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Whisper Large V3
- Speech-to-Text
Whisper Large V3 is OpenAI’s large-scale speech recognition model designed for robust, multilingual transcription and translation. It is notable for high accuracy, support for many languages, and strong performance on real-world, noisy audio.
About the model
What is Whisper Large V3?
Whisper Large V3 is a neural speech-to-text and speech translation model developed by OpenAI for high-quality automatic transcription across many languages. It is mainly used to convert spoken audio from meetings, calls, videos, and podcasts into accurate text transcripts. It is also used for tasks like subtitle generation, live captioning, and translating spoken content between languages. It follows earlier Whisper versions (such as Whisper Large V1/V2) as part of the Whisper family of speech recognition models.
Model capabilities
5 Core Capabilities
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Multilingual Transcription
Accurately transcribes spoken audio into text across many languages, handling varied speakers, accents, and recording conditions robustly.
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Robust Speech Recognition
Performs automatic speech recognition with strong noise robustness, capturing words correctly even in challenging, real-world acoustic environments.
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Language Identification
Automatically detects the spoken language in audio segments, enabling downstream transcription and translation workflows without manual language selection.
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Speech Translation
Converts spoken content from one language into written text in another, supporting multilingual applications and cross-language communication scenarios.
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Timestamped Segmentation
Produces time-aligned text segments, enabling subtitle creation, search within audio, and precise navigation of long recordings.
Use cases
6 Most Valuable Use Cases
- Multilingual Speech Transcription
- Meeting and Lecture Captions
- Call Center Conversation Logging
- Media Subtitle Generation
- Voice-Based Accessibility Tools
- Audio Data Preprocessing Pipeline
Transparent pricing
Cost Comparison
LLM API offers the lowest per‑minute STT pricing and best overall limits for Whisper-class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~350ms | ~120 min/s | 99.99% | $0.003/min | $0.003/min | ~600 min audio |
| OpenAI | Global | ~500ms | ~60 min/s | 99.9% | $0.006/min | $0.006/min | ~480 min audio |
| Azure OpenAI | US East / EU West | ~550ms | ~50 min/s | 99.9% | ~$0.007/min | ~$0.007/min | ~480 min audio |
| Replicate | Global | ~700ms | ~30 min/s | ~99.5% | ~$0.009/min | ~$0.009/min | ~300 min audio |
| AssemblyAI (Whisper-equivalent) | Global | ~600ms | ~40 min/s | 99.9% | ~$0.010/min | ~$0.010/min | ~300 min audio |
Performance benchmarks
Technical Specifications
| Metric | Whisper Large V3 (OpenAI) | Whisper Large (OpenAI, v2) | Deepgram Nova-2 General |
|---|---|---|---|
| Avg Latency (30s clip) | ~1.2s | ~1.5s | ~1.0s |
| Languages Supported | ~100+ | ~100+ | ~30+ |
| Price per Minute | $0.006 | $0.006 | $0.004 |
| Max Audio Duration per Request | ~2h | ~2h | ~6h |
| Accuracy (WER, clean English) | ~6–7% | ~8–9% | ~7–8% |
| Streaming Support | Yes | Partial | Yes |
| Uptime (SLA style) | ~99.9% | ~99.9% | ~99.9% |
30-day usage via LLM API
- 620M
- Audio seconds transcribed in last 30 days
- 11.4M
- Transcription & translation API requests
- 210K
- Active developer accounts using Whisper Large V3
- 99.9%
- Average API uptime over the last 30 days
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Automatically route each request to the optimal model across providers based on latency, cost, and quality—no application refactors or manual traffic shifting required.
One endpoint, every model -
Cost-Aware Orchestration
Control spend with per-route budgets, smart model downgrades, and granular cost analytics so you can experiment freely without surprise invoices or manual tuning.
Cut costs, keep quality -
Resilient Fallback Flows
Define automatic failover chains so timeouts or provider outages seamlessly retry on backup models—keeping your production AI APIs reliable without extra glue code.
Stay online, even if models don’t -
End-to-End Observability
Trace every request across providers with logs, metrics, and payload inspection, making it easy to debug prompts, compare models, and ship reliable AI features.
See every token, everywhere -
Task-Level Abstractions
Call high-level tasks like chat, generate, or embed instead of vendor-specific APIs, so you can swap models without rewriting business logic or prompts.
Code to tasks, not vendors -
High-Throughput Batch APIs
Process millions of operations in parallel with robust batching, retries, and rate control, maximizing throughput while staying within provider limits.
Batch at production scale
Decision guide
When to Use — When NOT to Use
Use it if...
- You need high-quality automatic speech recognition across many languages and acoustic conditions.
- You need to transcribe long-form audio like podcasts, lectures, or meetings reliably.
- Your use case involves generating subtitles or captions from prerecorded video or audio files.
- Your use case involves building voice-enabled applications that convert speech to text server-side.
- You need to fine-tune downstream NLP workflows on accurate transcripts instead of raw audio.
- You need robust transcription of accented speech, noisy environments, or varied microphone quality.
Avoid if...
- You need text-to-speech synthesis rather than converting spoken audio into text transcripts.
- You need real-time interactive latency on-device without sending audio to external servers.
- Your workload requires understanding or generating text beyond transcription, like reasoning or coding.
- You need to process exclusively text inputs, without any audio or speech components.
- Your workload requires detailed speaker diarization, like labeling and separating multiple speakers.
- You need secure offline transcription entirely air-gapped, with no cloud connectivity allowed.
FAQ
Frequently Asked Questions
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What is Whisper Large V3?
Whisper Large V3 is OpenAI’s large-scale speech recognition model optimized for accurate transcription and translation of audio via API.
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What modalities does Whisper Large V3 support?
Whisper Large V3 supports audio-to-text transcription and speech-to-text translation, returning text outputs only.
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How do I access Whisper Large V3 through LLM.API?
You call the LLM.API endpoint with provider set to OpenAI and model set to Whisper Large V3, passing audio as input.
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What is the context window or length limit for Whisper Large V3 inputs?
Whisper Large V3 limits inputs primarily by audio duration and file size rather than a traditional token-based context window.
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How fast is Whisper Large V3 in terms of latency?
Latency depends on audio length and server load, but Whisper Large V3 is designed for near real-time or faster-than-real-time transcription.
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How is pricing for Whisper Large V3 handled on LLM.API?
Pricing for Whisper Large V3 on LLM.API is typically usage-based per unit of audio processed, following OpenAI-linked rate structures.
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What is Whisper Large V3 best suited for?
Whisper Large V3 is best for high-quality multilingual speech transcription, captioning, and audio-to-text pipelines in applications and backends.
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How does Whisper Large V3 compare to smaller Whisper variants?
Whisper Large V3 generally offers higher accuracy and robustness than smaller Whisper models at the cost of higher compute and latency.
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What are the main limitations of Whisper Large V3?
Whisper Large V3 can struggle with very noisy audio, heavily accented speech, overlapping speakers, and does not produce structured metadata like timestamps by default.
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Can Whisper Large V3 handle streaming or long-form audio via LLM.API?
Yes, Whisper Large V3 can be used on long-form or chunked audio, though you must manage segmentation and reassembly at the application level.