Best open-source speech-to-text model
4 models · updated 2026-07-15
The verdict
OpenAI Whisper leads — 2 of 4 models rank OpenAI Whisper the top pick.
Not unanimous: ChatGPT picks Qwen3-ASR; Grok picks NVIDIA Canary-Qwen 2.5B.
As of 2026-07-15, ChatGPT, Claude, Gemini, Grok collectively rank OpenAI Whisper first for open-source speech-to-text model on modelsagree.com.
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Combined ranking
- 1GPT #3Claude #1Gemini #1Grok #3
Still the default choice for the typical practitioner in 2026 — ~99-language coverage, strong robustness to accents and noisy real-world audio, permissive MIT license, and an unmatched ecosystem (faster-whisper, whisper.cpp, WhisperX for diarization/alignment, endless fine-tunes) that means it runs everywhere from a phone to a server; ranked on total practical value, not raw leaderboard WER, where newer models beat it.
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Claude Still the default choice for the typical practitioner in 2026 — ~99-language coverage, strong robustness to accents and noisy real-world audio, permissive MIT license, and an unmatched ecosystem (faster-whisper, whisper.cpp, WhisperX for diarization/alignment, endless fine-tunes) that means it runs everywhere from a phone to a server; ranked on total practical value, not raw leaderboard WER, where newer models beat it.
Gemini Unmatched multilingual coverage across 99+ languages and a dominant open-source ecosystem of optimized runtimes (like whisper.cpp and faster-whisper) that make it the industry standard for general-purpose deployment.
GPT The safest general-purpose choice thanks to robust multilingual recognition, translation, mature tooling, broad hardware support, permissive licensing, and an excellent speed-quality trade-off
Grok Proven multilingual powerhouse (99+ languages) with strong real-world robustness to accents, noise, and long-form audio; massive ecosystem, easy fine-tuning, and forks (e.g., faster-whisper) make it accessible and valuable for typical diverse-language practitioners despite not always topping pure English leaderboards.
Where it falls shortper GPT It trails newer specialists on raw accuracy and can hallucinate text during silence or difficult audio
per Claude Slow and compute-hungry at full size with no native streaming, and it notoriously hallucinates text on silence or music — long-form production use requires VAD chunking and an optimized runtime rather than the vanilla repo.
per Gemini High computational latency and susceptibility to hallucination loops or repetition during periods of background noise or silence.
per Grok Slower inference on standard hardware without heavy optimization; can struggle with very domain-specific jargon or highest English precision vs. newer hybrids.
- 2GPT #2Claude #2Gemini —Grok #4
Near-tie for practical first place; exceptionally fast, accurate transcription with punctuation, capitalization, timestamps, long-audio support, modest hardware needs, and 25 European languages
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GPT Near-tie for practical first place; exceptionally fast, accurate transcription with punctuation, capitalization, timestamps, long-audio support, modest hardware needs, and 25 European languages
Claude The speed-accuracy king — near-top of the Hugging Face Open ASR Leaderboard while transcribing hours of audio in seconds (RTF orders of magnitude better than Whisper large), CC-BY licensed, with accurate word-level timestamps and punctuation; v3 extended it to ~25 European languages, making it the best pick for high-volume English/European transcription pipelines.
Grok Outstanding speed/throughput (high RTFx) and low-latency streaming suitability while maintaining competitive accuracy; ideal for real-time or batched production use cases common to practitioners; strong NVIDIA ecosystem integration.
Where it falls shortper GPT Its language coverage is largely European, making it unsuitable for many Asian, African, and Middle Eastern deployments
per Claude Language coverage is still a fraction of Whisper's, and it lives in the NVIDIA NeMo ecosystem — smoothest on NVIDIA GPUs, with less community tooling for exotic deployment targets.
per Grok Trades some peak accuracy for speed; less strong on broad multilingual support than Whisper.
- 3GPT #5Claude #4Gemini —Grok #1
Tops or near-tops Hugging Face Open ASR Leaderboard with ~5.63% avg WER on English benchmarks via strong Conformer + Qwen LLM decoder hybrid (SALM architecture); excellent accuracy for typical practitioner use in clean-to-moderate noisy English audio; strong community/HF support and NVIDIA tooling for deployment.
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Grok Tops or near-tops Hugging Face Open ASR Leaderboard with ~5.63% avg WER on English benchmarks via strong Conformer + Qwen LLM decoder hybrid (SALM architecture); excellent accuracy for typical practitioner use in clean-to-moderate noisy English audio; strong community/HF support and NVIDIA tooling for deployment.
Claude Took the top WER spot on the Open ASR Leaderboard at release with a hybrid ASR+LLM design that also enables post-transcription tasks (summarization, Q&A), CC-BY licensed and commercially usable — near-tie with Parakeet, which wins above it only on throughput per dollar for bulk transcription.
GPT Exceptional English accuracy, punctuation, capitalization, hallucination robustness, and optional transcript summarization or question answering
Where it falls shortper GPT It is English-only, relatively large, NeMo-dependent, and optimized around audio segments no longer than roughly 40 seconds
per Claude English-only, which disqualifies it for the many practitioners who need multilingual support (its multilingual Canary 1B sibling covers fewer languages at lower accuracy).
per Grok Higher latency/compute needs than lighter CTC/TDT models; not ideal for real-time streaming or very low-resource/edge devices without optimization.
- 4GPT —Claude —Gemini #2Grok #2
Top-tier accuracy on open benchmarks under an Apache 2.0 license, offering a non-autoregressive variant for massive throughput and a Plus model that natively supports speaker attribution and timestamps.
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Gemini Top-tier accuracy on open benchmarks under an Apache 2.0 license, offering a non-autoregressive variant for massive throughput and a Plus model that natively supports speaker attribution and timestamps.
Grok Very close to top accuracy (~5.85% WER), excels in enterprise English ASR plus built-in translation capabilities; robust for production with good handling of varied domains; open weights suit self-hosting practitioners needing reliability over raw speed.
Where it falls shortper Gemini Very limited multilingual support, optimized primarily for English and five other major languages.
per Grok Larger size leads to higher inference costs/latency; less optimized for extreme multilingual or ultra-low-latency scenarios compared to specialists.
- 5GPT #1Claude —Gemini —Grok —
Best overall accuracy-capability balance; strong multilingual and noisy-audio recognition across 30 languages and 22 Chinese dialects, plus long-form, streaming, language detection, songs, vLLM serving, and Apache-2.0 weights
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GPT Best overall accuracy-capability balance; strong multilingual and noisy-audio recognition across 30 languages and 22 Chinese dialects, plus long-form, streaming, language detection, songs, vLLM serving, and Apache-2.0 weights
Where it falls shortper GPT Timestamps require a separate 0.6B forced-aligner, increasing memory and deployment complexity
- 6GPT —Claude #3Gemini —Grok #5
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.
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Claude 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 it falls shortper 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.
per Grok Emerging ecosystem with potentially fewer mature deployment tools/forks than established options like Whisper; variable real-world testing depth.
- 7GPT —Claude —Gemini #3Grok —
Delivers state-of-the-art accuracy for English and 25 European languages using FastConformer architecture, often outperforming Whisper on word error rate (WER) and speech translation.
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Gemini Delivers state-of-the-art accuracy for English and 25 European languages using FastConformer architecture, often outperforming Whisper on word error rate (WER) and speech translation.
Where it falls shortper Gemini Deeply tied to NVIDIA's heavy NeMo framework, making CPU-only or lightweight edge deployment highly impractical.
- 8GPT —Claude —Gemini #4Grok —
Exceptionally fast non-autoregressive model (up to 15x faster than Whisper-large) that natively provides language identification, speech emotion recognition, and audio event detection.
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Gemini Exceptionally fast non-autoregressive model (up to 15x faster than Whisper-large) that natively provides language identification, speech emotion recognition, and audio event detection.
Where it falls shortper Gemini Highly localized language support, performing well only for Mandarin, Cantonese, English, Japanese, and Korean.
- 9GPT #4Claude —Gemini —Grok —
Strong multilingual accuracy across 14 languages, Apache-2.0 licensing, and straightforward Transformers integration earn it a close place behind Whisper
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GPT Strong multilingual accuracy across 14 languages, Apache-2.0 licensing, and straightforward Transformers integration earn it a close place behind Whisper
Where it falls shortper GPT Gated model access and a younger, less battle-tested ecosystem reduce its practical value
- 10GPT —Claude #5Gemini —Grok —
The best open option for real-time streaming — purpose-built for low-latency (~0.5-2.5s delay) transcription with semantic VAD and word timestamps, CC-BY-4.0, scaling to hundreds of concurrent streams per GPU; fills the production niche (voice agents, live captions) that batch-oriented Whisper-style models handle poorly.
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Claude The best open option for real-time streaming — purpose-built for low-latency (~0.5-2.5s delay) transcription with semantic VAD and word timestamps, CC-BY-4.0, scaling to hundreds of concurrent streams per GPU; fills the production niche (voice agents, live captions) that batch-oriented Whisper-style models handle poorly.
Where it falls shortper Claude Covers only English and French at launch-model quality, so it's a specialist tool for live voice products, not a general transcription workhorse.
- 11GPT —Claude —Gemini #5Grok —
Designed specifically for low-latency edge deployment by using a dynamic input window that scales compute with audio length rather than forcing a fixed 30-second padding.
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Gemini Designed specifically for low-latency edge deployment by using a dynamic input window that scales compute with audio length rather than forcing a fixed 30-second padding.
Where it falls shortper Gemini Restricted strictly to English and lacks the vocabulary depth needed for complex multi-speaker or noisy environments.
Just missed the top 5
GPT NVIDIA Canary — excellent multilingual ASR and translation, but less compelling overall than Qwen3-ASR and Parakeet · OpenAI Whisper large-v3 — slightly stronger than Turbo in some conditions, but usually not enough to justify its much higher inference cost
Claude Distil-Whisper / faster-whisper — huge practical value but they're distillations/runtimes of Whisper rather than independent models — their merit is largely credited to the Whisper entry
Gemini Meta SeamlessM4T v2 — Missed because its translation-centric, massive architecture is overkill and computationally inefficient for simple speech-to-text tasks · Qwen2-Audio — Missed because its large-scale LLM backbone is too resource-heavy for dedicated ASR compared to dedicated transcription models
Grok Cohere Transcribe — strong leaderboard contender but narrower language focus/less general practitioner accessibility
By model
ChatGPT
- 1.Qwen3-ASR
- 2.NVIDIA Parakeet TDT
- 3.OpenAI Whisper
- 4.Cohere Transcribe
- 5.NVIDIA Canary-Qwen 2.5B
Claude
- 1.OpenAI Whisper
- 2.NVIDIA Parakeet TDT
- 3.Voxtral
- 4.NVIDIA Canary-Qwen 2.5B
- 5.Kyutai STT
Gemini
- 1.OpenAI Whisper
- 2.IBM Granite Speech
- 3.NVIDIA Canary
- 4.Alibaba SenseVoice
- 5.Moonshine
Grok
- 1.NVIDIA Canary-Qwen 2.5B
- 2.IBM Granite Speech
- 3.OpenAI Whisper
- 4.NVIDIA Parakeet TDT
- 5.Voxtral
Common questions
What is the best open-source speech-to-text model according to AI models?
OpenAI Whisper leads. 2 of 4 models rank OpenAI Whisper the top pick. The current top 3: OpenAI Whisper, NVIDIA Parakeet TDT, NVIDIA Canary-Qwen 2.5B. Ranked by asking ChatGPT, Claude, Gemini, Grok the same buying question and merging their top-5 picks, updated 2026-07-15. Source: modelsagree.com.
Which open-source speech-to-text model did each AI model pick first?
ChatGPT: Qwen3-ASR. Claude: OpenAI Whisper. Gemini: OpenAI Whisper. Grok: NVIDIA Canary-Qwen 2.5B.
Do the AI models agree on the best open-source speech-to-text model?
Not unanimous. ChatGPT picks Qwen3-ASR; Grok picks NVIDIA Canary-Qwen 2.5B.
How is this open-source speech-to-text model ranking made?
ChatGPT, Claude, Gemini, Grok are each asked the same buying question in a fresh session with no system steering. Their top-5 answers are merged (rank 1 = 5 pts … rank 5 = 1 pt) into the consensus ranking, re-polled weekly and tracked over time.
More on how polling works: full methodology →
This ranking moves
We re-poll all four models weekly. Get one short email when a #1 flips.
Cite this ranking
ModelsAgree, “Best open-source speech-to-text model” — merged ranking from ChatGPT, Claude, Gemini & Grok, polled 2026-07-15. https://modelsagree.com/best/best-open-source-speech-to-text-model (CC BY 4.0)
Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled weekly