Top picks for Transcript Cleanup (2026)

Which should you use? Anthropic: Claude Opus 4.7 (batch) tops this ranking on blended score. If cost drives the decision, DeepSeek: DeepSeek V4 Pro 0423 is the cheapest of the leaders at $1.04/M input.

For Transcript Cleanup, we weight models on context window, low cost. Scores combine each model's public specs with independent benchmark results (Aider Polyglot coding scores, Artificial Analysis intelligence/coding/agentic indices) and live pricing. See full methodology →

Transcript Cleanup is the process of converting raw automatic speech recognition (ASR) output into grammatically correct, punctuated, readable prose. You need this task when ASR systems produce unpunctuated runs of words with repeated filler words, missing capitalization, or transcription errors that obscure meaning. A good model understands context deeply enough to add proper punctuation, fix obvious transcription mistakes (like "uh" vs "a"), and preserve speaker intent without hallucinating content. Poor models either over-correct and change meanings, or leave obvious errors intact. The main trade-off is latency: models that achieve 95%+ accuracy typically process at 2-4x slower speeds than basic regex-based cleanup, which matters for live meeting transcripts versus batch processing.

When to use: Use this when you have meeting recordings, interviews, podcasts, or lecture transcriptions that came from an automated speech-to-text service and need to be readable for publication, archiving, or distribution without manual proofreading.