Whisper catalog| Capability | Status | Current behavior |
|---|
| Whisper Tiny | Available | Smallest Whisper model exposed by the in-app transcription catalog. |
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| Whisper Base | Available | Lightweight Whisper model exposed by the in-app transcription catalog. |
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| Whisper Small | Available | Whisper model recommended by the current heuristic for many lower-memory machines. |
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| Whisper Medium | Available | Whisper model used by the current recommendation heuristic for Intel machines with sufficient memory. |
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| Whisper Large v3 Turbo | Available | Default transcription model and the current Apple Silicon recommendation for many machines with at least 16 GB of memory. |
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| Whisper Large v3 | Available | Largest Whisper model exposed by the in-app transcription catalog. |
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Local and provider-aware model catalog| Capability | Status | Current behavior |
|---|
| Llama 3.2 3B | Available | Lightweight local model exposed by the curated Ollama catalog and recommendation heuristic. |
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| Llama 3.1 8B | Available | Default local summary and chat model and the balanced recommendation for higher-memory machines. |
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| Llama 3.1 70B | Available | Large optional local model exposed by the curated Ollama catalog. |
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| Mistral 7B | Available | Optional local model exposed by the curated Ollama catalog. |
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| Mistral NeMo 12B | Available | Optional local model exposed by the curated Ollama catalog. |
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| Gemma 2 9B | Available | Optional local model exposed by the curated Ollama catalog. |
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| Phi 3.5 3.8B | Available | Lightweight local model exposed by the curated catalog and lower-memory recommendation path. |
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| Qwen 2.5 7B | Available | Optional local model exposed by the curated Ollama catalog. |
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| Nomic Embed Text | Beta | Local embedding model used by the Ollama-backed search and indexing paths. |
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| OpenAI-compatible embedding model | Beta | The configured remote endpoint must implement this embedding model and can receive transcript, summary, or query text. |
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Sizes in the product facts are catalog estimates, not a disk-space, network, performance, or quality guarantee. A remote endpoint must support the fixed embedding model identifier used by remote embedding paths.
