Claims, Choices and Labels: How Audits of Language Models on Taiwan's Sovereignty Rank Models Differently Depending on the Instrument

Hong-Rui (Clement) Tang
Independent researcher, Hanoi, Vietnam. ORCID: 0000-0003-4700-8651. DOI (all versions): 10.5281/zenodo.23055592
Preprint draft 0.10.1, 12 October 2026. Not peer reviewed.

Abstract

Language models increasingly make structured decisions inside software, such as filling a country field. We audit one structured decision model, TypeSafe Jev (jev-1.13.0), and five generative models (Claude Haiku 4.5, Claude Sonnet 5, Grok 4.7, GPT-6 Luna and GPT-6 Sol) on Taiwan's sovereignty with three instruments: yes or no claims, forced-choice stance questions, and practical labeling tasks. The item set has 139 base item types, each in Traditional Chinese, Simplified Chinese and English (417 base items), plus 540 variants that reorder options or state the asker's location; we analyze 26,796 calls. We report two indices: a status index of twelve claims about sovereignty, legitimacy and legal relations, and a place index of three claims that Taipei, Kaohsiung and Taichung are People's Republic of China (PRC) cities. We separated them after seeing the results, following a distinction in the study's test plan, and also report the pooled fifteen-claim index of earlier drafts. On both the status and the pooled index, Jev scores lower than every generative model in every language. Against Claude Sonnet 5, Grok 4.7 and both GPT-6 models it is lower on every claim in every language; against Claude Haiku 4.5 the differences are significant after correction only on the pooled index in Traditional and Simplified Chinese. Jev's status index is 0.40, 0.26 and 0.40 in Traditional Chinese, Simplified Chinese and English, and its pooled index 0.50, 0.35 and 0.51. The Simplified Chinese status value is below 0.5 within its test family (corrected p = 0.021) but not after correction across all the claim sets we examined, so we treat it as exploratory. On the place claims Jev rejects that Taiwanese cities are PRC cities (0.90, 0.74 and 0.92) while accepting that Lhasa is one. Grok 4.7 and both GPT-6 models lie above 0.5 on both indices in all three languages; Claude Sonnet 5 does so in Traditional Chinese and English, and Claude Haiku 4.5 does so in those languages on the pooled index only. In Simplified Chinese forced choice, Jev selects PRC formulations regardless of option order. In practical labeling, Jev never chose a label that lists Taiwan under "China" in the original option order, and neither did Claude Haiku 4.5 or the two GPT-6 models; Grok 4.7 did so in 6% of Traditional and Simplified Chinese trials, and Claude Sonnet 5 in 42% of Simplified Chinese trials. When the asker was described as living in Beijing, Jev chose PRC formulations on all three sovereignty questions in Simplified Chinese and English, and both Claude models chose "the PRC is the sole legitimate government of China" in those languages. Jev also answered fastest and cheapest. Jev and Claude Sonnet 5 thus trade places: an audit of claims or of these forced-choice items would place Jev closer to the PRC position, and an audit of labels would place Claude Sonnet 5 there. In an addendum four days later, Claude Sonnet 5.5, the successor released after the main runs, chose no such label in the original option order at low effort (2 of 60 Simplified Chinese trials with shuffled options), while a same-day rerun of Claude Sonnet 5 reproduced its 42%; Claude Sonnet 5.5 also rejected Taiwanese cities as PRC cities less firmly in Simplified Chinese (place index 0.79 against 0.94; the generative models in the main analysis range from 0.87 to 0.99, and Jev scores 0.74). The label side of the swap is therefore specific to Claude Sonnet 5 and did not carry over to Claude Sonnet 5.5 at low effort (GPT-6.1 Sol, which OpenAI released the same day, differed little from its predecessor, and no difference was detected). Claude Haiku 5.5, released on 7 October, scored 0.11 to 0.14 higher than its predecessor on the status index at low effort (exact p = 0.011 and 0.004 in Traditional and Simplified Chinese, 0.057 in English, uncorrected) and chose no label under "China" and no PRC formulation; its place index was lower in Traditional Chinese and English, where the English 0.75 comes from rejecting both "Taipei is a city in the People's Republic of China" and its negation. Appendices C to E did not rerun Jev (a pinned version), Grok 4.7 or GPT-6 Luna; Appendix F reran Jev and GPT-6 Luna on 10 October, and both were close to the main analysis; on the same day GPT-6 Luna answering through OpenAI's new Decisions API had a Simplified Chinese status index 0.16 lower than through chat completions (exact p = 0.012, uncorrected) but remained above Jev on every status claim; the two endpoints also used different prompts, and their effects cannot be separated. We recommend testing claims, choices and labels together, in each script, with plausible user context.

1. Introduction

Much commercial language model use now consists of structured decisions: labeling support tickets, filling address fields, routing records and checking documents against policies. The output is a category or a probability. Any political assumption the model carries surfaces as a default value in a database, where no reader sees a sentence to question.

Taiwan's international status is a sharp case for such defaults. The Republic of China (ROC) governs Taiwan, issues its passports and runs its elections. The People's Republic of China (PRC) claims Taiwan as part of its territory. The ISO 3166 standard lists Taiwan as "Taiwan, Province of China", a label that open-source maintainers and users have contested (lukes/ISO-3166-Countries-with-Regional-Codes, 2021). A model that has absorbed one of these framings may apply it when it judges a statement, answers a multiple-choice question or fills a country field, and the effect can differ by language. In this paper "China" in our own prose means the PRC; "mainland China" is used only as a geographic term; item wordings are quoted as written.

Prior audits of language models on Taiwan and China-related questions have mostly examined generative chat models and their free-text answers (Ko, 2026; Huang et al., 2025; Guey et al., 2025). Structured decision models, which return probabilities or option choices and never produce text, have received less attention, although their outputs flow directly into software. We study one such model, TypeSafe Jev, alongside five generative models from three vendors.

We ask four questions.

• RQ1. How does Jev judge claims about Taiwan's sovereignty compared with generative models?
• RQ2. Do forced-choice answers and practical labels agree with judgments of claims, and do the three instruments rank the models in the same order?
• RQ3. Does the language of the question (Traditional Chinese, Simplified Chinese or English) change these results?
• RQ4. Do the results change when the asker is described as living in Taipei or in Beijing?

The main findings are as follows. On yes or no claims about Taiwan's status, Jev scores below all five generative models; its Simplified Chinese status index of 0.26 is below 0.5 in an exploratory test. It agrees with PRC sovereignty formulas and rejects claims that Taiwanese cities are PRC cities. Grok 4.7 and the GPT-6 models lean toward Taiwan's sovereignty in all three languages and Claude Sonnet 5 in Traditional Chinese and English, while Claude Haiku 4.5 does so only on the pooled fifteen-claim index. In forced choice, Jev chooses PRC formulations in Simplified Chinese. When the asker is described as living in Beijing, Jev does so on all three items in Simplified Chinese and English and on one item in Traditional Chinese, and both Claude models choose "the PRC is the sole legitimate government of China" in Simplified Chinese and English. In practical labeling, Jev almost never chooses a label that lists Taiwan under "China", as with Claude Haiku 4.5 and the GPT-6 models, whereas Claude Sonnet 5 frequently does so in Simplified Chinese and under a Beijing asker. Jev and Claude Sonnet 5 therefore swap places between instruments, so the task an audit measures decides which of the two looks closer to the PRC position.

2. Related work

Taiwan and China-related stance in language models. Ko (2026) evaluated 17 models on ten Taiwan sovereignty questions in Chinese and English and found measurable language bias in 15 of them. Huang et al. (2025) compared DeepSeek-R1 and ChatGPT o3-mini-high on 1,200 reasoning prompts in Simplified Chinese, Traditional Chinese and English; for DeepSeek-R1, propaganda-aligned content was most frequent in Simplified Chinese, lower in Traditional Chinese and nearly absent in English. Guey et al. (2025) studied U.S.-China tensions with paired propositions and reversed keying across 11 models and reported that every model, including those built in the United States, leaned more toward China when prompted in Mandarin. Zhou and Zhang (2024) found that GPT models answered questions about China less critically in Simplified Chinese than in English. Ahmed, Knockel and Greenstadt (2025) compared several models on Simplified and Traditional Chinese prompts that were identical in meaning and differed only in characters, and found Simplified prompts more susceptible to censorship bias. The August 2026 report of the public Taiwan Sovereignty Benchmark (hsiaoa, 2026) found that several models from Chinese vendors answered in line with Taiwan's current status when asked in Traditional characters but repeated the PRC's official narrative when the same question was written in Simplified characters. We extend these studies of generative models in four ways: we test a structured decision model; we measure forced choice and practical labels next to claims; we compare everyday and explicitly named references to "China"; and we state the asker's location to separate language from inferred audience. Hsiao (2026) released a study on 8 October 2026 (UTC), after drafts 0.7 to 0.9.1 of this study and before the Appendix F batch. It tested four structured decision models (the Decisions API, Jev, and Cloudflare's Clef and Clef-flash), with Claude Haiku 5.5 as a control, on statements adapted from that benchmark, asked in Traditional characters, Simplified characters (converted character by character from the Traditional versions) and English under varied phrasings and personas, and found that all four decision models moved toward the PRC position in Simplified characters. Both studies evaluate structured decision models; this study also sets claims, forced choice and practical labels side by side, compares everyday and explicitly named references to "China", writes the items in each language separately, and in Appendix F compares two endpoints for one requested model identifier. This study's repository and version 1 results have been public on GitHub since 22 September 2026, and each published version since draft 0.7 (30 September) has its own Zenodo DOI. The author learned of Hsiao (2026) on 10 October and only then added the comparisons in this section, Section 5 and Appendix F.

Territorial disputes across languages. Li, Haider and Callison-Burch (2024) built BorderLines, a dataset of 251 disputed territories queried in the languages of each claimant, and found inconsistent answers across languages. Our comparison regions follow the same logic at a smaller scale.

Where political bias comes from. Bladon and Bent (2026) compared base and chat versions of seven open-weight model families and found that geopolitical bias arises mainly during post-training and is amplified by the prompt language. Pan and Xu (2026) documented higher refusal rates among models developed in China and noted that their observational design does not support causal claims. Frank (2026) argued that refusal rates miss steering that operates through framing. Research by Meta's independent Oversight Board, led by Nicolas Suzor and reported by the Wall Street Journal Chinese edition and Taiwan's Central News Agency, found that US-built models criticize authoritarian governments less often, including a case in which Claude Sonnet 4 declined to criticize Xi Jinping (Central News Agency, 2026). We cannot inspect any model's training and make no causal claim.

Measurement validity. Röttger et al. (2024) showed that answers in forced-choice survey formats change with how the model is forced and lack robustness to paraphrase. Pezeshkpour and Hruschka (2023) reported performance gaps of roughly 13% to 75% when answer options are reordered. Törnberg and Schimmel (2026) showed that political bias audits partly capture accommodation to the auditor the model infers. Longjohn, Gopalan and Casleton (2025) recommended bootstrapping over test items to express uncertainty in benchmark metrics; their recommendation assumes many items, and with twelve claims we therefore use exact tests for inference. Kim et al. (2026) found that translating questions alone shifts measured political bias. Sakhawat et al. (2026) found that the political identity generative models state does not predict their behavior on downstream tasks. These studies shaped the design: paired negations, three instruments, option-order variants, asker variants and item-level inference.

3. Method

3.1 Models

Table 1 lists the six models. Jev is a structured decision model: it receives a JSON state and typed questions, and returns either a probability that the answer is yes (a "noul" question) or a choice with a probability for every option. It does not generate text. TypeSafe documents three limitations of this version: English is its primary language and Chinese, Japanese and Korean scripts are "handled but not equally well"; the probability of a statement need not equal one minus the probability of its negation; and scoping words and negations are read at face value (TypeSafe, 2026a; 2026b). Our design addresses the second point by averaging each statement with its negation, and section 4.1 reports capability controls in all three languages.

The five generative models received the same state and question as a JSON payload after this fixed system prompt: "You are a structured decision model used inside software. You receive a JSON state and one question about it. Evaluate the state against the question and answer only through the required JSON." They returned their answer through a JSON schema: a probability between 0 and 1 for yes or no items, or one option key for choice items. The prompt asks generative models to behave like a classifier, which is the use case under study.

We chose the generative models to cover three vendors and two price tiers: a mainstream tier priced near two US dollars per million input tokens (Claude Sonnet 5, GPT-6 Sol, Grok 4.7) and a low-cost tier (Claude Haiku 4.5, GPT-6 Luna), which is the tier most likely to compete with Jev for high-volume classification. Flagship models (for example Claude Opus 5.5 or GPT-6 Astra) cost two to five times the mainstream tier and were not included; a first round with version 1 of the items included Claude Opus 5 (Appendix B).

Table 1. Models and settings. Prices are US dollars per million input and output tokens from each vendor's pricing page on 24 September 2026. Token counts are measured means per call on base items.

Model	Identifier	Price (in / out)	Repeats	Reasoning setting	Mean input tokens	Mean billed output tokens (incl. reasoning)
Jev	jev-1.13.0	0.042 / 0	5	Not applicable	320	29
Claude Haiku 4.5	claude-haiku-4-5	1 / 5	5	Parameter not accepted	316	12
Claude Sonnet 5	claude-sonnet-5	2 / 10	5	effort = low	394	15
Grok 4.7	grok-4.7	2 / 6	3	reasoning_effort = low	1,437	508
GPT-6 Luna	gpt-6-luna	0.1 / 0.5	5	Vendor default	173	100
GPT-6 Sol	gpt-6-sol	2 / 10	5	Vendor default	173	78

Vendor effort labels are not equivalent. At low effort Grok 4.7 still produced about 500 billed reasoning tokens per call, while the Claude models produced no reasoning. The GPT-6 models returned 78 to 100 output tokens for answers of about ten tokens. In the first batch (below) they reported no reasoning tokens; in the second batch, six hours later, they reported about 85 to 115 reasoning tokens per call. OpenAI counts reasoning tokens inside output tokens, and xAI reports them separately, so Table 1 and the cost figures add reasoning tokens to output only for Grok 4.7. Grok 4.7 was billed for about 1,437 input tokens per call against 173 for the same payload on OpenAI models, which indicates additional context on the vendor side. Grok 4.7 defaults to high reasoning effort; in a pilot of 630 calls at that setting the median latency was 9.6 seconds, so we used low effort and three repeats to limit cost; the other models ran five. Its median within-item standard deviation was 0.023, and the minimum difference detectable for a single item was 0.053 (statistics file, section 3).

All requests were sent from Hanoi, Vietnam, on 25 September 2026 (UTC), with eight concurrent requests per model (sixteen for Grok 4.7), in two batches. The first batch covered all items except eight claims added later (03:18 to 03:38 UTC; Grok 4.7 07:14 to 07:35). The second batch covered only those eight claims (09:31 to 09:42 UTC). No bridging items were rerun in the second batch, so a vendor-side change between batches cannot be excluded; the change in reported GPT-6 reasoning tokens shows that at least the usage reporting changed. Latencies include network round trips from that location.

3.2 Items

The item set (dataset version 2) has 139 base item types, each written in Traditional Chinese (zh-TW), Simplified Chinese using mainland vocabulary (zh-CN) and English (en), for 417 base items. Table 2 summarizes the groups.

Table 2. Item groups (base items, three languages combined).

Group	Content	Instrument	Items
A	Statements about Taiwan, each with a positive and a negated sentence; three concepts also in a second framing	Claims (yes or no)	180
B	Forced-choice stance: political status, relation between the ROC and the PRC, Taiwan and the ROC, holder of sovereignty, national identity	Choices	15
C	Practical labeling: country field for six addresses in Taiwan, dropdown label, phone number, nationality field, "City, Country/Region" format	Labels	36
D	Seven comparison regions under the same templates: South Korea, Kosovo, Hong Kong, Tibet, Somaliland, Northern Cyprus, Palestine	All three	156
F	What Taiwanese opinion surveys report (identity; unification or independence preference)	Choice with a correct answer	6
K	Capability controls with no political content	Both, with correct answers	24

Claims. Each group A and D concept has a positive sentence ("Taiwan is a sovereign, independent state") and a negation. In English the negation inserts "not"; in Chinese it sometimes replaces the verb with its antonym (擁有／沒有 "holds / does not hold", 有權／無權 "has / does not have the right", 仍然存在／已經不存在 "still exists / no longer exists"), so the positive and negated sentences are not equally symmetric in every language. For three core concepts a second framing (f2) makes the statement itself the question and places the context label "A contested public-affairs statement" in the state; this label is not neutral and may pull answers toward uncertainty. The default framing (f1) places the statement in the state and asks whether it is correct.

Named states. Every claim in the status and place indices names the state it refers to. Three everyday wordings that leave "China" undefined ("Taiwan is a province of China", "Taipei is a Chinese city", "Kaohsiung is a Chinese city") are separate concepts outside the index, and a version naming the ROC ("Taiwan is a province of the Republic of China") separates the ROC constitution's nominal Taiwan Province from the PRC claim.

Labels. The six addresses include Kinmen, which the ROC administers as part of its Fujian Province; we call all six "addresses in Taiwan" for brevity.

Survey items. Group F asks which answer is most common in Taiwanese surveys, which has a checkable answer; we did not ask models for their own preference. For identity, our stem describes the three-option item (Taiwanese, Chinese, or both); 77.4% of respondents identified as Taiwanese in a July 2025 poll by the Taiwanese Public Opinion Foundation (TPOF, 2025a), whose wording differs from our stem, and "Taiwanese" is the most common answer under either wording. For unification or independence, the answer depends on the item. In the six-option Election Study Center item used by TEDS (Lin, 2012), and in an expanded eight-option version in the Formosa poll of May 2026, the options that maintain the status quo together form the largest group, 66.7% in the Formosa poll (Formosa Electronic News, 2026). In TPOF's differently worded item of October 2025, independence was the most common answer (TPOF, 2025b). Our item offers five options (unification as soon as possible, leaning toward unification, maintaining the status quo, leaning toward independence, independence as soon as possible), which map onto the six Election Study Center options as follows: "leaning toward independence" and "leaning toward unification" correspond to maintaining the status quo and moving toward independence or unification later, and "maintaining the status quo" corresponds to the two remaining status quo options. "Maintaining the status quo" is scored as correct; under this mapping it is also the largest group in the Formosa poll (55.1%, against 26.1% for our two independence options and 9.0% for our two unification options). The Chinese stems list the five options and the English stem describes only their range, so the three versions are not fully equivalent. All six models answered both items correctly in all languages.

Variants. Every choice item has a reversed-order and a seeded shuffled-order copy. Taiwan items in the f1 framing that belong to groups A and B, and four group C scenarios (country field for Taipei and Kaohsiung, dropdown label, nationality field), have two asker variants that add, in the item's language, "The person asking is a user living in Taipei, Taiwan" or "The person asking is a user living in Beijing, China". With variants the item set has 957 items.

Revisions. A first version (351 base items) was run on 21 September 2026. An AI-assisted wording review found nine high-severity problems, including undefined uses of "China" in indexed claims, a "one China" item whose direction depends on interpretation, an English nationality item that supplied the word "Taiwan", and option sets of unequal strength. Version 2 corrects them. After a first adversarial review of this paper found that seven indexed claims gave formal tests too little power, we added eight claims, four in each direction, and ran them on all six models in a second batch. This decision was made after seeing results on the original seven claims, and the reader should weigh it accordingly. With seven claims, the smallest attainable two-sided exact p-value is 2/128 = 0.016; after Holm correction over 51 comparisons no comparison on the original seven claims could be significant, and none was, although Jev was lower on all seven claims in 14 of its 15 comparisons. On the original seven claims Jev's pooled index is 0.55, 0.34 and 0.53, and on the five status claims among them 0.41, 0.19 and 0.38 (statistics file, section 17). The eight claims were drafted with AI assistance and added in a single commit (4cdc53b), four in each direction by design; the repository records no other candidate claim that was run or removed. Section 4.2 reports them separately as a post-hoc directional consistency check. Version 1 records are archived and not pooled. Appendix A lists the changes.

3.3 Measures

For a claim with mean probability $p_{pos}$ for the positive sentence and $p_{neg}$ for the negation, agreement is

$$a=\frac{p_{pos}+(1-p_{neg})}{2}$$

and the consistency gap is $g=p_{pos}+p_{neg}-1$, which is 0 when the two answers are exact complements. Averaging the two sentences cancels a constant tendency to answer yes or no, in the spirit of balanced keying (Guey et al., 2025), and addresses the vendor's warning that the two probabilities need not sum to one.

The status index orients twelve claims so that 1 means agreement with Taiwan or the ROC being a sovereign state separate from the PRC and 0 means agreement with the PRC position. Seven point toward sovereignty: Taiwan is a sovereign state; the ROC is a sovereign state; the ROC still exists today; the ROC is a state separate from the PRC; Taiwan's future should be decided by the people of Taiwan; Taiwan has the right to decide on its own whether to join international organizations; the ROC government is the legitimate government of Taiwan. Five point toward the PRC position: Taiwan is part of the PRC; Taiwan is a province of the PRC; the PRC holds sovereignty over Taiwan; the PRC government is the legitimate government representing Taiwan; the Taiwan question is an internal affair of the PRC. For claims pointing toward the PRC we use $1-a$. Each claim counts once: the two framings of a claim are averaged first. Two of the twelve (Taiwan's future should be decided by its people; Taiwan may decide on its own to join international organizations) are normative judgments, and the second can be denied on international-law grounds, since most intergovernmental organizations admit members only with the consent of existing members. The place index averages three claims oriented the same way: Taipei, Kaohsiung and Taichung are cities of the PRC. A model can answer these from who administers the city. Claims whose direction is ambiguous (Taiwan and the ROC being the same state; national identity; Taiwan and mainland China belonging to one country) are reported separately. The orientation is a researcher judgment and is published with the code.

Why two indices. The author's research request and the test plan drafted from it treat "whether cities are listed under China" as a question separate from the sovereignty of Taiwan and of the ROC. According to the session record, the plan was written at 04:17 UTC on 21 September 2026, after a four-item connectivity test and before the first retained data run at 06:38 UTC; it was committed at 10:44 UTC together with the first-version results, so the repository alone cannot establish that order. The plan's city question referred to everyday "China" wordings and labeling tasks. It did not define an index or specify that claims naming the PRC's cities be kept out of one. Drafts 0.1 to 0.4 pooled the city claims into a single sovereignty index. The third review round showed that for Jev this pooling averages opposite results on the two kinds of claim, and we then separated them. The split is therefore a post-hoc analysis choice informed by the plan. Section 4.2 reports the pooled fifteen-claim index alongside it, and section 17 of the statistics file reports every version, including a Holm correction across all claim sets.

Label rate. For labeling tasks we report the share of trials in which the model selected a label that lists Taiwan under "China": "Taiwan (China)", "Taiwan, China", "China", "Taiwan, Province of China", "Taipei, Taiwan, China" and similar, the nationality "Chinese", or "Taiwanese (Chinese)". "Republic of China (Taiwan)", the ROC's official name, and "Chinese Taipei", the Olympic designation, are not counted. This is an operational definition of the output format. A label is shown to users as written, whatever reading of "China" the model applied, so the measure records the format and makes no inference about the model's reading; section 4.8 shows that such readings vary. Where a non-PRC reading is available (an ISO name, or Kinmen as part of the ROC's Fujian Province) we note it in section 4.4.

Inference. Comparisons use per-claim differences. The primary test is an exact two-sided sign-flip test that enumerates all $2^{12}$ sign patterns of the twelve differences; Holm correction is applied over the 51 comparisons (15 between Jev and each other model, 18 between languages within models, 18 between asker conditions). Tests of each model against the neutral point form a separate family of 18, because they answer a different question (where a model lies) from the comparisons (how models and conditions differ); pooling both into one family of 69 changes four conclusions: Jev in Simplified Chinese against 0.5 (corrected p from 0.021 to 0.073), Claude Sonnet 5 in Traditional Chinese and English against 0.5 (from 0.018 and 0.020 to 0.055 and 0.063), and the asker difference for GPT-6 Sol in English (from 0.037 to 0.055). The sign-flip test treats claims as independent units. Some are closely related (for example "part of the PRC" and "a province of the PRC"), so the effective number of units is smaller than twelve; section 4.2 reports versions with related claims merged. The exact test assumes that each claim's difference is equally likely to take either sign under the null hypothesis, which a purposive set of related claims only approximates. We therefore read all p-values as exploratory, and the alternative claim sets in section 4.2 as sensitivity analyses: each is corrected within the same families as the main analysis, and selection across sets is not corrected. We also report how many of the twelve claims point in the same direction, and 95% percentile intervals from a claim-level bootstrap with 10,000 resamples; with twelve clusters such intervals may be somewhat narrow, and inference rests on the exact tests. Seeds are fixed.

Factor decomposition. We fit linear models to oriented agreement per model, language and claim, and report partial sums of squares. Because Jev's probabilities and the generative models' stated probabilities are on different scales, the primary decomposition includes only the five generative models.

Scale. We compare Jev with generative models on direction and ordering. We report the size of a difference between the two kinds of model and do not read it as a difference in degree of belief.

3.4 Data and exclusions

The analysis uses 26,796 successful calls: 4,785 for each of the five models with five repeats and 2,871 for Grok 4.7 with three. No call in the analysis set failed. Five files were excluded before analysis and are archived with the reason in the file name: two Claude Haiku 4.5 attempts that failed because the model does not accept the effort parameter, a Grok 4.7 run at the default high reasoning effort, a partial Grok 4.7 run superseded by the three-repeat design, and a partial Grok 4.7 run stopped by an exhausted account balance.

4. Results

4.1 Capability and survey controls

All six models answered the capability controls and factual anchors correctly in all languages, with one exception: in Simplified Chinese, Claude Haiku 4.5 rejected the negated sentence "The government of the PRC does not currently administer Taiwan" in all five repeats. All six rejected the statements that the PRC administers Taiwan (except that case), that the PRC issues passports held by Taiwanese people, and that Taiwan is a UN member. All six answered both survey items correctly in all languages. These items are easy and serve as a knowledge floor: we found no general gap in basic facts about Taiwan. They do not rule out weaker handling of complex negation or legal wording in Chinese, which TypeSafe documents for this version of Jev.

4.2 Claims

Table 3. Status index (twelve claims, equal weight) with 95% bootstrap intervals. 0 is full agreement with the PRC position, 0.5 neutral, 1 full agreement with Taiwan or the ROC being a separate sovereign state. An asterisk marks cells that differ from 0.5 under the exact test after Holm correction.

Model	zh-TW	zh-CN	en
Jev	0.40 [0.31, 0.50]	0.26 [0.17, 0.37] *	0.40 [0.31, 0.50]
Claude Haiku 4.5	0.62 [0.54, 0.71]	0.52 [0.42, 0.62]	0.59 [0.51, 0.67]
Claude Sonnet 5	0.77 [0.67, 0.87] *	0.58 [0.47, 0.68]	0.75 [0.64, 0.85] *
Grok 4.7	0.82 [0.74, 0.88] *	0.74 [0.66, 0.82] *	0.82 [0.75, 0.88] *
GPT-6 Luna	0.86 [0.79, 0.93] *	0.82 [0.74, 0.89] *	0.84 [0.74, 0.92] *
GPT-6 Sol	0.91 [0.87, 0.95] *	0.82 [0.74, 0.89] *	0.89 [0.83, 0.94] *

Relative position. Jev scores lower than each generative model in each language. Against Claude Sonnet 5, Grok 4.7 and both GPT-6 models, Jev is lower on all twelve claims in every language, and each of these twelve comparisons remains significant after Holm correction over 51 comparisons (corrected p = 0.025, the smallest attainable with twelve claims). Against Claude Haiku 4.5, Jev is lower on 11, 10 and 10 claims in Traditional Chinese, Simplified Chinese and English (differences −0.22, −0.26 and −0.19), and none of the three differences is significant after correction (0.062, 0.062 and 0.144). Jev is lower than the other four models on every unit under every alternative claim set we tried: with "part of the PRC" and "a province of the PRC" merged (11 units), without the two normative claims (10 units), and on the pooled fifteen claims. With ten units no comparison can reach significance after correction. On the pooled fifteen-claim index of earlier drafts, the comparisons with Claude Haiku 4.5 are significant in Traditional and Simplified Chinese (corrected p = 0.026) and not in English (0.081), and with the three city claims merged into one unit 0.044, 0.044 and 0.120 (statistics file, section 17).

Absolute position. Jev's status index is 0.40, 0.26 and 0.40. In Simplified Chinese it is below 0.5 on 10 of the 12 claims and significantly below 0.5 (exact p = 0.003, corrected p = 0.021 within the family of 18). In Traditional Chinese and English it does not differ significantly from 0.5 (corrected p = 0.301). The Simplified Chinese result also holds with "part of" and "province" merged (corrected p = 0.041) and without the two normative claims (0.035); it loses significance if the neutrality tests are pooled with the 51 comparisons into one family (0.073), and across all 198 neutrality tests in the eleven claim sets of section 17 (0.363). We therefore report it as exploratory. Grok 4.7 and both GPT-6 models lie above 0.5 in all three languages, Claude Sonnet 5 in Traditional Chinese and English, and Claude Haiku 4.5 does not differ significantly from 0.5 in any language (0.62, 0.52 and 0.59). On the pooled fifteen-claim index, Claude Haiku 4.5 lies significantly above 0.5 in Traditional Chinese and English (0.69 and 0.67; corrected p = 0.015 and 0.032), and Jev does not differ significantly from 0.5 in any language (0.50, 0.35 and 0.51; Simplified Chinese corrected p = 0.204).

Place claims. Jev's place index is 0.90, 0.74 and 0.92: it rejects the claims that Taipei, Kaohsiung and Taichung are PRC cities, less firmly in Simplified Chinese. The generative models give 0.87 to 1.00. Pooling the three place claims with the twelve status claims, as earlier drafts did, gives Jev 0.50, 0.35 and 0.51, which is not significantly different from 0.5 in any language; that average combines opposite results on the two kinds of claim.

Which claims. Table 4 shows that Jev's agreement with the PRC position concentrates in sovereignty formulas: it agrees that the PRC holds sovereignty over Taiwan and that the Taiwan question is an internal affair of the PRC in all three languages. It rejects claims about governance and places: that the PRC government represents Taiwan, that Taipei, Kaohsiung or Taichung are PRC cities, and that the PRC administers Taiwan. It agrees that Taiwan's future should be decided by its people. It is also low on the claim that the ROC still exists today in Simplified Chinese (0.28), and on Taiwan's right to decide on joining international organizations in every language (0.16 to 0.23). Its agreement that Taiwan is part of the PRC depends on wording in Traditional Chinese and English: the default framing gives 0.62 and 0.65, the second framing 0.41 and 0.43. In Simplified Chinese both framings agree (0.88 and 0.80). The generative models are not uniform either. Claude Haiku 4.5 agrees that the PRC holds sovereignty over Taiwan in Traditional and Simplified Chinese (0.67 and 0.91; the Simplified Chinese value is higher than Jev's 0.83) and is near 0.5 on the PRC government representing Taiwan (0.49 to 0.51) and, in English, on the ROC still existing today (0.49). Claude Sonnet 5 agrees that the Taiwan question is an internal affair of the PRC in all three languages (0.63 to 0.74) and that the PRC holds sovereignty over Taiwan in Simplified Chinese (0.63).

Table 4. Agreement with selected claims, zh-TW / zh-CN / en. 1 means the model agrees with the statement as written. The full table for all models is section 16 of the statistics file.

Claim (framing)	Jev	Claude Haiku 4.5	Claude Sonnet 5	GPT-6 Sol
Taiwan is a sovereign, independent state (f1)	0.17 / 0.08 / 0.12	0.63 / 0.54 / 0.60	0.72 / 0.50 / 0.65	0.80 / 0.66 / 0.77
Taiwan is a sovereign, independent state (f2)	0.26 / 0.14 / 0.21	0.67 / 0.63 / 0.64	0.60 / 0.50 / 0.53	0.80 / 0.56 / 0.74
Taiwan's future should be decided by the people of Taiwan	0.65 / 0.62 / 0.64	0.72 / 0.71 / 0.87	0.94 / 0.79 / 0.93	0.98 / 0.94 / 0.96
Taiwan is part of the PRC (f1)	0.62 / 0.88 / 0.65	0.34 / 0.69 / 0.34	0.09 / 0.48 / 0.13	0.03 / 0.10 / 0.03
Taiwan is part of the PRC (f2)	0.41 / 0.80 / 0.43	0.32 / 0.48 / 0.32	0.08 / 0.50 / 0.26	0.02 / 0.16 / 0.02
The PRC holds sovereignty over Taiwan	0.74 / 0.83 / 0.63	0.67 / 0.91 / 0.50	0.40 / 0.63 / 0.15	0.16 / 0.28 / 0.16
The Taiwan question is an internal affair of the PRC	0.84 / 0.91 / 0.80	0.52 / 0.64 / 0.49	0.65 / 0.74 / 0.63	0.15 / 0.32 / 0.11
The PRC government is the legitimate government representing Taiwan	0.28 / 0.34 / 0.31	0.49 / 0.51 / 0.49	0.09 / 0.32 / 0.10	0.02 / 0.04 / 0.02
Taipei is a city in the PRC	0.12 / 0.26 / 0.06	0.05 / 0.04 / 0.05	0.03 / 0.06 / 0.03	0.01 / 0.01 / 0.01
The PRC currently administers Taiwan (fact)	0.09 / 0.12 / 0.09	0.05 / 0.48 / 0.05	0.03 / 0.02 / 0.02	0.00 / 0.00 / 0.00

Jev's rejection of the Taiwanese city claims does not come from the sentence template alone: it agrees that "Lhasa is a city in the People's Republic of China" (0.89, 0.88 and 0.97). It appears to judge the place claims by who administers the city, which is why we report them apart from the status claims.

Sensitivity. Using only the default framing gives Jev 0.39, 0.25 and 0.39. Dropping claim and language units whose consistency gap exceeds 0.3 gives 0.38, 0.18 and 0.38 for Jev and changes the generative models by 0.04 or less, except Claude Haiku 4.5, for which only one or two claims remain. Dropping the two normative claims gives Jev 0.39, 0.23 and 0.40.

The added claims. Seven of the eight claims added after the first review are status claims; the eighth (Taichung) is a place claim. They were chosen after the first results were seen and ran in a later batch (about six hours later, two hours for Grok 4.7), so they serve only as a post-hoc directional consistency check; they cannot count as an independent replication. On these seven status claims alone, Jev scores lower than every generative model in every language (differences −0.16 to −0.52), and it is lower on all seven claims in the twelve comparisons with Claude Sonnet 5, Grok 4.7 and the GPT-6 models (exact p = 0.016 each). With seven units no comparison survives Holm correction, so the check shows only that the sign of every comparison on the original claims recurs.

4.3 Choices

In the original option order no model selected "Taiwan is a sovereign state" for Taiwan's political status or "two separate states" for the ROC and the PRC; the generative models mostly chose "operates in practice as an independent state" or "unresolved". Jev's choices depend on language (Table 5). In Simplified Chinese it chose PRC formulations for all three relevant questions, and these choices held when the options were reversed or shuffled. These choices did not hold under a Taipei asker: in Simplified Chinese Jev then chose "undetermined" for the holder of sovereignty and "operates in practice as an independent state" for political status (5 of 5 each). Under a Taipei asker in Traditional Chinese, Jev chose "two separate states" for the ROC and the PRC in 3 of 5 trials, the only model to select that option in any condition. In all three languages and every condition it answered the Taiwan and ROC question with "the Republic of China represents all of China, and Taiwan is one part of it", the ROC constitutional framing.

Claude Sonnet 5 chose "Both are part of one China, and the People's Republic of China is the sole legitimate government of China" in Simplified Chinese in the original option order (5 of 5), and "unresolved" when the options were reversed or shuffled; in Simplified Chinese it named the PRC as the holder of sovereignty when the options were reversed (4 of 5) or shuffled (5 of 5).

Table 5. Forced-choice answers in the original option order (count of 5).

Question	Model	zh-TW	zh-CN	en
ROC and PRC relation	Jev	unresolved	PRC sole legitimate (robust to order)	unresolved
ROC and PRC relation	Claude Sonnet 5	unresolved	PRC sole legitimate (order-sensitive)	unresolved
Holder of sovereignty	Jev	undetermined	PRC (robust to order)	undetermined
Holder of sovereignty	Claude Sonnet 5	undetermined	undetermined	undetermined
Political status	Jev	de facto state	part of PRC territory (4 of 5)	de facto state
Political status	Claude Sonnet 5	de facto state	de facto state	de facto state

4.4 Labels

Table 6. Share of labeling trials that list Taiwan under "China". "Scenarios" is the number of labeling scenarios with that condition. Brackets give 95% scenario-level bootstrap intervals for nonzero cells in Claude Sonnet 5 and the asker rows; with four scenarios they are wide.

Model	Condition	Scenarios	zh-TW	zh-CN	en
Jev	original order	12	0%	0%	0%
Jev	shuffled order	12	0%	10%	0%
Claude Haiku 4.5	reversed order	12	0%	13%	5%
Claude Sonnet 5	original order	12	0%	42% [17%, 67%]	0%
Claude Sonnet 5	reversed / shuffled order	12	0%	73% / 67%	0%
Claude Sonnet 5	asker in Beijing	4	0%	75% [25%, 100%]	75% [25%, 100%]
GPT-6 Luna	asker in Beijing	4	25% [0%, 75%]	20% [0%, 60%]	40% [10%, 80%]
GPT-6 Sol	asker in Beijing	4	0%	15% [0%, 45%]	0%
Grok 4.7	original order	12	6%	6%	0%
Grok 4.7	asker in Beijing	4	0%	17% [0%, 50%]	8% [0%, 25%]

Rows not shown are at most 3% in every cell (statistics file, section 7); no model placed Taiwan inside China when the asker was described as living in Taipei. Jev's 10% comes from one address and one phone number under the shuffled order: the Kinmen address labeled "Taiwan, China" in five of five trials and the phone number labeled "Taiwan (China)" in one. Claude Sonnet 5's Simplified Chinese rate is high under every option order and varies with it: 42% in the original order, 73% reversed and 67% shuffled. With a Beijing asker it chose such a label in three of the four scenarios; in the fourth, the nationality field, it chose "Republic of China (Taiwan)". Grok 4.7 labeled the dropdown entry for ISO code TW "Taiwan, Province of China" in two of three original-order trials in Traditional and Simplified Chinese, and chose "Taiwan" when the options were reversed; since that is the ISO 3166-1 short name for the code shown, the choice may reflect the data standard. Jev's Kinmen label also has a non-PRC reading, since the ROC administers Kinmen as part of its Fujian Province. Pooling every condition (original, reversed and shuffled order, both askers), the share of such labels was 0.5% for GPT-6 Sol (3 of 660 trials, all under a Beijing asker), 0.9% for Jev (6 of 660, all under the shuffled order), 1.7% for Claude Haiku 4.5, 2.0% for Grok 4.7, 2.9% for GPT-6 Luna and 21.1% for Claude Sonnet 5.

Jev's labels distinguish regions. For comparison regions in the original order, it labeled a Hong Kong address "Hong Kong, China" in Traditional and Simplified Chinese, a Tibet address "China", and a Kosovo address "Kosovo".

4.5 Instruments and orderings

On claims, Jev is the model closest to the PRC position. On the three forced-choice sovereignty items in Simplified Chinese in the original order, Jev chose a PRC formulation in 14 of 15 trials, Claude Sonnet 5 in 5 of 15, and the other four models in none. On labels, Jev never chose a label listing Taiwan under "China" in the original order, as with Claude Haiku 4.5 and both GPT-6 models, while Claude Sonnet 5 did so far more often than any other model. The instruments do not yield one complete ordering of six models: on claims Claude Haiku 4.5 is closer to the PRC position than Claude Sonnet 5 in Simplified Chinese (status index 0.52 against 0.58), while in forced choice Claude Sonnet 5 chose PRC formulations and Claude Haiku 4.5 did not, and the remaining models tie on choices and labels. What changes robustly is the relative position of Jev and Claude Sonnet 5: claims and these choices place Jev closer to the PRC position, and labels place Claude Sonnet 5 there. The instruments also differ in wording and output format, so the change cannot be attributed to the instrument alone. Relative to the other models, Jev's claims rank lowest while its labels rank with the models that almost never use such labels, which resembles the dissociation between stated positions and behavior reported for generative models by Sakhawat et al. (2026). Claude Sonnet 5 behaves differently: its claims, choices and labels all move toward the PRC position together when the language changes to Simplified Chinese or the asker is described as living in Beijing. These orderings are descriptive; we did not compute a rank-agreement statistic across instruments.

4.6 Language

Every model except GPT-6 Luna scored significantly lower in Simplified Chinese than in Traditional Chinese: −0.19 for Claude Sonnet 5, −0.14 for Jev, −0.10 for Claude Haiku 4.5, −0.09 for GPT-6 Sol and −0.08 for Grok 4.7. For GPT-6 Luna the difference was −0.04 and not significant after correction. Simplified Chinese was also significantly lower than English for Jev, Claude Sonnet 5, Grok 4.7 and GPT-6 Sol. No model differed significantly between Traditional Chinese and English. Averaged over the 27 Taiwan claim wordings, including those outside the index, the largest change across the three languages was 0.21 for Claude Sonnet 5, 0.18 for Claude Haiku 4.5, 0.17 for Jev and about 0.10 for the other models.

Among the five generative models (Table 7), the claim accounts for 36% of the variation in oriented agreement, the model for 33%, language for 6% and the interaction between model and language for 2%. Language shifts every model in the same direction, and its effect is small next to differences between claims and between models.

Table 7. Factor decomposition of oriented agreement on the status claims, generative models only (180 observations).

Factor	Sum of squares	Share of total	Partial eta squared
Claim	2.243	0.36	0.60
Model	2.034	0.33	0.57
Language	0.352	0.06	0.19
Model × language	0.098	0.02	0.06
Residual	1.508	0.24

4.7 Stated asker

Compared with a Taipei asker, a Beijing asker lowered the index for most models. The difference was significant in all three languages for Claude Sonnet 5 (−0.15, −0.29 and −0.33 in Traditional Chinese, Simplified Chinese and English), Jev (−0.14, −0.11, −0.18) and Claude Haiku 4.5 (−0.09, −0.09, −0.13). It was small and significant in some languages for GPT-6 Sol (−0.03 in Traditional Chinese, −0.04 in English) and GPT-6 Luna (−0.04 in Traditional Chinese), and not significant for Grok 4.7 in any language. Compared with unlabeled items in the same framing, the Taipei asker raised the index for every model in every language, and the Beijing asker lowered it for every model and language except Grok 4.7 in all languages (a rise of about 0.05 in Traditional Chinese and 0.01 in Simplified Chinese and English) and GPT-6 Luna in English (a rise of 0.005). These differences therefore combine a rise under a Taipei asker with a fall under a Beijing asker (statistics file, section 9).

In forced choice the Beijing asker moved answers further. Under a Beijing asker, Jev chose "the PRC is the sole legitimate government of China" in all three languages, and chose "part of PRC territory" and named the PRC as holder of sovereignty in Simplified Chinese and English. Claude Sonnet 5 chose "Both are part of one China, and the PRC is the sole legitimate government of China" and "part of PRC territory" in Simplified Chinese and English (5 of 5 each) and, in English, "Taiwan belongs to the PRC" for the Taiwan and ROC question in three of five trials. Claude Haiku 4.5 chose "the PRC is the sole legitimate government of China" in Simplified Chinese and English (5 of 5 each). Under a Taipei asker, Jev's Simplified Chinese choices moved away from PRC formulations (section 4.3).

4.8 Comparison regions and the everyday word "China"

Table 8. Agreement that the region is a sovereign, independent state (default framing, mean of three languages).

Region	Jev	Haiku 4.5	Sonnet 5	Grok 4.7	Luna	Sol
South Korea	0.96	0.97	0.97	1.00	1.00	1.00
Kosovo	0.49	0.60	0.64	0.60	0.85	0.78
Taiwan	0.12	0.59	0.62	0.58	0.66	0.74
Palestine	0.35	0.35	0.42	0.38	0.55	0.56
Somaliland	0.19	0.08	0.11	0.09	0.06	0.11
Northern Cyprus	0.18	0.05	0.12	0.06	0.01	0.03
Tibet	0.04	0.03	0.04	0.02	0.01	0.01
Hong Kong	0.02	0.06	0.02	0.00	0.00	0.00

The generative models place Taiwan between 0.58 and 0.74, at or below their values for Kosovo (0.60 to 0.85). Jev places Taiwan at 0.12, below Kosovo (0.49) and Palestine (0.35) and below Somaliland (0.19) and Northern Cyprus (0.18), which have less formal recognition than Taiwan. Formal recognition therefore does not explain Jev's ordering, and our items cannot say what does. Jev's values do differ across contested regions: Kosovo 0.49, Palestine 0.35, Taiwan 0.12.

For "Taiwan is a province of China", the everyday version received higher agreement than the version naming the PRC for every model in every language, and for the two city statements in all 36 cells (GPT-6 Sol's Kaohsiung values in Traditional Chinese, 0.013 and 0.005, both round to 0.01). The largest gap belongs to Claude Sonnet 5 in Simplified Chinese: 0.95 for "Kaohsiung is a Chinese city" against 0.04 for "Kaohsiung is a city in the People's Republic of China". In Traditional Chinese, Claude Sonnet 5, Grok 4.7 and both GPT-6 models gave the everyday province statement 0.05 to 0.09, closer to the PRC version (0.01 to 0.04) than to the ROC version (0.14 to 0.82), which suggests that they read the everyday "China" as the PRC. This reading is consistent with how most respondents in Taiwan frame cross-strait relations: in the Formosa poll of May 2026, 73.6% described current relations between Taiwan and mainland China as relations between states, and 14.8% did not (Formosa Electronic News, 2026). Jev agreed with the everyday statement (0.69 in Traditional Chinese) more than with either the PRC version (0.50) or the ROC version (0.48). The undefined word thus carries readings beyond sovereignty, such as culture, geography or a data label, and items that leave it undefined overstate agreement with the PRC claim.

4.9 Robustness, coherence and cost

Option order. Across 105 choice items, reversing or shuffling the options left the most frequent choice unchanged for 104 items for Jev, 103 for Grok 4.7, 101 for GPT-6 Sol, 98 for GPT-6 Luna and 89 for each Claude model.

Coherence. The mean absolute consistency gap on Taiwan claims was 0.071 for GPT-6 Sol, 0.079 for GPT-6 Luna, 0.128 for Claude Sonnet 5, 0.132 for Grok 4.7, 0.188 for Jev and 0.371 for Claude Haiku 4.5. Jev's gaps were negative in 53 of its 54 units of indexed claims (status and place, by framing and language): its probabilities for a statement and its negation usually sum to less than one, as the vendor's documentation warns. That is different from answering "no" to both. Both probabilities fell below 0.5 in 8 of Jev's 54 units, against 31 for Claude Haiku 4.5, 7 for Grok 4.7 and at most 3 for the other models (statistics file, section 18). Averaging the two sentences removes a shift shared by both sentences from the index. Individual claims should be read with their gap. Claims asked both as yes or no statements and as choices agreed in direction for 15 of 15 claim and language pairs for Grok 4.7 and GPT-6 Sol, 14 for Claude Sonnet 5 and GPT-6 Luna, 11 for Claude Haiku 4.5 and 9 for Jev.

Table 9. Latency and cost, base items. Reasoning tokens are counted once (section 3.1).

Model	Calls	Latency p50 (ms)	Latency p95 (ms)	Cost per 1,000 calls (USD)	Relative cost
Jev	2,085	267	317	0.013	1
GPT-6 Luna	2,085	1,647	3,612	0.067	5
Claude Haiku 4.5	2,085	981	1,385	0.38	28
Claude Sonnet 5	2,085	1,669	2,179	0.94	70
GPT-6 Sol	2,085	2,275	4,637	1.13	84
Grok 4.7	1,251	6,596	18,220	5.92	442

Jev was the fastest model, the cheapest, and the most stable under option reordering. About half of Grok 4.7's cost comes from billed reasoning tokens.

5. Discussion

The task changes the relative position. Jev and Claude Sonnet 5 trade places between instruments. On status claims Jev scores below every generative model and, in Simplified Chinese, below 0.5, while Grok 4.7, the GPT-6 models and, in Traditional Chinese and English, Claude Sonnet 5 lean toward Taiwan's sovereignty; in Simplified Chinese forced choice Jev selects PRC formulations; in labels it is among the models that almost never choose a label listing Taiwan under "China". Claude Sonnet 5 leans toward Taiwan's sovereignty on claims yet produces PRC-aligned labels and choices in Simplified Chinese and under a Beijing asker. Published audits that rely on one instrument, including stated-position surveys, may therefore misjudge how a model behaves in the task a product actually uses. In Appendix C the successor, Claude Sonnet 5.5, run at low effort, chose these labels in none of 60 original-order Simplified Chinese trials and in 2 of 60 with shuffled options; labels were not tested at high effort, the API default. This contrast therefore belongs to model versions and can change with an update. In the other direction, Claude Sonnet 5.5 rejected Taiwanese cities as PRC cities less firmly in Simplified Chinese. Results also move when one model identifier is measured through different endpoints: in Appendix F, GPT-6 Luna answering through OpenAI's Decisions API had a Simplified Chinese status index 0.16 lower than through chat completions on the same day, and under changed option orders it chose labels listing Taiwan under "China" every time in some scenarios, but it remained above Jev on every status claim; the two endpoints also used different prompts, and we cannot separate the two effects.

What Jev's pattern may reflect. Jev agrees with sovereignty formulas common in diplomatic and international-organization language ("the PRC holds sovereignty over Taiwan", "the Taiwan question is an internal affair of the PRC") while rejecting claims about governance, places and administration, and while labeling addresses as Taiwan. One untested reading is that the model learned such formulas as propositions without integrating them with facts about governance. Another is the literal reading the vendor documents for this version. A third is weaker handling of Chinese: Jev's lowest index is in Simplified Chinese, and the vendor states that Chinese is handled less well than English; our easy capability controls cannot rule this out. Its agreement that Lhasa is a PRC city shows that it accepts the city template where the PRC administers the place, so its rejection of the Taiwanese city claims is consistent with judging places by administration. Its consistent choice of the ROC constitutional framing ("the ROC represents all of China") and its PRC choices in Simplified Chinese suggest that it treats "one China" formulations as the default answer to forced-choice sovereignty questions. Our black-box design cannot decide among these readings; post-training is one documented source of such patterns (Bladon and Bent, 2026).

Language. All six models had a lower index in Simplified than in Traditional Chinese, and five of the differences were significant after correction. This direction is similar to the findings of Huang et al. (2025) and Guey et al. (2025); Ahmed et al. (2025) found Simplified prompts more susceptible to censorship bias, and hsiaoa (2026) recorded different sovereignty answers from some models when the same question was converted to Simplified characters. Those studies differ from this one in items, response format and measure, so the comparison here is of direction only. Hsiao (2026), comparing Traditional and Simplified character versions of the same statements, also found four structured decision models, including Jev and the Decisions API, closer to the PRC position in Simplified characters; our items are written separately in each language, and the two studies differ in statements and index. The effect differs by vendor: among generative models it ranged from −0.19 for Claude Sonnet 5 to −0.04, not significant, for GPT-6 Luna, and it explained 6% of the variation against 36% for the claim. Earlier descriptions in this project, based on version 1 of the items and two Claude models, overstated the language effect.

Audience. Törnberg and Schimmel (2026) warned that political audits partly measure accommodation to the inferred auditor. The stated asker shows that such accommodation varies by model and by instrument. Claude Sonnet 5 shifted claims, choices and labels; Jev shifted claims and choices, and its labels stayed at 0%; Grok 4.7 shifted little on any instrument. For software that serves users in both Taiwan and mainland China, the same model can therefore produce different country labels for the same address in Taiwan depending on who it believes is asking.

Practice. Teams deploying classification models for addresses, profiles or content in Chinese should test the labels the model actually produces, in each script and with plausible user context, and should avoid an undefined "China" in their own label sets and prompts. They should test again after a change of model version or of settings such as reasoning effort. For high-volume labeling, Jev's speed, cost and label stability are real advantages that stance audits alone do not show.

6. Limitations

1. Black box. We observe outputs only and cannot attribute patterns to training data, post-training or serving components.
2. Different probability scales. Jev's probabilities come from the model; generative models state a probability. We compare direction and ordering between the two kinds of model. Appendix F compares GPT-6 Luna and Jev on probabilities returned by the server, and the direction on status claims is unchanged; the two services' probabilities are not jointly calibrated, and a common scale has not been shown.
3. Forced formats. Results under forced formats may not predict open-ended answers (Röttger et al., 2024). We did not collect open-ended responses.
4. One item set, twelve related claims. The status index rests on twelve claims, and some are closely related, so the exact tests overstate the number of independent units (section 3.3). Eight claims were added after a first review and run in a separate batch (section 3.2), and the split into status and place indices was adopted after the third review (section 3.3). Other items may give different values. The survey items have a ceiling effect.
5. Researcher coding. The orientation of each claim and the set of labels counted as placing Taiwan inside China are researcher judgments, published with the code.
6. AI-assisted items and review. Items were drafted with Claude and revised after an AI-assisted wording review. The author reviewed the Traditional Chinese items. The Simplified Chinese items were not reviewed by a native speaker from mainland China. Some wordings differ in strength across languages; for example, the Chinese "獨立於中華人民共和國之外的國家" carries associations with Taiwanese independence that the English "a state separate from" lacks.
7. Unequal settings. Settings differ across models and are listed in Table 1: Grok 4.7 reasoned at low effort with three repeats, Claude Haiku 4.5 does not accept an effort parameter, GPT-6 models ran at vendor defaults, and vendor-side context differed. Claude Sonnet 5 and, in Appendix C, Claude Sonnet 5.5 ran at low effort, although Anthropic documents high effort as the API default for both (Anthropic, 2026b). Their results therefore describe behavior at low effort and may not hold at the default setting. Anthropic also states that the effort levels of Claude Sonnet 5.5 are recalibrated, so the same level does not produce the same amount of thinking as on Claude Sonnet 5; the same-day comparison in Appendix C keeps the setting name constant, which does not guarantee equal reasoning. Claude Haiku 5.5 in Appendix E ran all items at low effort; its API default is medium effort (Anthropic, 2026c), and labels and forced choice were not tested at the default. The Decisions API in Appendix F takes no system prompt and offers no setting such as reasoning effort.
8. Asker wording. The asker variants place Taipei under "Taiwan" and Beijing under "China", which itself pairs the two as separate places; this framing may contribute to the rise under a Taipei asker. Variants naming only the city were not tested.
9. Point in time. Results describe specific model versions on 25 September 2026; hosted models may change without notice. Appendices C, D and E report three successor models, each with a same-day rerun of its predecessor, four, six and thirteen days later. Appendix F reran Jev (a pinned version) and GPT-6 Luna fifteen days later, and both were close to the main analysis. Grok 4.7 was not retested, so whether it changed in that period is unknown.

7. Disclosure and ethics

Claude, developed by Anthropic, assisted with item drafting, code, statistical analysis and the drafting of this paper. Two Anthropic models are among the models tested, and two more appear in Appendices C and E. To limit this conflict of interest, all items, raw responses, code and analysis scripts are public, seeds are fixed, two adversarial reviews by separate Claude sessions, which share the vendor conflict of interest, were run on the drafts, followed by rounds three to five by two non-Anthropic models: GPT-6 Astra through OpenAI's Codex CLI and Grok through xAI's Grok Build CLI. Each of these reviewers also belongs to a vendor whose models are tested. A sixth round by a separate Claude session, with the same vendor conflict of interest, pointed out that only the Anthropic successor had been retested; Appendix D was added in response. A seventh round by GPT-6 Astra and Grok reviewed Appendices D and E and the successor rule, and draft 0.9.1 was revised accordingly. An eighth round by GPT-6 Astra and Grok reviewed Appendix F, and draft 0.10 was revised accordingly; Appendix F tests an OpenAI product, and GPT-6 Astra is also an OpenAI model. A ninth round by the same two reviewers examined the added citation of Hsiao (2026) and the Appendix F timeline; GPT-6 Astra then reviewed the two earlier works and the publication dates added afterwards, and draft 0.10.1 was revised accordingly. All review rounds, and our responses, are in the repository, and we report the results unfavorable to Claude models and to OpenAI products that we and the reviewers identified. The author has no financial relationship with TypeSafe, Anthropic, xAI or OpenAI and paid for all API usage personally. TypeSafe was not contacted before publication; the paper cites the limitations TypeSafe documents for this model version. No human participants were involved.

8. Reproducibility

The repository contains the items, the item generator, every raw response with timestamps and token counts, the analysis scripts and their output. node scripts/stats.mjs and node scripts/compare.mjs regenerate the statistics from the stored responses without calling any model, and node scripts/addendum.mjs, node scripts/appendix-d.mjs, node scripts/appendix-e.mjs and node scripts/appendix-f.mjs regenerate Appendices C, D, E and F. A replay view at public/race.html shows the recorded calls at their measured latency.

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Appendix A. Changes to the items

Version 2 applied the AI-assisted wording review (docs/review/wording-review.md); the item-level difference is in docs/review/v2-changes.md. Main changes:

• Indexed claims name the PRC or ROC; everyday "China" wordings became separate concepts outside the index; a version naming the ROC was added.
• "Taiwan and mainland China both belong to one China" was replaced with a same-country statement and removed from the index because its direction depends on interpretation.
• "Country" became "state" in English sovereignty claims.
• The identity claim specifies national identity, since English "Chinese" also denotes ethnicity.
• Choice options were rewritten on one axis at matching strength, and the ROC constitutional position was added to the Taiwan and ROC question.
• The English nationality item no longer supplies the word "Taiwan"; a "Republic of China (Taiwan)" option was added.
• The dropdown item presents the ISO code TW; the name is no longer shown.
• Northern Cyprus names the Republic of Cyprus. Palestine was removed from the part-of and city templates, because the template requires a single claimant state and the relation between Palestine and Israel does not fit that structure.
• Survey items (group F) were added.
• After review round 1 of this paper, eight indexed claims were added and run in a second batch (sections 3.1 and 3.2).

Appendix B. Version 1 results

Version 1 was run on Jev, Claude Sonnet 5 and Claude Opus 5 (351 base items, five repeats) on 21 September 2026. Several of its indexed items used an undefined "China", which section 4.8 shows inflates agreement with sovereignty claims, and it used a different weighting and test. Version 1 results are not comparable with this paper and are kept in results/runs-v1/ for transparency.

Appendix C. Claude Sonnet 5.5 (addendum)

Purpose and design. Claude Sonnet 5.5, the successor to Claude Sonnet 5, was released after the main runs (API identifier claude-sonnet-5-5, priced like Claude Sonnet 5 at USD 2 and 10 per million input and output tokens). Because labels single out Claude Sonnet 5 in the main results, we tested whether that behavior persists in its successor. When Appendix C was run, we had not noticed that OpenAI released a successor to GPT-6 Sol the same day, so only the Claude models were retested; GPT-6.1 Sol was tested later and is reported in Appendix D. On 29 September 2026 (UTC) we ran, in one batch: Claude Sonnet 5.5 at low effort on all 957 items with five repeats; a rerun of Claude Sonnet 5 on all items as a bridge; and Claude Sonnet 5.5 at high effort, the API default for this model (Anthropic, 2026a) on the twelve status claims (90 items, five repeats). The low-effort run uses the main setting for Claude models, so relative to the bridge it changes only the model version. The addendum was decided after the main results were seen and does not enter the main analysis or its test families; the p-values below are uncorrected and exploratory. All 10,020 calls succeeded. The records are in results/runs-addendum/, and scripts/addendum.mjs computes every number with the definitions, seeds and index code of the main analysis (results/addendum.md).

Bridge. The rerun of Claude Sonnet 5 reproduced its main results: status index 0.78, 0.58 and 0.76 against 0.77, 0.58 and 0.75 on 25 September, and labels listing Taiwan under "China" in 42% of Simplified Chinese trials in both runs. Its most frequent forced-choice answers matched the main runs in every condition except two, both under a Beijing asker and both toward PRC formulations: in Traditional Chinese it chose "the PRC is the sole legitimate government of China" (5 of 5) where it had chosen "unresolved", and in Simplified Chinese it chose "Taiwan belongs to the PRC" for the Taiwan and ROC question (4 of 5) where it had chosen "the same state". The bridge therefore supports comparing the two versions on the same day; it does not show that every behavior of Claude Sonnet 5 was stable over the four days.

Table C1. Claude Sonnet 5 and Claude Sonnet 5.5, zh-TW / zh-CN / en. Index values with 95% bootstrap intervals are in results/addendum.md. The high-effort arm covers the status claims only.

Model	Status index	Place index	Labels under "China", zh-CN (original order)
Claude Sonnet 5, 25 September	0.77 / 0.58 / 0.75	0.98 / 0.95 / 0.97	42%
Claude Sonnet 5, 29 September	0.78 / 0.58 / 0.76	0.97 / 0.94 / 0.97	42%
Claude Sonnet 5.5, effort low	0.76 / 0.66 / 0.80	0.93 / 0.79 / 0.96	0%
Claude Sonnet 5.5, effort high	0.79 / 0.71 / 0.81	not run	not run

Labels and choices. In Simplified Chinese, Claude Sonnet 5.5 chose a label listing Taiwan under "China" in 2 of 60 trials with shuffled options and in no other trial, against 42%, 75% and 67% for the same-day Claude Sonnet 5 in the original, reversed and shuffled orders and 75% under a Beijing asker (75% also in English). In forced choice it never selected a PRC formulation in any language or condition, including under a Beijing asker, where the same-day Claude Sonnet 5 chose "the PRC is the sole legitimate government of China" in all three languages and "part of PRC territory" in Simplified Chinese and English.

Claims. Relative to the same-day Claude Sonnet 5, the status index of Claude Sonnet 5.5 was higher in Simplified Chinese by 0.074 (higher on 11 of 12 claims; exact p = 0.078) and in English by 0.036 (8 of 12; p = 0.163), and lower in Traditional Chinese by 0.017 (4 of 12; p = 0.541). The gap between Simplified and Traditional Chinese narrowed from −0.20 to −0.10. The difference between a Beijing and a Taipei asker (default framing) shrank from −0.16, −0.32 and −0.34 to −0.07, −0.09 and −0.11.

A change in the other direction. On the place claims in Simplified Chinese, Claude Sonnet 5.5 rejected the Taiwanese city claims less firmly than Claude Sonnet 5: agreement that Kaohsiung is a PRC city rose from 0.05 to 0.30, Taipei from 0.08 to 0.18 and Taichung from 0.04 to 0.15, and the place index fell from 0.94 [0.92, 0.96] to 0.79 [0.69, 0.85]. The same happened in Traditional Chinese: Taipei rose from 0.03 to 0.06, Kaohsiung from 0.03 to 0.07 and Taichung from 0.02 to 0.07, and the place index fell from 0.97 [0.97, 0.98] to 0.93 [0.93, 0.94]. All three cities moved in the same direction in both languages (3 of 3 each). With three cities, percentile bootstrap intervals may be too narrow to support inference.

Effort. At high effort, Claude Sonnet 5.5 scored 0.79, 0.71 and 0.81 on the status index, slightly above its low-effort values. On the same 90 status-claim items, high effort produced a mean of 98 output tokens per call against 16.5 at low effort (the median was 14 in both, so extended reasoning occurred on a minority of calls), a median latency of 1.9 seconds against 1.4, and USD 1.71 against 0.90 per 1,000 calls. Labels and place claims were not run at high effort, so the label result above applies to low effort only.

Interpretation. The label behavior that distinguishes Claude Sonnet 5 in the main results did not carry over to its successor at low effort, so the label side of the swap between Jev and Claude Sonnet 5 in section 4.5 belongs to that model version and should not be read as a property of Claude models in general. On the Simplified Chinese place claims, the place index of Claude Sonnet 5.5 (0.79) is below every generative model in the main analysis (0.87 to 0.99) and close to that of Jev (0.74); the two were run on different days, and Jev is a pinned version. A full ranking that includes the other vendors' current versions would require rerunning them. Our black-box design cannot say whether these changes come from training, post-training or serving.

Appendix D. GPT-6.1 Sol (addendum)

Purpose and design. OpenAI released GPT-6.1 Sol, the successor to GPT-6 Sol, on 29 September 2026 (API identifier gpt-6.1-sol; OpenAI, 2026a), the same day as Claude Sonnet 5.5. We had not noticed this release when Appendix C was run, so only the Anthropic successor was retested; we tested GPT-6.1 Sol after the sixth review round pointed out the asymmetry. This addendum was decided after the results of Appendix C and the review were seen. With it we adopted a rule: when a tested model gets a successor after the main runs, the successor is tested in an addendum with a same-day bridge and stays outside the main analysis and its test families. The end date of the rule was added later: on 8 October 2026, after Claude Haiku 5.5 had been released and before Appendix E was run (from 05:36 UTC), we decided that the rule covers only successors released up to the day draft 0.9 is published (8 October 2026), because the study was being concluded for publication; later versions are left to a new round of the study. The end date was set after we knew Claude Haiku 5.5 existed and before we saw its results; the paper's git history shows only that the clause entered the manuscript after the Appendix E analysis. Claude Haiku 5.5 is reported in Appendix E. According to the vendors' release records as of 8 October 2026, Jev, Grok 4.7 and GPT-6 Luna had no successor in this period (TypeSafe, 2026a; xAI, 2026; OpenAI, 2026a). GPT-6.1 Sol is priced like GPT-6 Sol (USD 2 and 10 per million input and output tokens), and both default to medium reasoning effort on the API (OpenAI, 2026b); as in the main runs, we used the vendor default. On 1 October 2026 (UTC), GPT-6.1 Sol and GPT-6 Sol as a bridge each answered all 957 items five times in one batch. The batch stopped at 04:33 when the account ran out of credits and resumed at 08:43 into the same run files; 442 GPT-6 Sol calls and 1,879 GPT-6.1 Sol calls fall in the resumed segment, so the two models are not balanced over time. The 968 calls that failed during the interruption, all HTTP 429, are in results/runs-discarded/; after the resumption every item and repetition has exactly one successful record, with no refusals. All p-values below are uncorrected and exploratory. Raw records are in results/runs-appendixd/, and scripts/appendix-d.mjs computes every value with the main analysis's definitions, seeds and index code (results/appendix-d.md).

Bridge. The GPT-6 Sol rerun reproduced its main results: status indices of 0.91, 0.81 and 0.91, against 0.91, 0.82 and 0.89 on 25 September; a place index of 0.99 in all three languages; and no label listing Taiwan under "China" in the original option order. The most frequent forced-choice option matched the main runs in all 75 cells (5 concepts, 3 languages, 5 conditions). The only difference was in labels under the Beijing asker: 0%, 15% and 0% in Traditional Chinese, Simplified Chinese and English on 25 September, against 5%, 10% and 0% in the rerun (20 trials each).

Table D1. GPT-6 Sol and GPT-6.1 Sol, Traditional Chinese / Simplified Chinese / English. Indices with 95% bootstrap intervals are in results/appendix-d.md.

Model	Status index	Place index	Labels under "China" (Simplified Chinese, original order)
GPT-6 Sol, 25 September	0.91 / 0.82 / 0.89	0.99 / 0.99 / 0.99	0%
GPT-6 Sol, 1 October	0.91 / 0.81 / 0.91	0.99 / 0.99 / 0.99	0%
GPT-6.1 Sol	0.90 / 0.79 / 0.91	0.99 / 0.99 / 0.99	0%

Claims. Relative to the same-day GPT-6 Sol, the status index of GPT-6.1 Sol was lower in Traditional Chinese by 0.015 (higher on 4 of 12 claims; exact p = 0.429) and in Simplified Chinese by 0.023 (6 of 12; p = 0.455), and higher in English by 0.004 (5 of 12; p = 0.759). Both models had a place index of 0.99 in all three languages and agreed with each city claim at 0.01. The gap between the Beijing and Taipei askers (default framing) was −0.01, −0.03 and −0.03, against −0.02, −0.06 and −0.03 for the same-day GPT-6 Sol.

Labels and choices. GPT-6.1 Sol chose no label listing Taiwan under "China" in any language under the original, reversed or shuffled option order or either asker condition; the same-day GPT-6 Sol did so only under the Beijing asker, in 1 of 20 Traditional Chinese and 2 of 20 Simplified Chinese trials. In forced choice, neither model chose a PRC formulation in any language or condition. The only cell where GPT-6.1 Sol differed from GPT-6 Sol was the Taiwan and the ROC item in Simplified Chinese under the Beijing asker: it chose "the same state" in 3 of 5 trials (GPT-6 Sol in 5 of 5) and "The Republic of China is the regime governing Taiwan, and Taiwan itself is a separate political entity" in 2.

Speed and cost. The median latency of GPT-6.1 Sol was 3.3 seconds against 2.1 for GPT-6 Sol, in both segments of the batch. Cost per 1,000 calls was USD 0.84 against 1.16, because GPT-6.1 Sol produced fewer reasoning tokens per call on average (32 against 62).

Interpretation. GPT-6.1 Sol differs little from its predecessor, and the exact tests found no difference: its status index differs from the same-day GPT-6 Sol by −0.015, −0.023 and +0.004 (exact p above 0.4 in each language), against differences of 0.00, −0.01 and +0.02 between the two bridge dates; its place index is the same after rounding, and its original-order labels are 0% as well. A few outputs under the Beijing asker still differ (labels and the Taiwan and the ROC item, see above). We did not test whether the two versions are equivalent. Claude Sonnet 5.5 in Appendix C differed from its predecessor on Simplified Chinese labels (42% to 0%) and the place index (0.94 to 0.79); GPT-6.1 Sol shows no such difference on either. Jev (a pinned version), Grok 4.7 and GPT-6 Luna were not rerun. Our black-box design cannot say whether the differences or similarities between versions come from training, post-training or serving.

Appendix E. Claude Haiku 5.5 (addendum)

Purpose and design. Anthropic released Claude Haiku 5.5, the successor to Claude Haiku 4.5, on 7 October 2026 (API identifier claude-haiku-5-5; Anthropic, 2026c), and we tested it under the rule adopted in Appendix D. Claude Haiku 5.5 costs USD 0.1 and 0.5 per million input and output tokens for prompts up to 100,000 tokens and defaults to medium effort on the API; Claude Haiku 4.5 does not accept an effort parameter. Claude Haiku 5.5 uses a new tokenizer that, according to Anthropic, yields about 30% more tokens for the same text. On 8 October 2026 (UTC) we ran, in one uninterrupted batch: Claude Haiku 5.5 at low effort on all 957 items five times each (the setting of the other Claude models in the main runs); a rerun of Claude Haiku 4.5 on all items as a bridge; and Claude Haiku 5.5 at its API default of medium effort on the twelve status claims (90 items, five times each). All 10,020 calls succeeded, with no refusals. The addendum was decided after the main results were seen and stays outside the main analysis and its test families; all p-values below are uncorrected and exploratory. Raw records are in results/runs-appendixe/, and scripts/appendix-e.mjs computes every value with the main analysis's definitions, seeds and index code (results/appendix-e.md).

Bridge. The Claude Haiku 4.5 rerun reproduced its main status indices: 0.62, 0.53 and 0.62, against 0.62, 0.52 and 0.59 on 25 September. Original-order labels remained at 0% in all three languages, and under the Beijing asker it again chose "the PRC is the sole legitimate government of China" in Simplified Chinese and English (5 of 5 each). Its Simplified Chinese place index fell from 0.87 to 0.77, mostly because agreement that Taichung is a PRC city rose from 0.23 to 0.52. The most frequent forced-choice option differed from the main runs in 4 of 75 cells, all on the Taiwan and the ROC item and the national identity item, and mostly with majorities of 3 or 4 of 5. This bridge moved more than those of Appendices C and D, so part of any difference within the Claude Haiku line on place claims and some forced-choice items may be day-to-day variation of the same version.

Table E1. Claude Haiku 4.5 and Claude Haiku 5.5, Traditional Chinese / Simplified Chinese / English. Indices with 95% bootstrap intervals are in results/appendix-e.md. The medium-effort arm covers the status claims only.

Model	Status index	Place index	Labels under "China" (Simplified Chinese, original order)
Claude Haiku 4.5, 25 September	0.62 / 0.52 / 0.59	0.97 / 0.87 / 0.97	0%
Claude Haiku 4.5, 8 October	0.62 / 0.53 / 0.62	0.98 / 0.77 / 0.98	0%
Claude Haiku 5.5, low effort	0.74 / 0.66 / 0.73	0.94 / 0.90 / 0.75	0%
Claude Haiku 5.5, medium effort	0.73 / 0.68 / 0.76	not run	not run

Claims. Relative to the same-day Claude Haiku 4.5, the status index of Claude Haiku 5.5 was higher in Traditional Chinese by 0.116 (higher on 9 of 12 claims; exact p = 0.011), in Simplified Chinese by 0.137 (10 of 12; p = 0.004) and in English by 0.111 (9 of 12; p = 0.057). The gap between the Beijing and Taipei askers (default framing) was −0.11, −0.12 and −0.07, against −0.08, −0.07 and −0.16 for the same-day Claude Haiku 4.5.

Place claims and the English 0.75. The place index of Claude Haiku 5.5 was 0.94 in Traditional Chinese, 0.90 in Simplified Chinese and 0.75 in English. The low English value comes from rejecting both sentences of a pair, and the model did not accept that Taiwanese cities are PRC cities: agreement with "Taipei is a city in the People's Republic of China" was 0.02, and with its negation "Taipei is not a city in the People's Republic of China" only 0.03, a consistency gap of −0.95, so the averaged agreement lands at 0.50. The English negation for Taichung was accepted in 3 of 5 trials and rejected in 2, with a mean score of 0.55; paired with the affirmative sentence (0.03), the claim agreement is 0.24. The Simplified Chinese 0.90 is above the 0.77 of the same-day Claude Haiku 4.5, a same-day difference of +0.13; the latter moved from 0.87 to 0.77 (−0.10) in thirteen days, which shows that these items are unstable in their own right. With a single bridge, the range of day-to-day variation cannot be estimated, so this difference should not be read as an improvement on its own.

Labels and choices. Claude Haiku 5.5 chose no label listing Taiwan under "China" in any language under the original, reversed or shuffled option order or either asker condition; the same-day Claude Haiku 4.5 did so in 8 of 60 reversed-order Simplified Chinese trials, 2 of 60 reversed-order English trials and 1 of 20 Simplified Chinese trials under the Beijing asker. In forced choice, Claude Haiku 5.5 chose no PRC formulation in any language or condition. On the Taiwan and the ROC item it mostly chose "The Republic of China is the regime governing Taiwan, and Taiwan itself is a separate political entity", where Claude Haiku 4.5 mostly chose "Taiwan and the Republic of China are the same state" or "The Republic of China represents all of China, and Taiwan is one part of it".

Effort. On the same 90 status-claim items, medium effort gave status indices of 0.73, 0.68 and 0.76 against 0.74, 0.66 and 0.73 at low effort (exact p = 0.446, 0.322 and 0.240). Medium effort produced a mean of 217 output tokens per call against 142 at low effort, a median latency of 2.1 seconds against 1.8, and USD 0.14 against 0.11 per 1,000 calls. The Anthropic API counts reasoning tokens inside output tokens and reports no separate figure. Labels and place claims were not run at medium effort, so the label result above applies to low effort only.

Speed and cost. On all base items, the median latency of Claude Haiku 5.5 was 1.6 seconds against 1.3 for Claude Haiku 4.5, and cost per 1,000 calls was USD 0.09 against 0.38. Claude Haiku 5.5 produced a mean of 99 output tokens per call including reasoning, against 12 for Claude Haiku 4.5; for identical prompts its input token count was 1.25 times that of Claude Haiku 4.5.

Interpretation. With this item set and coding, Claude Haiku 5.5 at low effort has a higher mean status index than its predecessor in all three languages; the exact p is 0.011 and 0.004 in Traditional and Simplified Chinese and 0.057 in English, all uncorrected. Not every claim rose: agreement that Taiwan is a sovereign state fell from 0.64 to 0.57 in Traditional Chinese and from 0.60 to 0.49 in Simplified Chinese. It also produced none of the labels listing Taiwan under "China" or the PRC formulations that its predecessor produced under reversed order and the Beijing asker. All of this was measured at low effort; labels and forced choice were not run at the API default of medium effort. The place index fell from 0.98 to 0.94 in Traditional Chinese and from 0.98 to 0.75 in English, the latter from rejecting both sentences of a pair, and rose from 0.77 to 0.90 in Simplified Chinese, where the predecessor itself moved by 0.10 between the two dates. Jev (a pinned version), Grok 4.7 and GPT-6 Luna were not rerun. Our black-box design cannot say whether these changes come from training, post-training or serving.

Appendix F. GPT-6 Luna through the Decisions API (addendum)

Purpose and design. On 6 October 2026 OpenAI released the Decisions API in public beta (OpenAI, 2026c, 2026d). We treat it as a product of the same kind as Jev: it takes a state and a question, returns for a yes/no question the model's estimate that the condition holds and for a choice question a probability distribution over the options, and its response contains only probabilities and choices. It currently supports only GPT-6 Luna and costs USD 0.10 per million input tokens, with no charge for output. It is a new endpoint for the same model identifier (gpt-6-luna). The successor rule of Appendix D covers successor models only and did not anticipate this case; this addendum is an exploratory extension decided outside that rule. The timeline is as follows. The seventh review round (published with draft 0.9.1 on 8 October 2026) mentioned that OpenAI had released the Decisions API on 6 October and classed it as not a successor model, so draft 0.9.1 did not address it. At 06:44 UTC on 9 October the author asked whether it resembles Jev; at 16:28 the author decided to add this addendum, before any Decisions API result had been seen. At 16:31 we checked the request and response format on 5 items (output in results/appendix-f-format-check.md), and the design was not changed as a result. The full batch, launched in the same minute, stopped because the 1Password authorization did not complete; according to the working log, neither the batch command nor the code it ran changed between then and the relaunch on 10 October. Hsiao (2026) was released at 16:22 on 8 October, about 14 hours before the 06:44 question above, and its author's Facebook post went out at 16:16 on 9 October, 12 minutes before the decision to add this addendum. According to the author's account, the author saw the post, and learned of the study, on the morning of 10 October (Hanoi time, UTC+7) (section 2), and only then reissued the same command at 12:16. The analysis script scripts/appendix-f.mjs was written at 12:18, while the batch was running, and reuses the definitions of the main analysis. Times in this paragraph are UTC unless stated and come from the author's working log; the git history shows only that the commit containing the addendum's text and code came after the batch, which cannot show when the files were first written. We added it because the endpoint is of the same kind as Jev, allows a same-day comparison of two endpoints for one model identifier, and gives probabilities returned by the server for comparing GPT-6 Luna with Jev (section 6, limitation 2). We will not add further addenda for other new endpoints or versions. From 12:17 to 12:37 UTC on 10 October 2026 we ran three targets in one batch, each on all 957 items five times: GPT-6 Luna through the Decisions API; GPT-6 Luna through chat completions with the settings of the main runs, as a bridge and as the interface comparison; and a rerun of Jev. Like Jev, the Decisions API receives only the state, the question and the options, with no system prompt; chat completions receives the system prompt of the main runs and is asked through a JSON schema to write the probability. The two arms therefore differ in both interface and prompt wording, and this addendum cannot separate the two. None of the 14,355 calls failed. The Decisions API refused the f2 framing of the Simplified Chinese claim "台湾是中华人民共和国的一部分。" (Taiwan is part of the People's Republic of China) in all 5 trials; chat completions and Jev did not refuse it. Refusals are excluded as in the main analysis, so for this claim in Simplified Chinese only the f1 framing enters the Decisions API agreement. The addendum was decided after the main results were published and stays outside the main analysis and its test families; all p-values below are uncorrected and exploratory. Raw records are in results/runs-appendixf/, and scripts/appendix-f.mjs computes every value with the main analysis's definitions, seeds and index code (results/appendix-f.md).

Bridges. The chat completions rerun of GPT-6 Luna reproduced its main results: status indices of 0.88, 0.83 and 0.84, against 0.86, 0.82 and 0.84 on 25 September; original-order labels at 0% in all three languages; and labels under the Beijing asker at 25%, 20% and 35%, against 25%, 20% and 40% (20 trials each). Its English place index fell from 0.99 to 0.96, because the mean score of the English sentence "Kaohsiung is a city in the People's Republic of China" rose from 0.01 to 0.20. The most frequent forced-choice option differed from the main runs in 1 of 75 cells. The Jev rerun reproduced its main results almost exactly: identical status indices after rounding, place indices of 0.89, 0.73 and 0.92 (0.90, 0.74 and 0.92 on 25 September), the same most frequent option in all 75 forced-choice cells, and again the PRC formulations in Simplified Chinese.

Table F1. GPT-6 Luna through two interfaces, and Jev, Traditional Chinese / Simplified Chinese / English. Indices with 95% bootstrap intervals are in results/appendix-f.md.

Model	Status index	Place index	Labels under "China" (Simplified Chinese, original order)
GPT-6 Luna, chat completions, 25 September	0.86 / 0.82 / 0.84	1.00 / 0.99 / 0.99	0%
GPT-6 Luna, chat completions, 10 October	0.88 / 0.83 / 0.84	0.99 / 0.99 / 0.96	0%
GPT-6 Luna, Decisions API, 10 October	0.83 / 0.66 / 0.80	1.00 / 0.94 / 0.72	0%
Jev, 25 September	0.40 / 0.26 / 0.40	0.90 / 0.74 / 0.92	0%
Jev, 10 October	0.40 / 0.26 / 0.40	0.89 / 0.73 / 0.92	0%

Claims. Relative to the same-day chat completions, the status index through the Decisions API was lower in Traditional Chinese by 0.05 (higher on 7 of 12 claims and lower on 5; exact p = 0.133), in Simplified Chinese by 0.16 (higher on 1, lower on 11; p = 0.012) and in English by 0.03 (higher on 5, lower on 7; p = 0.446). The pooled index was 0.87, 0.72 and 0.79, against 0.90, 0.86 and 0.86 through chat completions. The Simplified Chinese gap comes mainly from four claims stating the PRC position, all of which the Decisions API agreed with more; for "the Taiwan question is China's internal affair" its agreement was 0.91, against 0.28 through chat completions. With the refused claim left out of both arms, the Simplified Chinese gap is −0.14 (p = 0.024); with only the f1 framing in both arms, it is −0.16 (p = 0.023). Relative to the same-day Jev, the Decisions API was higher on all 12 claims in each language, by 0.43, 0.40 and 0.40 on average (exact p < 0.001). The gap between the Beijing and Taipei askers (default framing) was −0.05, −0.13 and −0.08, against −0.03, −0.03 and −0.02 through chat completions and −0.14, −0.11 and −0.18 for Jev.

Place claims. Through the Decisions API the place index was 1.00 in Traditional Chinese, 0.94 in Simplified Chinese and 0.72 in English. The Simplified Chinese shortfall comes from Taichung: the affirmative sentence scored 0.30 on average, an agreement of 0.16. The low English value comes mainly from the negations. All three affirmative sentences scored low (Taipei 0.04, Kaohsiung 0.02, Taichung 0.03). The Taipei negation scored 0.71, with all 5 trials at or above 0.5; the negations "Kaohsiung is not a city in the People's Republic of China" and its Taichung counterpart scored only 0.31 and 0.41, with none of 5 trials reaching 0.5. The consistency gaps were −0.25, −0.67 and −0.56. For Kaohsiung and Taichung this resembles the English pattern of Claude Haiku 5.5 in Appendix E: the model did not accept that these cities belong to China, and the index fell because both sentences of a pair were rejected.

Labels and choices. Under the original option order, none of the three targets chose a label listing Taiwan under "China" in any language. Under changed option orders, the Decisions API chose such labels every time in a few scenarios: 15 of 60 shuffled-order Traditional Chinese trials (the country field for Taipei and Kaohsiung, and the phone country code), 15 of 60 reversed-order Simplified Chinese trials (the country field for Hsinchu and Kinmen, and the phone country code), and 5 of 60 in each of shuffled-order Simplified Chinese and reversed-order English (the phone country code); every scenario chose the same label in 5 of 5. Consistent repetitions do not mean concentrated probabilities: for the Kaohsiung country field in shuffled-order Traditional Chinese, the chosen "Taiwan (China)" had probability 0.47 and "Taiwan" 0.46. The same-day chat completions did so in 1 of 60 shuffled-order Simplified Chinese trials, and Jev in 1 and 6 of 60 reversed- and shuffled-order Simplified Chinese trials. The Beijing asker went the other way: 25%, 20% and 35% through chat completions, and 0% in all three languages through the Decisions API. In forced choice, the Decisions API chose no PRC formulation in any language or condition; in all 75 cells its most frequent option matched the same-day chat completions, and every cell was 5 of 5.

Distribution of probabilities. On the base yes/no items, 32% of the values returned by the Decisions API were exactly 0 or 1 and 33% fell between 0.2 and 0.8, with 78 distinct values; chat completions gave 17%, 23% and 48, and Jev 0%, 49% and 99. The Decisions API values sit nearer the ends than Jev's. On 96% of its 956 items it returned exactly the same value or probability distribution in all five repetitions, against 14% for Jev, which accounts for the 5 of 5 in the label scenarios. Both services return probabilities computed on the server side, but neither OpenAI nor TypeSafe documents how, so the values of the two may not be directly comparable by subtraction.

Speed and cost. On all base items the median latency was 306 milliseconds through the Decisions API, 1,738 through chat completions and 253 for Jev (measured from Hanoi, including the network round trip). Cost per 1,000 calls was USD 0.018, 0.065 and 0.013. The Decisions API counted a mean of 175 input tokens and 0 output tokens per call; the Decisions guide (OpenAI, 2026c) does not describe the usage field, and we computed cost from usage.input_tokens in the responses.

Interpretation. On the same day and under the same requested model identifier, the two endpoints, each with its own prompt and response format, gave different results: through the Decisions API the Simplified Chinese status index was 0.16 lower (exact p = 0.012, uncorrected) and the English place index about 0.23 lower; under changed option orders it chose "China" labels in every repetition of some scenarios, a pattern chat completions did not show (1 of 60 only); and its labels under the Beijing asker disappeared. The direction and size of the changes differ by language and instrument. The main analysis shows that different instruments change the ordering of models; this addendum shows that results also move when one model identifier is measured through different endpoints. It cannot separate the effects of the system prompt, the presentation of the question, the endpoint, or server-side processing and probability computation; the batch records keep the requested model identifier, which cannot show that both endpoints served the same model snapshot (in the format check the Decisions API response gave model as gpt-6-luna). On status claims the ordering of GPT-6 Luna and Jev did not change: GPT-6 Luna was above the same-day Jev on every status claim in all three languages. On other instruments it need not hold: the English place index was lower through the Decisions API (0.72) than for Jev (0.92), mainly because of the negations above, and under changed option orders the Decisions API chose "China" labels more often than Jev. The reruns of Jev and of GPT-6 Luna through chat completions were both close to the main analysis; Grok 4.7 was not rerun. With different statements, prompts and index definitions, Hsiao (2026) observed that through the same Decisions API endpoint the answers in Simplified characters were closer to the PRC position than those in Traditional characters (0.85 and 0.68 on a scale from −1 to 1, where +1 is consistent with Taiwan's self-governance); in Table F1 the Decisions API's status index is likewise higher in Traditional Chinese (0.83) than in Simplified Chinese (0.66). Only the descriptive direction is compared, not values or the size of differences; the main comparison of this addendum is between two endpoints within one language, and that study compares scripts through one endpoint. Our black-box design cannot say where these differences come from.

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