Observed Signal · Mar 23, 2026 · Product Comparison · Source: DEV Community · Impact: 1/5 · Sentiment: Positive
Cheapest Audio Transcription APIs Compared (2025)
This technical guide compares leading audio transcription APIs in 2025 — IteraTools, AssemblyAI, Deepgram, OpenAI Whisper API and Groq Whisper — across price, accuracy, language support, diarization, timestamps and developer experience. The article includes a feature/price comparison table and runnable examples (curl and Python) for IteraTools, demonstrating URL and file uploads plus word-level timestamps in responses. It notes Whisper-based services offer broad multilingual coverage (99+ languages) while vendors with custom models (AssemblyAI, Deepgram) may deliver stronger English/domain accuracy and speaker diarization. The author concludes IteraTools offers the best cost / multi-language balance (~$0.003/min with word timestamps) while recommending AssemblyAI or Deepgram for English-first use cases requiring diarization.
Developer-focused comparison of transcription APIs; relevant to content, captioning and audio workflows for publishers and creators but not industry-shifting for core AdTech/MarTech.
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Key Takeaways & Evidence Grounding
- IteraTools transcription priced at approximately $0.003 per minute, supports 99+ languages (Whisper), provides word-level timestamps, and does not provide speaker diarization.
- AssemblyAI transcription priced at $0.01 per minute, supports 99+ languages, and offers speaker diarization and timestamps using custom models.
- Deepgram transcription priced at about $0.0043 per minute, supports 36 languages, and offers speaker diarization and timestamps with custom models.
- OpenAI Whisper API listed at $0.006 per minute and Groq Whisper at $0.002 per minute (Groq uses Whisper large-v3); the article includes curl and Python examples for IteraTools demonstrating API usage and SRT generation from word timestamps.
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Compare Speech-to-Text Per-Minute Pricing (EU 2026)
Practical guidance for EU startups on shortlisting and benchmarking external speech-to-text (STT) APIs for production use in 2026. The article recommends gating vendors by verified per-minute cost plus minimum billing unit, language and vocabulary quality, asynchronous workflows, EU data handling, and measured latency (p95). It emphasises modeling billing granularity clip-by-clip, running a representative benchmark corpus, and precommitting weights for cost/quality/latency scoring. Four STT candidates (OpenAI, Deepgram, AssemblyAI, Google Cloud) are named for the audio shortlist, while a separate post-processing model comparison may include Anthropic, Google (Gemini), OpenRouter, Together AI and others. The piece includes an executable TypeScript benchmark that models billed seconds, estimated cost, quality, p95 latency, and eligibility gates.
Microsoft Launches MAI-Transcribe-2, Cheapest and Most Accurate in Market
Microsoft AI has launched MAI-Transcribe-2, a speech-to-text model that supports 60 languages and achieves top accuracy on the FLEURS benchmark with a 5.2% average word error rate. The model is 10x faster than OpenAI's GPT-Transcribe, 7x faster than ElevenLabs' Scribe v2, and 5x faster than Google's Gemini 3.5 Transcribe. It offers features like speaker diarization, word-level timestamps, keyword biasing, and code-switching. Priced at $0.10 per audio hour (promotional until end of year), it undercuts competitors significantly. For Thai, it achieves a 3.4% word error rate, besting Gemini 3.5 Transcribe (3.8%) and Whisper v3-large (8.7%). The model is available via Microsoft Foundry, MAI Playground, and OpenRouter, and is part of Microsoft's strategy to replace OpenAI technologies with in-house models.
Benchmark: Apple SpeechAnalyzer vs OpenAI Whisper
A technical benchmark comparing Apple's SpeechAnalyzer API and OpenAI's Whisper across latency, accuracy, resource usage, multilingual support, customization, cost, and deployment considerations. The analysis finds SpeechAnalyzer (on-device via Core ML on A16 Bionic) delivers lower latency (0.8–1.2s/min audio), smaller memory footprint (50–80MB), and stronger accuracy in clean and noisy LibriSpeech tests (2.1% WER clean; 5.4% WER noisy). Whisper shows higher latency (1.5–2.5s/min), much larger memory use (400–800MB), broader multilingual coverage (100+ languages), and better robustness to non-native accents. The article outlines recommended use cases, constraints (e.g., SpeechAnalyzer lacks custom acoustic models; Whisper often requires cloud/internet), and practical trade-offs for developers.
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