Model overview
This is a **CoreML** conversion of CohereLabs/cohere-transcribe-03-2026 with **6-bit palettization** for on-device speech recognition on Apple Silicon (macOS / iOS). 1. Add all `.mlpackage` files to your Xcode project 2. Load `coreml_manifest.json` at runtime for tokenizer IDs and pipeline config 3. Use `MLModel` with `.computeUnits = .cpuAndGPU` for best performance 4. Process audio in **30-second chunks** with **5-second overlap** (params in manifest) The decoder runs in two phases: 1. **Full-sequence masked** decoder generates the first token from encoder output 2. **KV-cached** decoder generates subsequent tokens autoregressively
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.
Are these real-time billing prices?
These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
Sources & traceability
许可证：gpl-3.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。