Detailed Analysis
A Reddit user in the r/Anthropic community has reported that Claude Opus 4.7, released on April 15, 2026, exhibits an unwanted language-mixing behavior in which random Ukrainian or Russian words appear within otherwise Polish-language conversations. The user notes that this problem was previously observed with Sonnet 4.5 but appeared to have been resolved in Opus 4.6, during which no such instances were recalled. The regression to Opus 4.7 has prompted frustration, with the user sharing a screenshot as evidence of the issue. The post has drawn attention because it points to a specific, reproducible quality-control concern in a model that Anthropic has otherwise positioned as a high-performance frontier release.
The broader context of Opus 4.7's launch underscores why this report is noteworthy. Anthropic has marketed the model primarily on the strength of its agentic coding capabilities, boasting benchmark results such as 64.3% on SWE-bench Pro and 87.6% on SWE-bench Verified, along with a 1 million token context window and enhanced self-verification behaviors. One of the headline improvements cited is sharper, more literal instruction adherence — making the emergence of unsolicited language substitutions particularly ironic, as injecting foreign vocabulary into a user's chosen language represents a direct failure of instruction-following at a fundamental level. Official documentation and third-party coverage have not acknowledged this issue, suggesting either that it has not yet risen to Anthropic's attention at scale or that it affects a narrow set of linguistic edge cases.
The phenomenon of cross-language contamination in large language models is a well-documented challenge rooted in the structure of multilingual training corpora. Slavic languages such as Polish, Ukrainian, and Russian share significant lexical and grammatical overlap, and models trained on large web datasets may encode statistical associations between these languages that surface under certain prompt conditions. The fact that this behavior disappeared in Opus 4.6 and reappeared in Opus 4.7 suggests that changes to the training data mixture, fine-tuning objectives, or reinforcement learning from human feedback introduced during the 4.7 development cycle inadvertently disturbed the language-boundary calibration that had been achieved in the prior version. Such regressions are a known hazard in iterative model development, where improvements in one capability domain can degrade performance in another.
This report fits into a broader pattern of user-identified behavioral regressions that accompany major model transitions, often surfacing in communities like r/Anthropic before formal acknowledgment by developers. Non-English speakers, and particularly speakers of lower-resource or closely related languages, disproportionately bear the burden of such issues, as the majority of safety evaluations and quality-control benchmarks skew toward English-language performance. Anthropic's stated emphasis on instruction adherence as a key Opus 4.7 differentiator will likely make this report harder to dismiss, since unprompted language substitution is a direct counterexample to that claim. Whether this constitutes a systemic flaw or an isolated edge case will depend on how reproducibly other Polish-speaking users can replicate the behavior across varied prompts and conversation lengths.
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