initial cap at 372,000 raw tokens, or 353,400 effective, already well below the 1.05-million specification 2. The current cut removes another 100,000 raw tokens. At the 95-percent effective rate, usable capacity dropped from 353,400 to 258,400, a 26.9-percent reduction 2.

The developer who surfaced the regression, posting under the handle Lady-Lin, titled the GitHub issue "SEVERE REGRESSION" and attached sanitized runtime records documenting both cuts 2. The complaint: paying subscribers discovered the change through disrupted workflows, not through release notes or documentation. Long-running coding sessions that previously fit within the window now trigger auto-compaction earlier, forcing the model to summarize and discard context it was actively using 2. Repository-scale analysis, multi-agent coordination, and large specification reviews all depend on stable context capacity. All are disrupted when that capacity shrinks without warning 2.

OpenAI's own model page adds a pricing dimension. API requests exceeding 272,000 input tokens are priced at twice the standard input rate and 1.5 times the standard output rate, applied to the entire request 1. Codex caps the raw context at exactly that 272,000 threshold 2. Subscribers using Codex cannot reach the long-context tier that the API makes available at premium rates 2. The capacity exists in the model specification and carries a price in the API. Through Codex, it is blocked entirely.

Competitors sit at or above the million-token mark. Google documents Gemini models with 1-million-token context windows 4. Anthropic offers a 1-million-token option for Claude Sonnet 4 5. xAI lists 1 million tokens for Grok 4.3 6. All three are specified at roughly the same tier as GPT-5.6's headline figure.

On Hacker News, commenters described auto-compaction loops that summarize context prematurely, forcing models to re-read entire codebases mid-session 7. One developer described the cycle of loading a large project, hitting the context limit at around 15 percent remaining, auto-compacting, and starting over from scratch, consuming subscription quota on repeated reads 7. Another wrote about uninstalling Codex and migrating to Anthropic's Claude Code after the context cuts repeatedly disrupted work 7. A parallel discussion on Reddit's r/codex drew similar accounts from developers hitting the same wall 8.

For developers building on GPT-5.6, context capacity is an economic variable, not a technical footnote. It determines how much code a model can reason over in a single pass, how often compaction interrupts work, and what it costs to sustain a complex task. An architecture designed around a million-token window behaves fundamentally differently when the working budget shrinks to 258,400 tokens. Context limits shape product design, cost projections, and engineering decisions at every layer.

The reductions surfaced through token meters, compaction failures, and faster quota burn. The developers who noticed were the ones already paying.