Best AI Transcription Models in 2026: Accuracy, Pricing, and Value

One hundred hours of recordings can cost $4 to transcribe with Groq's Whisper Large V3 Turbo, or $18 with Mistral's Voxtral Mini Transcribe 2, before extras. The cheaper bill is attractive. Whether it is the better purchase depends on the transcript you need: searchable words, speaker-labelled meeting notes, or captions that must line up with the recording.

Our budget pick for basic recorded-audio transcription is Whisper Large V3 Turbo on Groq. For meetings that need speaker labels and word-level timestamps, Voxtral Mini Transcribe 2 is our cost-first shortlist pick, with ElevenLabs Scribe v2 worth comparing when reducing transcription errors matters more than the lowest base rate. The prices and evidence behind these choices are below.

MAI-Transcribe-2 is the compelling newcomer on price and published accuracy, but it is not our default production recommendation: its Azure integration is still public preview, without an SLA, and Microsoft advises against production use. That restriction matters as much as its benchmark score.

Prices and documentation were checked on September 4, 2026. This is an editorial comparison of public API documentation and independent benchmarks, not a claim that we tested every model ourselves. It focuses on completed recordings, not meeting-app subscriptions or live voice agents.

Transcription API prices at a glance

The comparison unit is a model served by a particular provider in a particular mode. “Whisper pricing” alone is not specific enough. Open weights, a hosted API, and a desktop application using those weights have different costs.

These are published base usage rates for file transcription, not guaranteed invoices. Dollar amounts are USD. We exclude tax, storage, retries, optional features, free credits, and negotiated discounts. “Approx.” denotes a token-based estimate; the Scribe row shows usage value before any plan-level charges or credits.

There is also a lower-priced challenger to check if its service fits your region and language: StepFun lists StepAudio 2.5 ASR at CNY 0.15 per hour, or CNY 15 for 100 hours. We retain its original currency instead of pretending a converted price establishes a globally available cheapest option.

This is a representative shortlist with public rates, not every speech service on the market. An existing cloud contract, required deployment region, or specialist vocabulary can justify a different shortlist.

What the accuracy rankings actually measure

OpenRouter's transcription rankings help identify models people use. They rank request volume, not transcription accuracy. A popular model is worth investigating; popularity is not proof that it will recognize your customers' names.

For a like-for-like quality signal, the following snapshot comes from the non-streaming AA-WER v2 table published by Artificial Analysis:

Word error rate counts substitutions, deletions, and insertions against a reference transcript. It is not a probability that any particular sentence is correct. A wrong amount or missing “not” may matter more to you than several harmless formatting differences.

The benchmark methodology uses roughly eight hours across three datasets, weighted 50%, 25%, and 25%. It includes English parliamentary speech and earnings calls; some long files are chunked to fit model limits. These results do not establish a Chinese, Arabic, or all-language winner. They also do not measure whether a speaker label is correct.

Use this table to narrow the field, not to skip a sample of your own audio. Keep the model version and provider attached to the score; a result for Universal-3 Pro, for example, should not be relabelled as Universal-3.5 Pro.

Which model would we choose?

Whisper Turbo on Groq: our budget pick for basic transcripts

At $4 for 100 hours, this is the first option we would evaluate for a searchable recording archive or a first-pass transcript. Its appeal is the low hosted price combined with a documented API—not a promise that its errors are negligible.

Groq's documentation separates Turbo from Large V3: Turbo supports multilingual transcription but not the translation endpoint. Requests shorter than 10 seconds still incur 10 seconds of billing. That minimum is minor for interviews but significant when a pipeline splits audio into many tiny clips.

Choose it when inexpensive text is the main deliverable and your sample passes review. If you need reliable “who said what” output, compare a complete speaker-labelling solution rather than treating the base transcription rate as the finished workflow price.

Voxtral versus Scribe: meeting features or fewer text errors

Voxtral Mini Transcribe 2 is our cost-first choice to evaluate for speaker-labelled meetings. Mistral documents speaker diarization, word-level timestamps, and 13 supported languages. Diarization means separating the speakers so statements can be attributed to the right person.

There is an important limitation: Mistral says overlapping speech typically produces a transcript of one speaker. Do not confuse the presence of speaker labels with complete capture of everyone talking at once.

Scribe v2 is the accuracy-oriented alternative in this pair. Its listed base usage cost is only $4 more across 100 hours, and it has the lower WER in the snapshot above. That makes it worth a direct test when editing time matters. ElevenLabs charges separately for keyterm prompting and entity detection; compare the actual plan and selected features, not only $0.22/hour. The benchmark does not settle which service labels your meeting's speakers better.

MAI-Transcribe-2: a strong preview, not a production default

Microsoft's September 3 launch announcement offers $0.10/hour through the end of 2026. Together with the quality snapshot, that makes MAI-Transcribe-2 an attractive experimental price–accuracy option.

But the Azure Speech documentation explicitly marks the integration as public preview, without a service-level agreement, and not recommended for production workloads. Evaluate it for a prototype if the required region and terms fit. Do not base an unattended production pipeline on the promotional price alone, or assume the same price continues in 2027.

OpenAI and Google: compare the dedicated transcription models

Within OpenAI, GPT-Transcribe is the newer dedicated option in this comparison. GPT-4o Mini Transcribe remains the cheaper alternative at an estimated $0.18/hour. We would compare those two before assuming the older GPT-4o Transcribe is the best fit simply because “4o” sounds familiar.

Gemini 3.5 Transcribe adds another dedicated option, with speaker diarization and word timestamps. Google's approximately $0.005/minute estimate combines audio input and text output; it is not just the input fee. It is worth comparing when you already use Google's API, but this is not the same product as Transcribe Live or a general Gemini chat model receiving an audio attachment.

Soniox, Qwen, AssemblyAI, and Deepgram: reasons to keep alternatives

Soniox deserves a place in a low-cost comparison, but its roughly $0.10/hour is an estimate. Its billing includes audio input, supplied text context, and text output. More output or translation changes the bill.

For a Chinese-language workflow, include Qwen in the sample rather than extrapolating an English benchmark. Alibaba Cloud's file-transcription rate depends on the region and exact model. The open-weight Qwen3-ASR-1.7B is a separate deployment choice, not the Flash API under another name. Local hosting removes a per-call API invoice, not hardware and maintenance costs.

AssemblyAI's current model documentation lists Universal-3.5 Pro at $0.21/hour and Universal-2 at $0.15/hour for pre-recorded audio. Check language support and the exact version instead of borrowing older benchmark scores. Deepgram's Nova-3 pricing includes speaker diarization for pre-recorded audio, while its multilingual base rate is higher than its monolingual rate. Both belong in a feature-matched meeting comparison, even if they do not win the cheapest-base-rate column.

What 100 hours really costs

Take a hypothetical monthly workload of 100 hours of single-channel interviews. The first deliverable is text; summaries come later. At the listed rates, Groq Turbo costs $4, Voxtral $18, and GPT-Transcribe $27 before extras. That is a usage calculation, not an observed bill.

Now suppose a reviewer's time is worth $20/hour. Voxtral's $14 premium over Groq buys 42 minutes of review time across the entire month. If it saves more than that in your workflow, the higher API price can pay for itself. We have not measured that saving; 42 minutes is the break-even point you can test.

Three billing details can change the comparison:

• Small requests: Groq's 10-second minimum increases the cost of heavily fragmented audio.
• Required features: ElevenLabs lists keyterm prompting at an additional $0.05/hour. Across 100 hours, that adds $5 of usage before other charges.
• Channels: Deepgram bills processed channels separately; a 10-minute, two-channel recording can count as 20 minutes of processing.

The relevant sources are the Groq file limits, ElevenLabs API rates, and Deepgram billing explanation. Use the configuration you will actually send.

Live transcription needs a separate comparison. AssemblyAI's streaming billing, for example, counts connection duration rather than just uploaded audio. A model's ability to process an existing file quickly does not tell you how soon a live listener sees the first or final words.

Add the cost of summaries and translation

The transcription API produces the source text. If an LLM then turns it into meeting notes, translates it, or extracts action items, that is another bill. The summarization-model comparison explains how to check that shortened notes preserve decisions and conditions.

Use AILesson's LLM price calculator for this text-processing stage. Suppose your monthly transcripts and prompts total 1.5 million input tokens, and the summaries total 0.15 million output tokens. Enter 1.5 and 0.15 in the respective fields to compare model costs. These are hypothetical token totals, not a fixed conversion from 100 hours of audio.

The calculator estimates input/output token costs; it is not an audio-minute transcription calculator. Add its text-processing estimate to the transcription usage, required features, storage, retries, and review time. If you split long transcripts into chunks and run a final summary pass, include those repeated inputs too.

Pick two candidates and check the difficult parts

For basic transcripts, start with Groq Turbo and one higher-accuracy alternative. For meetings, compare Voxtral and Scribe; add MAI only if a preview is acceptable for the experiment. For Chinese or mixed-language speech, include the relevant Qwen service rather than assuming the English ordering transfers.

Use recordings you are authorized to process, including one clean sample, one noisy or overlapping conversation, and one containing names, amounts, or technical vocabulary. Give each model the same audio and equivalent instructions. Check missing speech, important words, speaker changes, timestamps, and the time you spend repairing the result—not just whether the transcript reads smoothly.

Before sending private recordings, confirm the provider's retention, training-use, and regional-processing terms. Then choose the least expensive option that passes your actual requirements. A $4 transcript that takes hours to repair is not the budget winner.

References

• OpenRouter transcription rankings — usage-based discovery, not an accuracy test.
• Artificial Analysis non-streaming leaderboard and methodology — the quality snapshot and its limits.
• Groq speech-to-text documentation — Whisper rates, model differences, and minimum billing.
• Mistral Voxtral documentation and release details — price, meeting features, and overlap limitations.
• ElevenLabs API pricing — Scribe usage rates and add-ons.
• Microsoft launch terms and Azure integration documentation — promotion and preview restrictions.
• OpenAI pricing and Google Developer API pricing — dedicated transcription model rates and estimates.
• Soniox pricing, Alibaba Cloud pricing, and StepFun pricing — alternative billing models and regional rates.
• AssemblyAI model documentation and Deepgram pricing — model/mode distinctions and feature costs.
• Qwen3-ASR-1.7B model card — the separate open-weight deployment option.