On 7 August 2025, OpenAI launched GPT‑5 and removed GPT‑4o from the model picker. Existing conversations did not reset. A thread with hundreds of 4o‑authored turns simply received its next turn from a different model — across a product with roughly 700 million weekly users.
Nobody measured what that does. Seven months later, a team at NatWest AI Research and UCL built a benchmark for exactly this: a switch matrix, where one model writes the early turns and a different model writes the last one.
The rollout is our framing, not the paper's — the paper never mentions it. It is a useful illustration and not evidence for these results. The rollout changed the model, its personality, its rate limits and its routing all at once, with no counterfactual; the study below changes exactly one thing and holds the rest fixed. Treat the rollout as the reason to care, and the matrices as the actual finding.
A handoff is a structured distribution shift: the model writing the final turn is conditioning on a dialogue it did not write.
Rows are the prefix model that wrote the early turns. Columns are the suffix model that wrote the final one. Each cell is ΔA→B against that suffix model's own no-switch baseline — blue means the foreign prefix helped, red means it hurt. Hover any cell for the exact value, its significance and the underlying cell mean. The hatched diagonal is the baseline itself.
Suffix model — writes the final turn →
← scroll the grid sideways · row labels stay fixed
Three findings, in the paper's own terms.
One handoff is enough. Of the 72 off-diagonal switches, 16 on CoQA (22%) and 18 on Multi-IF (25%) are significant at the 95% level — from a single final turn written by a different model.
It is directional, not symmetric. A handoff that harms B after A may be neutral or beneficial in reverse. Handoff robustness is a property of the ordered pair and the dialogue regime the prefix establishes, not of model quality. Within-family switches among the GPT‑5 variants sit near zero on CoQA, which the authors read as shared conventions reducing mismatch.
It cuts both ways. The largest Multi-IF effects are positive: a stronger prefix can stabilise a compliant output regime and lift a weaker suffix. The failures look like behavioural anchoring — the suffix continuing, or failing to override, a protocol the prefix set — rather than lost capability.
The ten largest single-handoff effects in each benchmark. Positive means the foreign prefix improved the suffix model's final answer. These are the tails, not the typical case — see the note below the chart.
The paper shows drift is largely additive: ΔA→B ≈ μ + αA + βB, where α is prefix influence (how much a model's dialogue style helps or hurts whoever continues it) and β is suffix susceptibility (how much a model gains or loses when handed a foreign history). That compresses a 9×9 grid into 18 numbers you can monitor.
α · prefix influence — higher means this model's history helps whoever writes next
β · suffix susceptibility — positive means this model tends to gain from a foreign prefix; negative means it degrades
The paper publishes α and β as bar charts and states six β values in its text, but does not print the full set. We refit the paper's own model — equation 1, ordinary least squares on the 72 off-diagonal cells with sum-to-zero constraints — to the published Δ tables. The fit reproduces the paper's stated R² (we get ) and all six stated β values to within 0.0007. Rank order matches the paper's figures in three of four charts; the exception is CoQA β, where GPT‑5.2 and Gemini‑2.5‑Flash differ by less than 0.0001 and swap places — a rounding artefact of fitting on three-decimal published values rather than raw episode scores. Treat the ordering as sound and the last decimal as ours, not theirs.
The paper's operational recommendations.
Log the authoring model per turn, and monitor the first turn after a switch specifically — a handoff is a discrete change point, and averaging across a session hides it.
Run a handoff regression before upgrades or fallbacks. Replay historical prefixes through the candidate suffix model to estimate ΔA→B, and flag risky pairs or spot uplifts before they reach users.
Track α and β as a compressed view, then prioritise deeper evaluation for the pairs with large residuals. Gate routing when deviation from the no-switch baseline is large; the authors suggest injecting a short handoff instruction as a simple mitigation.
The paper measures rather than fixes. It names explicit handoff summaries, lightweight adapters and continuity-aware routing policies as future work, and does not test them.
This timeline is our assembly of public, dated artefacts. The connections drawn between them are ours.
Two independent routing projects converged on the same mitigation shape within months. But the vLLM Semantic Router issues do not cite this paper, and they were filed two and three days after it was posted — suggestive timing, no demonstrated causal link, and we are not claiming one. Formal academic uptake is meanwhile close to zero: OpenAlex records no citations and Google Scholar one, as of August 2026. The paper's visible afterlife so far is as engineering justification, not as a benchmark anyone has rerun.