Why Is GPT-5.2 Making Basic Spelling Mistakes? Experts Investigate AI Regression

Despite being marketed as OpenAI’s "smartest and longest-thinking" model to date, GPT-5.2 has drawn widespread user concern after numerous reports emerged of it making basic spelling mistakes that were uncommon in earlier versions like GPT-3.5. Users on platforms like Reddit have documented instances of the AI misspelling common words such as "definitely," "separate," and "accommodate"—errors that even basic spellcheckers would flag. This regression in linguistic precision has sparked a broader debate: Has the pursuit of deeper reasoning and creative output come at the cost of fundamental language accuracy?

While OpenAI has not officially addressed the issue, internal documentation and research trends suggest a possible explanation. According to the foundational work on GPT-3 published by OpenAI on GitHub, early models were trained with a strong emphasis on statistical language modeling and token-level accuracy, prioritizing fluency and grammatical correctness as core metrics. GPT-5.2, however, appears to have been optimized for extended reasoning chains and multi-step problem solving—a shift that may have inadvertently diluted the model’s focus on low-level lexical precision.

One theory gaining traction among AI researchers is that the model’s increased use of "thinking" tokens—extended internal reasoning phases designed to simulate human-like deliberation—may be introducing noise into its output generation pipeline. In earlier models, the decoding process favored high-probability tokens, ensuring consistent spelling. In GPT-5.2, however, sampling strategies may have been adjusted to encourage creativity, diversity, and stylistic variation, inadvertently increasing the likelihood of low-probability, incorrect token selections—even for frequently used words.

Additionally, changes in training data composition could be a contributing factor. Although OpenAI has not disclosed the exact composition of GPT-5.2’s training corpus, industry analysts note that recent models have incorporated more conversational, user-generated, and informal text sources—including social media, forums, and unedited blogs—to improve contextual understanding. These sources often contain nonstandard spellings, intentional misspellings, and slang, which may have subtly influenced the model’s probabilistic outputs.

Some engineers suggest that the issue may also be related to post-training alignment processes. As models are fine-tuned to follow complex instructions and adopt nuanced tones, the reinforcement learning signals may prioritize "naturalness" or "personality" over strict orthographic correctness. In essence, the model may be learning to sound more human—not by being more accurate, but by mimicking human error patterns.

This phenomenon echoes broader concerns raised in a 2026 opinion piece in The New York Times, which warned that OpenAI’s rapid iteration cycle may be replicating the trade-offs seen in early social media platforms: prioritizing engagement and novelty over reliability and consistency. The article noted that users initially praised AI assistants for their "human-like" quirks, but later grew frustrated when those quirks manifested as unreliable outputs—especially in professional or academic contexts.

For now, users are advised to treat GPT-5.2’s output with the same editorial scrutiny as any other automated tool. While its reasoning capabilities may be unprecedented, its spelling remains fallible. OpenAI has yet to release a patch or official statement, but given the public outcry, a hotfix or update to decoding parameters may be imminent. Until then, the paradox remains: the most intelligent AI ever built is making mistakes a third-grader would avoid.