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Claude’s expressed values differ, on average, across the models and languages Anthropic studied. Its July 2026 analysis found tendencies toward greater warmth, caution, brevity, or other response styles—but individual conversations vary more than model averages, and the study does not show that Claude has intrinsic beliefs or explain what causes the differences.

What Anthropic means by Claude’s “values”

In this study, values are normative considerations—such as honesty or caution—that appear in Claude’s answers. Anthropic is describing observable response patterns, not claiming that Claude intrinsically holds beliefs. The authors put it plainly: “We do not imply that Claude intrinsically holds values.” Anthropic’s July 13, 2026 study examines what Claude says and does in sampled conversations.

To organize those patterns, the researchers started with 3,307 values identified in earlier Values in the Wild research, manually grouped similar ones into 339 high-level categories, then used dimensionality reduction to summarize how values co-occurred.

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How the study was conducted

Anthropic analyzed 309,815 Claude.ai conversations involving subjective tasks. The conversations were collected over two weeks in May 2026 and sampled across three models—Sonnet 4.6, Opus 4.6, and Opus 4.7—and the 20 most common languages on Claude.ai, with roughly 5,000 conversations per model-language pair. An automated, privacy-preserving analysis labeled high-level values, task, topic, and values expressed by users. The study’s methods and results describe this as an analysis of sampled conversations, not a test of every possible prompt or interaction.

The four axes Anthropic used

The analysis summarizes value patterns along four dimensions. They are not mutually exclusive personality traits: an answer can be warm and rigorous at once. Each axis indicates which cluster was more prominent in the measured pattern.

  • Deference vs. caution: accommodating a user’s preferences versus emphasizing responsible guidance and harm reduction.
  • Warmth vs. rigor: positive framing, encouragement, and care versus accuracy, precision, and transparency.
  • Depth vs. brevity: nuanced, detailed explanation versus concise compliance with a request.
  • Candor vs. execution: foregrounding uncertainty or errors versus producing polished, confident output.

Together, these four axes captured 15% of the total variance in values across conversations after controlling for task, topic, and values expressed by the user, according to Anthropic. They are therefore a compact summary, not a complete account of the differences among responses.

How the three models differed

Anthropic reports these average tendencies across the conversations it analyzed:

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Model Reported tendencies Examples Anthropic associates with the pattern
Sonnet 4.6 More deference, warmth, and brevity More likely to affirm a user’s ideas, mirror their tone, use humor, and offer comfort
Opus 4.6 Deference, rigor, brevity, and execution Anthropic describes this as its own profile; the cited summary does not attach a separate set of specific behaviors
Opus 4.7 More caution, rigor, depth, and candor More likely to critique work candidly or offer unsolicited risk warnings

These are tendencies, not guarantees about an individual answer. Anthropic says average differences between models are small compared with variation from one conversation to another, even though the patterns are structured and detectable. The authors suggest character training and other fine-tuning choices may contribute, but their analysis does not isolate a cause.

How expressed values varied by language

Across the 20 languages in the sample, Anthropic reports different average profiles. Hindi and Arabic leaned most toward warmth; English and Russian leaned most toward rigor. Arabic showed the strongest deference and brevity, while English showed the strongest caution and depth. Dutch leaned furthest toward candor, and Indonesian toward execution. These are rankings in Anthropic’s sample—not inherent properties of a language or its speakers.

Training-data quantity and composition are possible contributors, Anthropic says, but the study does not establish that explanation. Nor does it determine which profile users in a language community prefer. Conversational norms may differ, and the authors say it remains unclear how much variation is desirable.

What the findings do—and do not—show

  • They show: measurable average differences in expressed-value patterns among the sampled model-language pairs.
  • They do not show: that every response follows its model’s average profile, that language itself causes a shift, or that one profile is better.
  • They do not establish: effects on user trust, wellbeing, or decision quality. Anthropic identifies these as possible subjects for future study, alongside training stages and cultural context.

Anthropic points to system-card evaluations as related evidence that Claude’s behavior can vary across languages, including in knowledge and refusal behavior. Those evaluations concern different outcomes; they are not measurements of the four value axes.

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Expressed values versus Claude’s intended values

Anthropic’s constitution and the values study answer different questions. The constitution describes intended guidance and behavior; the study measures patterns in sampled outputs. Anthropic calls its 2026 constitution “a detailed description of Anthropic’s vision for Claude’s values and behavior.” The company says the document is written for mainline, general-access Claude models and that it will report cases where behavior departs from its intentions. Anthropic’s constitution announcement sets out that intended scope; it should not be read as evidence that every response conforms to the guidance.

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