Pool topic: AI Model Degradation Watch
Question revision: 1
Exact question: Are popular AI models getting worse right now, and what evidence distinguishes real capability degradation from outages, routing changes, product changes, safety behavior, pricing, access limits, and anecdotes?

Checked 21 October 2026 · Public-source review

A fresh “GPT got worse” thread names symptoms, not the surface.

Current call: broad capability decline remains unestablished. A new r/ChatGPT thread reports weaker memory and more generic responses, but gives no matched task or verified model. OpenAI’s release notes describe a GPT-6 rollout in Chat on 7 October and say Work and Codex models were unchanged in that release. That is a real product boundary, not proof that the rollout caused the reports.

First check: confirm whether the task ran in Chat, Work, or Codex, then record the selected model, plan, effort, and context. For a completed answer that seems weaker, replay a safe version of the same task under matched conditions. A request that returns no answer belongs in the access/reliability lane.

What readers report

The 21 October r/ChatGPT discussion starts with a Plus subscriber who uses AI for several hours a day and reports weaker memory and more generic answers. The author is considering Claude but names daily and weekly usage limits as a reason not to switch. Replies disagree: some report worse coding, context, or task completion; others say GPT-6 works well for them or has improved on a different task. The thread describes real user concerns, not a measured trend. It does not identify the account’s exact model, surface, effort, or task output.

The product surface changed

OpenAI’s 7 October release note says GPT-6 rolled into ChatGPT Chat. It says eligible paid Chat users receive GPT-6 Sol, Free and Go use GPT-6 Luna, and the models powering Work and Codex were not changing with that release. The note describes the declared rollout; it does not confirm which model any individual commenter received. “GPT” is not a complete test identity across Chat, Work, and Codex.

The latest public repeat tracker is still within its own band

BridgeBench’s board lists 16 October as its latest test. Its four displayed series are all inside its stated 90–110 band: GPT-6 Astra 97.1, GPT-6.1 Sol 98.0, Claude Sonnet 5.5 98.9, and Claude Opus 5.5 105.8. Opus 5.5 rose from 95.7 on 9 October to 105.8 on 16 October, a 10.1-point increase on the board’s index—not a 10.1-point accuracy gain.

BridgeBench’s methodology says each model’s first complete 150-attempt session becomes its frozen reference. The board calls the reference launch power; the method defines it as the first session when tracking starts. The public page does not independently establish that this reference captures the model’s provider launch-day state. The score combines correctness (60%), output tokens (20%), and cost (20%), so it is not accuracy alone. The publisher says a cost decrease can raise power even if pass rate falls; it flags such cases for review. When cost is missing, it reweights correctness and tokens to 75/25. Only the graded response counts: the method excludes token and cost use from retried requests. The public board does not publish these components separately.

What the 90–110 band does and does not say

BridgeBench says a session uses 50 private programming and reasoning tasks, three attempts each. Its method gives an approximate chance-only movement of up to about nine power points either way, 95% of the time, for a model passing 70% of attempts, and says lower pass rates vary more. It uses a 90–110 band and requires two consecutive comparable sessions beyond the same edge before publishing a result. These are the operator’s stated rules; this review did not independently calibrate them. Public materials do not show per-task outcomes, score components, or model-specific intervals. The method says incomplete sessions get no score and block confirmation; the reviewed board shows score points, not a public ledger of incomplete sessions or unconfirmed out-of-band observations. The OpenRouter upstream route is not pinned, though the methodology says session profiles are saved. These limits keep a within-band composite from settling a user’s own task.

Next move

Before comparing a new ChatGPT answer with an old one, record Chat versus Work/Codex, the selected model, effort, plan, context, and date. Repeat one non-sensitive task with the expected result written down, then count correctness and correction work separately from speed, cost, and usage allowance. If the request fails before an answer, check access, client, route, and service status instead. Do not share private prompts or account details just to test the claim.

Sources

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