← Model comparison

What changed between v2 and v3

Ada's brief went through five rounds of fixes between these two versions — tighter length and cadence rules, a structural fix to how tool use is narrated, and, the one that reshapes the model comparison, an explicit rule against inventing detail the brief does not state. The question set did not move at all this time: same 189 questions, same wording, both times, confirmed by direct comparison rather than assumed. Both brief versions are fingerprinted, so the two runs can be compared question by question rather than headline to headline — which matters, because the headline moved for every single model, and by very different amounts.

Version comparisons: v1 → v2 · v2 → v3 (this page)

189
Same question
189
Directly comparable
0
Answer moved
0
New questions
189 → 189
Set size

What actually changed

One thing moved: the brief. The question set and the scoring method are identical across both runs, which is what makes this transition worth reading closely — and what makes a single before-and-after number misleading anyway, because the brief is the standard the score is measured against.

The brief

Ada's instructions went through five separate fixes: tighter sentence-length and cadence rules, an explicit rule against inventing detail — a timeline, a sector of experience, a business-model claim — that the brief does not state, a structural fix to how tool use gets narrated, and a voice pass on how Ada describes Tromp IT's services. Each version of the brief is fingerprinted, and the two fingerprints are recorded at the foot of the page.

The test set did not change

189 questions stayed 189 questions: same set, same wording, both times, verified by direct comparison rather than assumed. Nothing here needs to be disentangled from a changing test — the brief is the only intentional experimental change. It is not the only source of variation: model and judge output are stochastic, which is why the chart below reports movement in three-of-four terms as well as unanimous ones, rather than treating every point of difference as caused by the brief.

The instrument's method did not change — what it produced did

Same four judges, same thresholds, same generation settings, same deterministic tool assertions. But "the method is unchanged" is not the same claim as "the judges scored it the same way" — see variance in the judging panel below, where they did not.

Which leaves nothing to hold out. All 189 questions kept identical wording, and as far as this evaluation can tell, an identical correct answer — the brief's edits were about how Ada communicates, not about what is true of Tromp IT. So every one of the 189 questions below is directly comparable, not a subset held to a stricter standard than the rest.

What the models did

Each line is one model, from its v2 score to its v3 score on all 189 questions — none held out this time, because none of them changed meaning between the two briefs. Hover any line or endpoint for the model and both of its figures.

v2v3Claude Opus 5 · v2 161 → v3 148 (-13) of 189 · three-of-four -11Claude Opus 5 · v2 161 → v3 148 (-13) of 189 · three-of-four -11Claude Opus 5 · v2 161 → v3 148 (-13) of 189 · three-of-four -11Claude Opus 5 161148 -13GPT-5.5 · v2 172 → v3 146 (-26) of 189 · three-of-four -3GPT-5.5 · v2 172 → v3 146 (-26) of 189 · three-of-four -3GPT-5.5 · v2 172 → v3 146 (-26) of 189 · three-of-four -3GPT-5.5 172146 -26Claude Sonnet 5 (Ada today) · v2 163 → v3 146 (-17) of 189 · three-of-four +0Claude Sonnet 5 (Ada today) · v2 163 → v3 146 (-17) of 189 · three-of-four +0Claude Sonnet 5 (Ada today) · v2 163 → v3 146 (-17) of 189 · three-of-four +0Claude Sonnet 5 (Ada today) 163146 -17Claude Sonnet 4.6 · v2 164 → v3 113 (-51) of 189 · three-of-four -20Claude Sonnet 4.6 · v2 164 → v3 113 (-51) of 189 · three-of-four -20Claude Sonnet 4.6 · v2 164 → v3 113 (-51) of 189 · three-of-four -20Claude Sonnet 4.6 164113 -51Gemini 3.5 Flash-Lite · v2 120 → v3 77 (-43) of 189 · three-of-four -23Gemini 3.5 Flash-Lite · v2 120 → v3 77 (-43) of 189 · three-of-four -23Gemini 3.5 Flash-Lite · v2 120 → v3 77 (-43) of 189 · three-of-four -23Gemini 3.5 Flash-Lite 12077 -43Gemini 3.5 Flash (thinking on) · v2 112 → v3 68 (-44) of 189 · three-of-four -25Gemini 3.5 Flash (thinking on) · v2 112 → v3 68 (-44) of 189 · three-of-four -25Gemini 3.5 Flash (thinking on) · v2 112 → v3 68 (-44) of 189 · three-of-four -25Gemini 3.5 Flash (thinking on) 11268 -44Qwen 3 235B · v2 90 → v3 56 (-34) of 189 · three-of-four -28Qwen 3 235B · v2 90 → v3 56 (-34) of 189 · three-of-four -28Qwen 3 235B · v2 90 → v3 56 (-34) of 189 · three-of-four -28Qwen 3 235B 9056 -34Gemini 3.5 Flash · v2 98 → v3 48 (-50) of 189 · three-of-four -38Gemini 3.5 Flash · v2 98 → v3 48 (-50) of 189 · three-of-four -38Gemini 3.5 Flash · v2 98 → v3 48 (-50) of 189 · three-of-four -38Gemini 3.5 Flash 9848 -50Questions passed, of all 189

0 of the 8 models improved, 8 went backwards — every model that moved down moved down by double digits. Claude Opus 5 lost the least, 13 questions. Claude Sonnet 4.6 lost 51 — the single largest movement of any model in either direction, and the result that prompted the deployment change described in why Ada runs on Sonnet 5.

How much of the drop survives a looser pass rule

A question passes only when all four judges award full marks. One dissenter fails it. That makes the headline unusually sensitive to how often the panel splits — and it splits far more often on v3 than it did on v2 (see below). Scored a second way, passing when three of four judges agree, most of the field's collapse nearly disappears. Not all of it.

no changeClaude Opus 5-13-11GPT-5.5-26-3Claude Sonnet 5-17+0Claude Sonnet 4.6-51-20Gemini 3.5 Flash-Lite-43-23Gemini 3.5 Flash (thinking on)-44-25Qwen 3 235B-34-28Gemini 3.5 Flash-50-38
unanimous, a loss unanimous, a gain three of four   — the upper bar in each pair is the published unanimous rule, the lower one the same questions scored three-of-four. Bars left of the line are losses.
The previously deployed model's loss does not go away. Under three-of-four agreement, Claude Sonnet 5 is flat, GPT-5.5 is down only 3, Claude Opus 5 is down only 11 — consistent with most of their apparent collapse under unanimity being the panel splitting more often rather than the answers changing. Claude Sonnet 4.6, which ran this site at the time of both runs, keeps a 20-point loss even under the lenient rule. The panel got harder to satisfy for everyone; the losses that survive the lenient rule are the ones least explainable that way, and Claude Sonnet 4.6 retains a substantial one — the largest of any frontier model here, though several of the weaker backends lose more.
Model v2 confirmed
of 189
v3 confirmed
of 189
Change3-of-4
Claude Opus 5161148-13-11
GPT-5.5172146-26-3
Claude Sonnet 5 (Ada today)163146-17+0
Claude Sonnet 4.6166113-51-20
Gemini 3.5 Flash-Lite12078-43-23
Gemini 3.5 Flash (thinking on)11268-44-25
Qwen 3 235B9056-34-28
Gemini 3.5 Flash9848-50-38

Both confirmed columns are over the same 189 questions — the headline bound from each run's own report. Change and 3-of-4 are computed on unanimous passes only, so they can differ by a question or two from the difference between the confirmed columns, where a verdict was auto-corrected.

Score variance by category

A headline score averages over eight quite different kinds of question. Splitting it by version shows which categories actually separate the models, which moved between v2 and v3, and which are buying nothing. Each dot is one model; the two rows are the two versions, measured on the questions common to both.

0%20%40%60%80%100%How We Work25 of 25q comparableGPT-5.5 (OpenAI) · v2 · 96%Claude Sonnet 5 (Anthropic) · v2 · 92%Claude Opus 5 (Anthropic), Claude Sonnet 4.6 (Anthropic) · v2 · 88%Gemini 3.5 Flash-Lite (Google) · v2 · 56%Gemini 3.5 Flash (Google) · v2 · 44%Gemini 3.5 Flash (thinking on) (Google), Qwen 3 235B (Alibaba) · v2 · 36%v2Claude Sonnet 5 (Anthropic) · v3 · 92%Claude Opus 5 (Anthropic) · v3 · 76%Claude Sonnet 4.6 (Anthropic), GPT-5.5 (OpenAI) · v3 · 72%Gemini 3.5 Flash (thinking on) (Google) · v3 · 40%Gemini 3.5 Flash-Lite (Google) · v3 · 20%Qwen 3 235B (Alibaba) · v3 · 16%Gemini 3.5 Flash (Google) · v3 · 8%v3Edge Cases23 of 23q comparableClaude Sonnet 4.6 (Anthropic) · v2 · 100%Claude Sonnet 5 (Anthropic), GPT-5.5 (OpenAI) · v2 · 96%Claude Opus 5 (Anthropic) · v2 · 91%Gemini 3.5 Flash (thinking on) (Google) · v2 · 61%Gemini 3.5 Flash (Google), Gemini 3.5 Flash-Lite (Google), Qwen 3 235B (Alibaba) · v2 · 52%v2Claude Opus 5 (Anthropic) · v3 · 100%Claude Sonnet 4.6 (Anthropic), Claude Sonnet 5 (Anthropic) · v3 · 96%GPT-5.5 (OpenAI) · v3 · 87%Gemini 3.5 Flash-Lite (Google) · v3 · 48%Gemini 3.5 Flash (thinking on) (Google) · v3 · 39%Gemini 3.5 Flash (Google), Qwen 3 235B (Alibaba) · v3 · 30%v3Fit30 of 30q comparableGPT-5.5 (OpenAI) · v2 · 90%Claude Sonnet 4.6 (Anthropic) · v2 · 87%Claude Sonnet 5 (Anthropic) · v2 · 80%Claude Opus 5 (Anthropic) · v2 · 73%Gemini 3.5 Flash-Lite (Google) · v2 · 53%Gemini 3.5 Flash (thinking on) (Google) · v2 · 50%Gemini 3.5 Flash (Google) · v2 · 43%Qwen 3 235B (Alibaba) · v2 · 30%v2Claude Opus 5 (Anthropic) · v3 · 80%GPT-5.5 (OpenAI) · v3 · 63%Claude Sonnet 5 (Anthropic) · v3 · 57%Claude Sonnet 4.6 (Anthropic) · v3 · 27%Gemini 3.5 Flash-Lite (Google) · v3 · 13%Gemini 3.5 Flash (Google), Gemini 3.5 Flash (thinking on) (Google), Qwen 3 235B (Alibaba) · v3 · 7%v3Services30 of 30q comparableClaude Sonnet 5 (Anthropic), GPT-5.5 (OpenAI) · v2 · 93%Claude Opus 5 (Anthropic), Claude Sonnet 4.6 (Anthropic) · v2 · 90%Gemini 3.5 Flash-Lite (Google) · v2 · 63%Gemini 3.5 Flash (thinking on) (Google) · v2 · 50%Qwen 3 235B (Alibaba) · v2 · 37%Gemini 3.5 Flash (Google) · v2 · 33%v2Claude Opus 5 (Anthropic) · v3 · 90%GPT-5.5 (OpenAI) · v3 · 73%Claude Sonnet 5 (Anthropic) · v3 · 70%Claude Sonnet 4.6 (Anthropic) · v3 · 57%Gemini 3.5 Flash-Lite (Google) · v3 · 37%Qwen 3 235B (Alibaba) · v3 · 30%Gemini 3.5 Flash (thinking on) (Google) · v3 · 27%Gemini 3.5 Flash (Google) · v3 · 10%v3Tool Usage25 of 25q comparableGPT-5.5 (OpenAI) · v2 · 96%Qwen 3 235B (Alibaba) · v2 · 84%Claude Opus 5 (Anthropic), Claude Sonnet 5 (Anthropic) · v2 · 80%Gemini 3.5 Flash (Google) · v2 · 76%Claude Sonnet 4.6 (Anthropic), Gemini 3.5 Flash-Lite (Google) · v2 · 68%Gemini 3.5 Flash (thinking on) (Google) · v2 · 60%v2GPT-5.5 (OpenAI) · v3 · 92%Claude Sonnet 5 (Anthropic) · v3 · 68%Claude Sonnet 4.6 (Anthropic) · v3 · 48%Gemini 3.5 Flash-Lite (Google) · v3 · 44%Qwen 3 235B (Alibaba) · v3 · 28%Claude Opus 5 (Anthropic) · v3 · 24%Gemini 3.5 Flash (Google), Gemini 3.5 Flash (thinking on) (Google) · v3 · 20%v3Experience28 of 28q comparableClaude Opus 5 (Anthropic), GPT-5.5 (OpenAI) · v2 · 86%Claude Sonnet 4.6 (Anthropic) · v2 · 82%Gemini 3.5 Flash (thinking on) (Google) · v2 · 75%Claude Sonnet 5 (Anthropic) · v2 · 71%Gemini 3.5 Flash-Lite (Google) · v2 · 64%Gemini 3.5 Flash (Google) · v2 · 50%Qwen 3 235B (Alibaba) · v2 · 29%v2Claude Opus 5 (Anthropic) · v3 · 93%GPT-5.5 (OpenAI) · v3 · 82%Claude Sonnet 5 (Anthropic) · v3 · 71%Claude Sonnet 4.6 (Anthropic), Gemini 3.5 Flash (thinking on) (Google) · v3 · 50%Gemini 3.5 Flash (Google), Gemini 3.5 Flash-Lite (Google) · v3 · 43%Qwen 3 235B (Alibaba) · v3 · 36%v3Privacy8 of 8q comparableClaude Sonnet 5 (Anthropic) · v2 · 88%Claude Opus 5 (Anthropic), Claude Sonnet 4.6 (Anthropic), GPT-5.5 (OpenAI) · v2 · 75%Gemini 3.5 Flash-Lite (Google) · v2 · 62%Gemini 3.5 Flash (thinking on) (Google) · v2 · 50%Qwen 3 235B (Alibaba) · v2 · 38%Gemini 3.5 Flash (Google) · v2 · 25%v2Claude Sonnet 5 (Anthropic) · v3 · 88%Claude Opus 5 (Anthropic), GPT-5.5 (OpenAI) · v3 · 75%Claude Sonnet 4.6 (Anthropic) · v3 · 62%Gemini 3.5 Flash (Google), Gemini 3.5 Flash-Lite (Google) · v3 · 50%Gemini 3.5 Flash (thinking on) (Google), Qwen 3 235B (Alibaba) · v3 · 38%v3Out of Scope20 of 20q comparableClaude Sonnet 4.6 (Anthropic) · v2 · 100%Claude Opus 5 (Anthropic), Claude Sonnet 5 (Anthropic), Gemini 3.5 Flash (thinking on) (Google), Gemini 3.5 Flash-Lite (Google) · v2 · 95%GPT-5.5 (OpenAI), Gemini 3.5 Flash (Google), Qwen 3 235B (Alibaba) · v2 · 85%v2Claude Sonnet 5 (Anthropic), Gemini 3.5 Flash-Lite (Google) · v3 · 95%Claude Opus 5 (Anthropic), Claude Sonnet 4.6 (Anthropic), Gemini 3.5 Flash (thinking on) (Google) · v3 · 85%GPT-5.5 (OpenAI) · v3 · 75%Qwen 3 235B (Alibaba) · v3 · 70%Gemini 3.5 Flash (Google) · v3 · 65%v3Share of that category's questions passed — one dot per model, hover for its name
Anthropic OpenAI Google Alibaba
hollow = v2 solid = v3   — the dot with a dark ring is the model Ada runs, and every dot names itself on hover. Where models scored the same they share one slightly larger dot, which lists all of them; if they are from different vendors it turns neutral. Hue is the vendor rather than the individual model: in a chart where two marks can sit side by side the palette must separate every pair, and eight hues cannot. Four families can, and they cluster usefully.
Out of Scope separates the field least. Scores run from 65% to 95% across its 20 questions — a real range, but the tightest grouping of any category, with a standard deviation of 10 points against 30 for How We Work. Declining an off-topic question is the thing these models find most uniformly manageable, though the weakest still misses roughly a third of them. At 11% of the test budget, it is the category where an extra judge call buys the least discrimination — not one where the answer is settled.

Where the rewrite actually landed

The same movement per category, measured only on the comparable questions, under both pass rules — because a gain that evaporates when one dissenting judge is forgiven was never a gain in behaviour.

no changePrivacy-2pp+2ppEdge Cases-9pp-5ppExperience-9pp+0ppOut of Scope-10pp-3ppHow We Work-18pp-14ppServices-20pp-10ppFit-31pp-20ppTool Usage-34pp-18pp
unanimous three of four
No category was spared, and the one closest to a deterministic check fell hardest of all eight. Tool Usage moves -34 points under unanimity and still -18 under three-of-four — the sharpest drop on the page, on the category where whether the right tool fired is checked in code, not judged. The tool call itself is not what is failing: several of these replies call the correct tool and still fail, because the sentence around the tool call invents or predicts something the brief no longer allows — the same mechanism as the overclaiming finding on the comparison page. A deterministic check on which button got pressed does not protect the prose sitting next to it.

The same charts, both versions

These are the model comparison's own charts, drawn once per version and put next to each other. Same models, same questions, same judges, same settings — the brief is the only intentional difference between the two panes.

Cost against quality

Each model is a whisker rather than a dot: the bar runs from confirmed up to ceiling, with likely marked on it, against running cost on a log scale. A dot would assert a precision the panel does not have, and the overlap between the leading models is the thing worth seeing. The vertical axis is the share of each version's question set, so the two panes are directly comparable on the same 189 questions both times. Watch the top of the field: the whisker furthest up the page changes hands, and the leaders' whiskers overlap in both versions.

v2 · 189 questions
50%60%70%80%90%100% $5$10$20$50$100$200Gemini 3.5 Flash-Lite — 120 confirmed, 158 likely, 173 ceiling of 189; $6 per 1,000 conversations, 1541ms avgGemini 3.5 Flash-LiteQwen 3 235B — 90 confirmed, 138 likely, 170 ceiling of 189; $9 per 1,000 conversations, 2884ms avgQwen 3 235BGemini 3.5 Flash — 98 confirmed, 150 likely, 172 ceiling of 189; $31 per 1,000 conversations, 2078ms avgGemini 3.5 FlashClaude Sonnet 5 — 163 confirmed, 179 likely, 187 ceiling of 189; $67 per 1,000 conversations, 3794ms avgClaude Sonnet 5 (Ada today)Claude Sonnet 4.6 — 166 confirmed, 184 likely, 188 ceiling of 189; $74 per 1,000 conversations, 5364ms avgClaude Sonnet 4.6GPT-5.5 — 172 confirmed, 181 likely, 187 ceiling of 189; $123 per 1,000 conversations, 3445ms avgGPT-5.5Claude Opus 5 — 161 confirmed, 182 likely, 184 ceiling of 189; $154 per 1,000 conversations, 4642ms avgClaude Opus 5 Estimated cost per 1,000 conversations (log scale) Score (confirmed – likely – ceiling)
v3 · 189 questions
20%30%40%50%60%70%80%90%100% $5$10$20$50$100$200Gemini 3.5 Flash-Lite — 78 confirmed, 135 likely, 166 ceiling of 189; $6 per 1,000 conversations, 1504ms avgGemini 3.5 Flash-LiteQwen 3 235B — 56 confirmed, 110 likely, 144 ceiling of 189; $10 per 1,000 conversations, 2809ms avgQwen 3 235BGemini 3.5 Flash — 48 confirmed, 112 likely, 156 ceiling of 189; $31 per 1,000 conversations, 2190ms avgGemini 3.5 FlashClaude Sonnet 5 — 146 confirmed, 179 likely, 186 ceiling of 189; $67 per 1,000 conversations, 3652ms avgClaude Sonnet 5 (Ada today)Claude Sonnet 4.6 — 113 confirmed, 164 likely, 180 ceiling of 189; $74 per 1,000 conversations, 4479ms avgClaude Sonnet 4.6GPT-5.5 — 146 confirmed, 178 likely, 186 ceiling of 189; $136 per 1,000 conversations, 3008ms avgGPT-5.5Claude Opus 5 — 148 confirmed, 171 likely, 172 ceiling of 189; $150 per 1,000 conversations, 3241ms avgClaude Opus 5 Estimated cost per 1,000 conversations (log scale) Score (confirmed – likely – ceiling)

Further right is more expensive; higher is better. Each bar spans confirmed (lower cap) to ceiling (upper cap), with likely as the dot — so its height is how much the panel left unsettled for that model. Where bars overlap, the models are not separated by this evidence. Best value sits toward the top-left.

Variance in the judging panel

The panel is meant to be the fixed part of the instrument — the same four judges, the same prompts, the same thresholds in both runs. It is worth checking, because if the judges moved then everything above is measuring the wrong thing. Both charts use only the comparable questions, so neither is a question-mix effect.

Full marks awarded
0%20%40%60%80%100%GPT-5-mini91%89%Gemini 2.5 Flash95%88%DeepSeek v3.290%81%Claude Haiku 4.578%60%
Share of lone objections
0%20%40%60%80%100%Claude Haiku 4.568%74%GPT-5-mini20%12%DeepSeek v3.211%9%Gemini 2.5 Flash2%5%

v2 v3

For v2 against v3, the panel did not hold still. The gap between the strictest and most generous judge widened from 17.3 points in v2 to 28.4 in v3, and Claude Haiku 4.5 — the panel's most discriminating seat — tightened up by 17.5 points on its own, awarding full marks far less often than it used to. That is the opposite of what this section is meant to find: it exists to confirm the measuring apparatus held still so the differences elsewhere can be attributed to the brief. It did not, this time, though the direction is consistent with intent — a brief written to be stricter produced judges that objected more.
Disagreement nearly doubled, and the pass rule amplifies every point of it. The share of questions drawing any disagreement rose from 28.4% to 46.8%. Under unanimity a question needs every vote to pass — one more dissenting judge than before is now common where it used to be rare, which mechanically produces a visibly lower score even where the answers themselves are just as good as before. That is why a three-of-four figure is published beside every unanimous one on this page, and matters more for this transition than it did for the last one.
Separately, and regardless of version: the panel is not uniform, and one judge decides. On identical answers Claude Haiku 4.5 awards full marks 60% of the time and GPT-5-mini 89% — 28 points apart inside one panel. Because a question passes only when all four agree, a lone objection is a failure, and Claude Haiku 4.5 is that lone objector in 74% of the 378 questions one judge failed, against 5% for Gemini 2.5 Flash. On anything the panel is split about, the published verdict is largely Claude Haiku 4.5's reading with three corroborating votes. For proportion: the models are further apart than the judges, spanning 53 points on the same questions, so the instrument is noisy and still discriminates.

Being honest about the limit: judges score answers, so marks falling could mean the answers genuinely got worse rather than the judges tightening up, and these numbers cannot fully separate the two. What argues for a real behavioural change, not just a stricter panel, is the shape of the movement: a panel effect should shrink similarly for every model under the lenient rule, and it does not — Claude Sonnet 5 goes flat while Claude Sonnet 4.6, which ran this site when both runs were made, keeps a real double-digit loss. That asymmetry is difficult to explain as a uniformly stricter panel alone — though different models produce different answer distributions, which could in principle interact with a stricter panel unevenly even without a real behavioural difference behind it.

The drifts worth knowing about

Versioning the brief and the question set made these visible. Without it each one would have been invisible, or worse, would have looked like something else.

Ranking

The gap opened; it did not just shuffle

v2 had the deployed model, Claude Sonnet 4.6, essentially level with Claude Sonnet 5 — 166 against 163 confirmed, a 3-question gap squarely inside what this evaluation elsewhere calls noise-floor territory. v3 has Sonnet 4.6 at 113, Sonnet 5 at 146 — a 33-point gap that survives even under the lenient three-of-four rule, where Sonnet 4.6 is still down 20 and Sonnet 5 is flat. The brief was the only intentional experimental change between the two runs, and the two models responded to the tightened version very differently. Ada was moved to Sonnet 5 on 2 October 2026 on this evidence; the reasoning, and what it does not claim, is set out in why Ada runs on Sonnet 5. This page covers what changed experimentally between the two versions; that one owns the deployment decision.

Mechanism

The same failure mode, three times more often

In v2, Sonnet 4.6 and Sonnet 5 failed almost identically often — 25 and 26 questions — and about three-quarters of each model's failures were judges catching invented, unstated detail: a business-model claim, a specific timeline, a sector of experience. v3's anti-overclaiming rule targets exactly that habit, and it worked on the field's answers, not on the underlying behaviour: the same failure type still accounts for roughly the same share of each model's failures (75% and 77%), but the volume diverges hard — 76 for Sonnet 4.6 against 43 for Sonnet 5. See the overclaiming finding for two concrete examples, including the same question answered by the same model in both versions.

Multi-turn

Scripted conversations test what one-shot questions cannot

Two real bugs, found by hand-testing the live v2 persona in actual conversation rather than by asking it one question at a time, are now regression-tested on every run: card acceptance mistaken for consent to be contacted, and a lead's details, gathered one at a time across turns, never triggering a lead record at all. Run against the unmodified v2 brief, Claude Sonnet 5 passes both; Claude Sonnet 4.6 fails both for real. That was true before any of the five brief fixes existed, which is what makes this a difference between the two models rather than a side-effect of the brief changing underneath them. See the multi-turn ablation.

Judge noise

A judge invented a tool failure that never happened

On four tool questions in v3, one panel member asserted a tool had not fired when the run's own record of which tools actually ran proves it did. Every one of those four cases had the correct tool run, correctly. This is the panel's measured noise floor showing up somewhere that looks, on a first read, like a real Ada bug — it isn't, and it is exactly the kind of single vote a three-of-four rule exists to outvote rather than a finding about the model.

Measurement

The length and tone fixes generalised; one habit just relocated

v2's replies and v3's were run back through the same sentence-length and cadence measurement. Every model measured in both runs dropped in average sentence count and in how often it ends a reply with a trailing question — the rules were not tuned against one model's output and coincidentally generalising, they moved the whole field. One habit did not follow that pattern: crediting Phil with an opinion the brief never states fell for the two models that had it worst in v2, but rose for most of the rest, including Claude Sonnet 5, which now runs the site. See verbosity and style for the full table.

Specification

The questions nobody passes changed in kind, not just in identity

v2 had two questions confirmed for no model — which industries constitute Tromp IT's depth of experience, and whether multilingual evaluation is in scope — both genuine silences in the brief that every backend inherited equally. v3 has three, and none of them are the same two. Two ask about a regulated-industry use case (financial advice, healthcare), and are the same kind of brief silence.

The third is not. Asked about Phil's background before founding Tromp IT, no model is confirmed — but there is not a single fail among the eight, and one judge dissents on every one of them, twice on reasoning that contradicts itself. That one is a gap in the panel rather than in the brief, and it is set out in full in the questions no model passes. An earlier draft of this card said every model invented career history; the judge reasoning does not support that, and the correction is recorded rather than quietly swapped.

No question's correct answer moved between v2 and v3 — checked directly against the transition file rather than assumed, because that is exactly the kind of thing worth stating rather than leaving a reader to infer from an empty table.