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Best Whisper Alternatives for Transcription in 2026

Looking for Whisper alternatives? Compare hosted APIs and open-weight models for transcription in 2026, with published prices, licenses and benchmark scores.

8 min read
By OndaLabs Team

The main Whisper alternatives fall into three groups: hosted transcription APIs, open-weight models you can run yourself, and faster ways to run Whisper itself. Hosted APIs suit teams that do not want to run servers. Open-weight models suit self-hosting. Choose by language coverage, timestamps, speaker labels and license. Prices and scores below are a snapshot from October 2026.

Why people look for Whisper alternatives

Whisper is a common starting point, but several trade-offs send people elsewhere:

  • Fixed 30-second windows. Whisper's receptive field is 30 seconds, so long audio needs a sequential or chunked method.
  • Text that was not spoken. The large-v3 model card warns that "predictions may include texts that are not actually spoken."
  • Speaker labels. OpenAI's guide now points to a separate model, gpt-4o-transcribe-diarize, for speaker labels.
  • Speed. The turbo variant is about 8 times faster than large, according to the repository, with only slight accuracy loss.
  • Licenses. The license depends on the source you download from, so each model needs its own check.

Whisper baseline: model sizes and licenses

The Whisper repository lists models from tiny (39M parameters) to large (1550M). The two models most people compare are below.

| Model | Parameters | Languages | License on the model card | |---|---|---|---| | whisper-large-v3 | About 1.55B | 99 | Apache-2.0 | | whisper-large-v3-turbo | 809M | 99 | MIT |

The large-v3 card says it was trained on about 1 million hours of weakly labeled audio and 4 million hours of pseudo-labeled audio. The turbo model cuts decoder layers from 32 to 4, which its card describes as "way faster, at the expense of a minor quality degradation." The GitHub README says MIT, while the large-v3 card says Apache-2.0, so read the license for the exact model you download.

Whisper alternatives: hosted transcription APIs compared

Hosted APIs bill per minute or per hour and handle the hardware for you. The table uses each vendor's published list price, with the per-hour figure derived by multiplying the per-minute price by 60.

| Service | Published price | Per audio hour | Notes | |---|---|---|---| | OpenAI gpt-transcribe | $0.0045 per minute | $0.27 | Recommended by OpenAI for recorded speech | | OpenAI whisper-1 | $0.006 per minute | $0.36 | Word and segment timestamps | | Deepgram Nova-3, monolingual | $0.0043 per minute | $0.26 | Diarization included on pre-recorded audio | | Mistral Voxtral Mini Transcribe V2 | $0.003 per minute | $0.18 | 13 languages, up to 3 hours per request | | AssemblyAI Universal-2 | $0.15 per hour | $0.15 | 99 languages | | ElevenLabs Scribe v2 | $0.22 per hour | $0.22 | 90+ languages | | Soniox async | $0.10 per hour | $0.10 | 60+ languages | | Speechmatics Standard, batch | $0.45 per hour | $0.45 | 55+ languages | | Google Cloud STT V2 Standard | $0.016 per minute | $0.96 | Dynamic batch is $0.003 per minute |

Several vendors offer starter credits. Deepgram gives $200 to new pay-as-you-go accounts, AssemblyAI gives $50 without a card, and Speechmatics gives $100. AssemblyAI's diarization add-on costs $0.02 per hour. Google's rate of $0.016 per minute applies to the first 500,000 minutes each month, and higher tiers cost less. Check each vendor's pricing page on the day you sign up, since promotions change.

Open-weight models compared

Open weights mean you can download a model and run it on your own hardware, subject to its license. The table below mixes two sources, and the column says which one each score comes from.

| Model | License | Languages | Mean English WER | Score source | |---|---|---|---|---| | NVIDIA Canary-Qwen-2.5B | CC-BY-4.0 | English only | 5.63 | Model card | | NVIDIA Parakeet TDT 0.6B v3 | CC BY 4.0 | 25 European languages | 6.34 | Model card | | IBM Granite Speech 3.3 8B | Apache 2.0 | 5 languages | 5.74 | Model card | | Cohere Transcribe (03-2026) | Apache-2.0 | 14 languages | 5.42 | Leaderboard feed | | Qwen3-ASR-1.7B | Apache-2.0 | 30 languages and 22 Chinese dialects | 5.76 | Leaderboard feed | | openai/whisper-large-v3 | Apache-2.0 on card | 99 | 7.44 | Leaderboard feed |

The leaderboard values come from a Hugging Face dataset feed of 63 entries, and every entry is marked unverified, so the feed is not a ranking. The Open ASR Leaderboard paper evaluated 86 open-source and proprietary systems across 12 datasets. Parakeet TDT 0.6B v3 can handle up to 24 minutes of audio with full attention, or 3 hours with local attention. Cohere's model has no timestamps and no diarization, and its repository is gated.

Faster ways to run Whisper itself

If you like Whisper's output but need more speed, several projects rebuild or optimize it:

  • faster-whisper (MIT). Its README claims it is "up to 4 times faster than openai/whisper for the same accuracy while using less memory."
  • whisper.cpp (MIT). A C and C++ port that runs on macOS, iOS, Android, Linux, Windows, WebAssembly and Raspberry Pi, with CPU-only inference.
  • Distil-Whisper (MIT). Its README claims 6 times faster and 49 percent smaller, within 1 percent WER on out-of-distribution sets. It is English only.
  • WhisperX (BSD-2-Clause). Adds word-level timestamps through forced alignment and speaker diarization with pyannote.

The faster-whisper repository publishes this benchmark for large-v2, on an RTX 3070 Ti with 8 GB of VRAM and a 13-minute clip:

| Setup | Time | Memory | |---|---|---| | openai/whisper, fp16 | 2 min 23 s | 4,708 MB | | faster-whisper, fp16 | 1 min 03 s | 4,525 MB | | faster-whisper, int8 | 59 s | 2,926 MB | | faster-whisper, batch 8, fp16 | 17 s | 6,090 MB |

These numbers come from the repository's own test conditions, on one GPU and one clip. Run your own test before you rely on them.

What does Whisper cost compared with the alternatives?

OpenAI now recommends gpt-transcribe for recorded speech and keeps whisper-1 for word or segment timestamps, subtitle output and translation to English. Its list prices are $0.006 per minute for whisper-1, $0.0045 per minute for gpt-transcribe, $0.003 per minute for gpt-4o-mini-transcribe and $0.017 per minute for the live model, gpt-live-transcribe. The OpenAI pricing page lists them. Self-hosting has no per-minute fee, but it needs hardware, as the VRAM column above shows.

How to choose a Whisper alternative

  1. Language coverage. Check your languages on each model card. Headline language counts use different definitions.
  2. Timestamps. whisper-1 returns word and segment timing. WhisperX adds forced alignment. Qwen3-ASR needs a separate aligner for timestamps.
  3. Speaker labels. AssemblyAI, Deepgram and OpenAI's diarization model offer them through their APIs. WhisperX needs a Hugging Face token for diarization.
  4. License. Read the license of the exact model, not the family name.
  5. Hardware. Open weights need a capable GPU or CPU. int8 quantization cut VRAM use in the benchmark above.
  6. Data handling. Read each vendor's security and privacy terms, since this article does not verify them.

For the metric behind these comparisons, read word error rate explained. For the basics, see what is speech to text.

Where OndaLabs fits

This article is about models and APIs for developers and self-hosters. OndaLabs is a hosted voice studio, not a model you download. If you just want text from a recording, OndaLabs has a speech to text tool that is included on the Free plan at 50 credits per minute. At the Starter plan price, that works out to about $0.159 per audio hour, derived from the plan price and the credit count. Try the speech to text tool in the app or compare plans on the pricing page.

Frequently asked questions

What is the best alternative to OpenAI Whisper?

There is no single best choice. Hosted APIs such as Deepgram, AssemblyAI and ElevenLabs suit teams that do not want to run models. Open-weight options such as NVIDIA Parakeet TDT 0.6B v3 and IBM Granite Speech 3.3 8B suit self-hosting, and their scores depend on the language and dataset.

Is Whisper still a good open-source speech recognition model?

Whisper is still widely used, and OpenAI's docs still recommend whisper-1 for timestamps and subtitle output. Its large-v3 model covers 99 languages, and the turbo variant trades a minor quality loss for speed. No deprecation notice was found as of October 2026, but check the docs before you migrate.

Is Whisper free to use commercially?

The code and weights are openly available, but the license depends on the source. The GitHub README says MIT, the large-v3 card says Apache-2.0, and the turbo card says MIT. Read the license of the exact model you download, and check with a lawyer for commercial projects.

Which Whisper alternative supports speaker diarization and word timestamps?

AssemblyAI returns a speaker for each word, with start and end times in milliseconds. Deepgram returns a speaker index for each word, with times in seconds. For self-hosting, WhisperX adds word alignment and pyannote diarization, but it needs a Hugging Face token. Check each docs page, since options change.

Can I run Whisper alternatives offline on a laptop?

Some can. whisper.cpp runs on macOS, Linux and Windows with CPU-only inference, and Moonshine is built for on-device use. Large models need a lot of memory, so the faster-whisper benchmark above used an 8 GB GPU. Start with a small model and measure the speed on your own machine.