Claude Values Across Models and Languages — Anthropic’s Four-Axis Study (July 2026)
Anthropic Jul 13, 2026: 309K conversations, 3,307 values compressed into four axes — Deference vs Caution, Warmth vs Rigor, Depth vs Brevity, Candor vs Execution. explainx.ai maps model and language shifts for builders.
Claude's constitution names honesty and warmth. In the wild, it expresses 3,000+ distinct values — and they shift by model and language.
On July 13, 2026, Anthropic published Claude's values across models and languages — a follow-up to Values in the Wild that compresses thousands of labeled norms into four measurable axes, then maps 309,815 anonymized Claude.ai conversations across Sonnet 4.6, Opus 4.6, and Opus 4.7 and the top 20 languages.
explainx.ai breaks down what each axis means, why Sonnet feels playful while Opus 4.7 critiques, why Hindi feedback may sound warmer than Russian, and what agent builders should measure — building on Constitutional AI and Teaching Claude why.
Warmth vs Rigor — Hindi/Arabic warmest · English/Russian most rigorous
Same user, same task, two languages?
Different value lean — e.g. business-plan feedback may feel harsher in Russian than Hindi
Is this deployed tooling?
Research paper — proposed for eval/monitoring, not a public dashboard yet
Link to constitution training?
Yes — axes may eventually trace back to character training and data choices
Why 3,000 values needed four axes
Anthropic's prior Values in the Wild work tagged 700,000+ conversations with 3,000+ distinct norms — honesty, warmth, brevity, harm reduction, and hundreds more. Comparing them one-by-one is unreadable.
Label each conversation for presence/absence of all 339 values via a privacy-preserving Claude-based tool
Control for task, topic, and user-expressed values
Reduce dimensionality → four axes that capture co-occurring value groups
Values appearing in more than 80% of chats (helpfulness, clarity, following instructions) were dropped so the analysis measures variation, not universals.
The four axes (with end-label examples)
Axis
Low end (examples)
High end (examples)
Deference vs Caution
Accommodation, respect for preferences
Responsible guidance, harm reduction
Warmth vs Rigor
Positive framing, encouragement
Accuracy, transparency, efficiency
Depth vs Brevity
Nuance, critical thinking, empowerment
Brevity, compliance, staying scoped
Candor vs Execution
Intellectual honesty, humility
Results orientation, optimization
explainx.ai read: These are spectrums, not mutually exclusive switches. Claude can be warm and rigorous in one chat — but expressing more of one side correlates with less of the other in practice.
Model value profiles — Sonnet vs Opus
Differences are small in standard deviations but structured and align with how users describe the models online and inside Anthropic.
Sonnet 4.6 — warm, deferential, brief
Axis
Lean (σ from mean)
Distinctive behaviors
Deference
+0.14
Affirms user ideas and work
Warmth
+0.17
Humor, playfulness, comfort without judgment
Brevity
+0.14
Mirrors tone; creative flourishes
Matches Anthropic's launch characterization of Sonnet 4.6 as warm and prosocial — and explains why many developers default to Sonnet for pairing-styleClaude Code sessions.
Middle sibling — less warmth theater than Sonnet, less unsolicited caution than Opus 4.7.
Language profiles — Hindi warmth, English rigor
Anthropic equal-sampled the 20 most common Claude.ai languages across all three models. Largest cross-language spread: Warmth vs Rigor and Candor vs Execution. Most stable: Deference vs Caution and Depth vs Brevity.
Language
Strongest leans
Observable behaviors
Hindi
Warmth (furthest)
Polite language, affirmations, humor
Arabic
Warmth, deference, brevity
Accommodating tone; shorter answers
English
Caution, rigor, depth
Challenges assumptions; refines details
Russian
Rigor (furthest)
Corrects details; asks for evidence
Dutch
Candor
Owns errors openly
Indonesian
Execution
Action-oriented, optimized outputs
Practical implication: Two founders reviewing the same business plan — one in Hindi, one in Russian — may get different perceived harshness even with identical prompts. That is not hypothetical; it is what the axis averages predict.
For India's #2 Claude market (INR pricing rollout) and Indic-model alternatives, this research adds a behavioral dimension beyond token cost: which language channel you standardize on changes the "personality" users experience.
Anthropic notes training data quantity and composition likely drive part of the gap — languages with more professional text may skew rigor; scarce locales may not receive the same character training fidelity as English.
What this is not
Limitation
Why it matters for builders
15% variance explained
Most behavioral spread still lives outside these four axes
Subjective tasks only
Coding agents and tool-use loops may profile differently
May 2026 snapshot
New models (Fable, Sonnet 5 rumors) are not in this sample
No user outcome data yet
Values measured, not trust/wellbeing impact
Desirability unsettled
Warmer Hindi may be culturally appropriate — or a training gap
Do not treat axis positions as Goodhart targets without human review — see specification gaming.
What builders should do now
1. Match model to value need
Use case
Model lean
explainx.ai pick
Onboarding, coaching, creative drafts
Warmth + deference
Sonnet 4.6
Code review, security review, red-team
Caution + candor
Opus 4.7
Fast scoped answers
Brevity + execution
Opus 4.6
2. Stratify evals by language
If you ship multilingual products, do not English-only eval and assume parity. Run the same rubric in Hindi, Arabic, English, and Russian — the languages with the widest axis spread in the paper.
3. Log value-relevant failures in production
Anthropic proposes pre-ship and post-deploy profiling. Until that ships, teams can approximate with:
Tag incidents: false reassurance (deference without caution) vs excessive hedging (candor without execution)
Version prompts and agent skills when you intentionally steer warmth or rigor
4. Connect to alignment training — not just prompts
This paper sits beside Teaching Claude why and J-space interpretability: Anthropic is building measurement for traits it also trains via constitution and character data. Value axes may eventually tell you which training stage moved the needle — not just that outputs changed.
What Anthropic plans next
The paper sketches open questions:
Trace shifts to specific data mixes and training stages
User studies correlating value profiles with trust and decision quality
Norm-setting per language — how much variation communities want
Steering tests — character training and system prompts, verified on axes
Eval integration — value profiling as a standard pre-release gate
For enterprise buyers, this is the strongest public signal yet that "same Claude" ≠ same experience across model SKU and locale — relevant to private benchmark design.
Summary
Anthropic's July 13, 2026 value study compresses 3,307 expressed norms into four axes — Deference vs Caution, Warmth vs Rigor, Depth vs Brevity, Candor vs Execution — measured on 309K+ conversations. Sonnet 4.6 skews warm and affirming; Opus 4.7 skews cautious and candid; Hindi and Arabic skew warmth while English and Russian skew rigor. The work does not yet say which shifts are bugs — but it gives builders a concrete eval vocabulary beyond "helpful/harmless" and a reason to stratify multilingual QA before you trust Claude for high-stakes feedback.
Value axis positions and model list reflect Anthropic's July 2026 publication. Production behavior depends on harness, system prompts, and tools — not conversation sampling alone.
