Recent advances in speech‐enabled AI, including Google’s NotebookLM and OpenAI’s speech-to-speech API, are driving widespread interest in voice interfaces across sectors such as finance, health, agritech, legal services, and call‐centers in the global north and south. Despite this momentum, there exists no publicly available application-specific model evaluation that caters to Africa’s linguistic diversity. We present AfriSpeech‑MultiBench, the first domain‐specific evaluation suite for over 100 African English accents across 10+ countries and seven application domains: Finance, Legal, Medical, General dialogue, Call Center, Named Entities, and Hallucination Robustness. We benchmark a diverse range of open, closed, unimodal ASR and multimodal LLM-based speech recognition systems using both spontaneous and non-spontaneous speech conversations drawn from various open African accented English speech datasets. Our empirical analysis reveals systematic variation: open‐source ASR excels in spontaneous speech contexts but degrades on noisy, non‐native dialogue; multimodal LLMs are more accent‐robust yet struggle with domain‐specific named entities; proprietary models deliver high accuracy on clean speech but vary significantly by country and domain. Smaller models fine‐tuned on African English achieve competitive accuracy with lower latency, a practical advantage for deployment. By releasing this benchmark, we empower practitioners and researchers to select voice technologies suited to African use‐cases, fostering inclusive voice applications for undeserved communities.