Open Whisper checkpoint for robust multilingual transcription and captioning
Recorded modality support shows what this model can accept and produce. An undocumented modality is shown as unknown rather than assumed to be unsupported.
Accepts
Produces
Open Whisper checkpoint for robust multilingual transcription and captioning This is a source-backed review draft for Whisper 3 Large from OpenAI. It documents what the registry says and calls out what still needs a human editor to verify; it does not turn unverified fields into product claims.
The source record lists a release date of 2024-10-01 and a knowledge cutoff of not supplied in this registry record. It identifies the model family as whisper and describes open weights as available. These fields are useful for narrowing a shortlist, but the linked primary source remains the reference for any current release or policy change.
Whisper 3 Large accepts audio and returns text according to the registry. That makes the model a potential fit only where those interface assumptions match the product workflow. A team should test its own file types, tool schema, safety settings, and deployment constraints rather than assuming that a listed modality works identically in every provider surface.
The listed context window is 448 tokens, with a listed maximum output of 4,096 tokens. The source does not declare additional capability flags. Those flags describe an interface surface; they are not evidence of response quality, speed, tool reliability, or availability.
A benchmark number is meaningful only alongside its task, harness, date, and source. It should not be treated as a universal ranking, and it should be refreshed before an editor uses it in a comparison or recommendation.
Start with a small, representative test set: the prompts people actually send, the tools the product calls, the languages and files it receives, and the failure modes users care about. Compare output quality, structured-output reliability, latency, token use, safety behavior, and operational support under the same conditions for every candidate. Keep the source link, test date, region, and provider route next to any decision so the review can be repeated later.
Before approval, an editor should verify the linked source, confirm whether a currently supported API or hosted route exists, add first-party pricing only when it is published, check benchmark provenance, and record any important limitations. If a fact cannot be verified, this page should say so plainly. That protects readers from stale model pages and gives search engines a materially useful, evidence-led resource rather than a generated catalogue entry.
Use the recorded facts as a shortlist, then validate the model against representative inputs and production constraints.
Whisper 3 Large is categorized for Audio-text-to-text, Automatic speech recognition, Text generation, Text-to-text AI models. Its current record accepts audio and produces text. Confirm file formats, preprocessing, and provider-specific request schemas before implementation.
The directory records 448 context and 4.1K maximum output. Listed token prices are $0.00 input and $0.00 output per 1M tokens. Treat missing values as unknown and recheck current commercial terms.
Recorded output speed is — and time to first token is —. Hosting route, region, prompt length, concurrency, and provider load can materially change both measurements.
This page separates sourced model facts from incomplete fields. Benchmark evidence is displayed only when its source is linked and verified. Before choosing Whisper 3 Large, test task quality, tool reliability, safety behavior, data controls, rate limits, and total cost on the exact route you intend to use.
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