Can Whisper run on a CPU?

Yes. The tiny and base models run well on CPUs and can transcribe faster than real-time. The small model works on CPU but is slower. Medium and large models practically require a GPU for reasonable performance.

Is Whisper as accurate as paid services?

The large-v3 model matches or comes close to commercial services like Deepgram and AssemblyAI on most benchmarks. For English audio, the gap is small. Commercial services often win on edge cases, noisy audio, and specialized vocabularies.

Can I use Whisper in a commercial product?

Yes. The MIT license permits commercial use, modification, and distribution with no restrictions. You do not need to open-source your product or pay royalties.

Open-source speech recognition model from OpenAI supporting 99 languages with word-level timestamps, translation, and MIT licensing. Free to self-host or available via API.

How much does Whisper cost?

Whisper pricing starts at Free (open source). A free plan is available.

Whisper delivers commercial-quality speech recognition for free under the MIT license. Accuracy across 99 languages is impressive, and the range of model sizes provides real flexibility. Lacks production features like streaming and diarization, but for raw transcription quality at zero cost, nothing else comes close. We rate it 8.1/10.

Whisper Review 2026: 8.1/10, Free and Open Source

TLDR

Whisper is OpenAI’s open-source speech recognition model supporting 99 languages, available for free self-hosting or via the OpenAI API. Best for: developers and teams who want accurate, flexible transcription without per-minute lock-in. Price: Free (open source, MIT license). Rating: 8.1/10

What is Whisper?

Whisper is a general-purpose speech recognition model released by OpenAI under the MIT license. Trained on 680,000 hours of multilingual and multitask supervised data collected from the web, it approaches the accuracy of commercial speech-to-text services while being completely free to run on your own hardware.

The model handles transcription, translation (any language to English), language identification, and timestamp generation. It comes in multiple sizes (tiny, base, small, medium, large) so you can trade accuracy for speed depending on your hardware and use case. The open-source nature means you can run it locally with no API costs, modify it for your needs, and keep your audio data entirely private.

Key Features

99 Language Support: Whisper handles 99 languages with a single model. You do not need separate models per language. The system automatically detects the spoken language and transcribes accordingly, making it exceptionally practical for multilingual applications.

Multiple Model Sizes: From the tiny model (39M parameters, runs on a CPU) to large-v3 (1.5B parameters, needs a GPU), Whisper offers meaningful flexibility. The tiny model transcribes faster than real-time on modest hardware. The large model rivals commercial APIs in accuracy.

Translation to English: Whisper can translate speech from any of its supported languages directly to English text. This is handled natively by the model rather than as a separate translation step, which tends to produce more natural results than piping transcription through a translation API.

Word-Level Timestamps: The model generates word-level timing information, which is essential for subtitle generation, audio editing, and any application that needs to align text with specific moments in audio.

MIT License: Complete freedom to use, modify, and distribute. No usage tracking, no per-minute fees, no vendor lock-in. You can fine-tune the model on your own data, embed it in commercial products, or run it in air-gapped environments.

OpenAI API Access: If you do not want to manage infrastructure, Whisper is available through OpenAI’s API at $0.006 per minute. This gives you Whisper accuracy without needing GPU hardware, though it adds the typical constraints of an external API dependency.

Pricing

Whisper itself is free and open source. The cost of self-hosting depends on your infrastructure. Running the large-v3 model requires a GPU with at least 10GB of VRAM. Cloud GPU instances (AWS, GCP) typically cost $0.50 to $2.00 per hour, which translates to well under $0.01 per minute of audio when processing at scale.

The OpenAI API charges $0.006 per minute, which is competitive with commercial alternatives and removes the infrastructure burden. For low-volume use cases, the API is more cost-effective than maintaining dedicated GPU hardware.

For teams processing large volumes, self-hosting becomes dramatically cheaper. Processing 100,000 minutes per month through the API costs $600, while a dedicated GPU instance running continuously might cost $200 to $500 and handle the same volume. The break-even point depends on your processing volume and how much infrastructure management you want to take on.

Pros and Cons

Plan	Price	Plan Features	Best For
Self-Hosted	Free	MIT license, all model sizes, no usage limits, requires own hardware	Teams with GPU infrastructure
OpenAI API	$0.006/min	Managed hosting, no GPU needed, usage-based billing	Low-volume or no-infrastructure teams

Who is Whisper Best For?

Whisper is ideal for developers and technical teams who value flexibility, privacy, and cost control. If you are comfortable managing Python environments and optionally GPU infrastructure, Whisper gives you commercial-grade transcription without commercial pricing.

Solo developers and freelancers benefit enormously from the free, no-strings-attached licensing. You can build transcription into client projects, side projects, or internal tools without worrying about API bills scaling with usage.

SaaS companies processing large audio volumes can achieve significant cost savings by self-hosting, especially if they already have GPU infrastructure for other ML workloads. The MIT license means no legal complications around embedding Whisper in commercial products.

Teams that need real-time streaming, speaker diarization, or production-grade reliability out of the box should consider Deepgram or AssemblyAI instead. Whisper’s core model does not support streaming natively, and adding production features requires additional engineering work.

Alternatives

Deepgram

Deepgram offers purpose-built speech models with sub-300ms streaming latency, built-in diarization, and a production-ready API. It is faster and more feature-complete than Whisper for real-time applications, but costs money per minute. Choose Deepgram when latency and production features matter more than cost savings.

AssemblyAI

AssemblyAI provides a polished API with strong accuracy, LLM integration via LeMUR, and features like content moderation and PII redaction. It handles production concerns that Whisper leaves to the developer. Best for teams who want a managed service with AI-native features beyond basic transcription.

Gladia

Gladia bundles transcription with translation, summarization, sentiment analysis, and entity extraction in one API. It supports 100+ languages and includes all features in the base price. Choose Gladia if you need audio intelligence features beyond transcription and prefer a single API over self-hosted infrastructure.

Verdict

Whisper changed the speech recognition landscape by making commercial-quality transcription freely available. The accuracy on English and major languages is genuinely impressive, the MIT license removes all commercial friction, and the range of model sizes means it runs on everything from a laptop CPU to a data center GPU cluster.

The main gap is everything around the core model. Whisper gives you a transcription engine, not a production service. There is no streaming API, no built-in diarization, no managed infrastructure. You need to build or integrate those pieces yourself, which is fine for technical teams but a significant barrier for everyone else.

For the sheer combination of accuracy, language support, flexibility, and price (free), Whisper is hard to beat. It has become the baseline against which all commercial speech-to-text services are measured, and for many use cases, the baseline is all you need.

What We Like

What Could Be Better