The verdict
Voxtral appears in 1 AI-ranked category.
Positioning brief — for the Voxtral team
Why the models put Voxtral at #6 for open-source speech-to-text model
- Whisper-class-or-better multilingual transcription Claude · Grok“Whisper-class-or-better multilingual transcription”
- transcription and downstream comprehension Claude“one open model covers both transcription and downstream comprehension”
- efficient balance of performance and deployability Grok“efficient balance of performance and deployability for multilingual or general use”
What the models credit OpenAI Whisper (#1) with — and don’t credit Voxtral
- robustness to accents and noisy audio Claude · GPT · Grok“strong robustness to accents and noisy real-world audio”
- unmatched ecosystem Claude · Gemini · GPT · Grok“an unmatched ecosystem (faster-whisper, whisper.cpp, WhisperX for diarization/alignment, endless fine-tunes)”
- broad hardware support Claude · GPT“broad hardware support”
What would move the rank — the models’ fix lines, unified
- overkill in VRAM and cost Claude“overkill in VRAM and cost versus Parakeet”
- fewer mature deployment tools Grok“fewer mature deployment tools/forks than established options like Whisper”
- variable real-world testing depth Grok“variable real-world testing depth”
Restructured from verbatim model output · nothing invented · every quote machine-verified
Apache-2.0 speech-LLM (3B Mini and 24B) that pairs Whisper-class-or-better multilingual transcription with genuine audio understanding — Q&A, summarization, and function-calling straight from speech — so one open model covers both transcription and downstream comprehension; assumed the practitioner values this consolidation, which is what lifts it over pure-ASR rivals.
Grok Competitive high accuracy among open weights (top-tier WER in evaluations); efficient balance of performance and deployability for multilingual or general use; newer architectures offer strong value for practitioners seeking alternatives to NVIDIA/IBM dominance.
Where Voxtral falls short, per the models
- Claude Heavier than dedicated ASR models for plain transcription — if you only need text out, a 24B (or even 3B) LLM-based stack is overkill in VRAM and cost versus Parakeet.
- Grok Emerging ecosystem with potentially fewer mature deployment tools/forks than established options like Whisper; variable real-world testing depth.
Top alternatives per the models: OpenAI Whisper · NVIDIA Parakeet TDT · NVIDIA Canary-Qwen 2.5B · IBM Granite Speech
Watch Voxtral
Boards re-poll weekly and the models change their minds. One short email only when Voxtral's standing moves — a rank change, a rival overtaking, or new reasoning from the models. Nothing otherwise.
Embed your ranking badge
Voxtral ranks #6 for best open-source speech-to-text model by AI-model consensus. Put the badge in your README, docs or site — it updates automatically as the models re-rank.
[](https://modelsagree.com/best/best-open-source-speech-to-text-model?utm_source=badge&utm_medium=embed&utm_campaign=badge-voxtral)<a href="https://modelsagree.com/best/best-open-source-speech-to-text-model?utm_source=badge&utm_medium=embed&utm_campaign=badge-voxtral"><img src="https://modelsagree.com/badge/voxtral.svg" alt="Voxtral — ranked #6 for Best open-source speech-to-text model by AI models on ModelsAgree" height="28"></a>Rankings are computed from what the models answer, re-polled on demand · raw reasoning shown verbatim · methodology