Which models can you actually run?
Every leaderboard ranks models you call over an API. This one ranks models you can put on your own machine — by the runtime that loads them, the format they ship in, whether they're quantised, and whether the licence lets you sell what you build.
Read from the index 2026-09-18Two different claims sit behind every bar, and they are kept apart everywhere on this page. Declared means a model card named the runtime. By format means the weights are in a container that runtime reads — which says the file will open, not that the architecture is implemented. A model is counted once if either is true. Nothing declares candle, burn or ort; no one writes a Rust runtime on a model card. That gap is the whole reason this page exists.
Reach, by runtime
declared on the model cardnot declared, but ships a format it reads
Every bar but one is almost entirely light, which is the finding: for most runtimes the evidence is the file, not the card. MLX is the exception — 11,788 cards name it against 471 models shipping an npz, because mlx-community publishes converted weights under a name that says MLX rather than in the format that proves it. It is the one runtime where the card is the better evidence.
The most-used models coreml can load
- Either signal
- 355
- Declared
- 121
- By format
- 350
- Commercial use ok
- 225
- Quantised build
- 43
| Model | Downloads | Params | Quant | Licence | Terms |
|---|---|---|---|---|---|
| google-bert/bert-base-uncased fill-mask | 47,693,504 | 110M | — | Apache-2.0 | commercial ok |
| argmaxinc/whisperkit-coreml declared automatic-speech-recognition | 11,230,874 | — | — | MIT | commercial ok |
| distilbert/distilgpt2 text-generation | 2,083,338 | 88M | — | Apache-2.0 | commercial ok |
| thenlper/gte-small sentence-similarity | 1,068,659 | 33M | — | MIT | commercial ok |
| jinaai/jina-embeddings-v2-small-en feature-extraction | 928,484 | 33M | — | Apache-2.0 | commercial ok |
| jinaai/jina-embeddings-v2-base-en feature-extraction | 391,530 | 137M | — | Apache-2.0 | commercial ok |
| FluidInference/parakeet-tdt-0.6b-v3-coreml declared automatic-speech-recognition | 288,503 | 627M base model | — | CC-BY-4.0 | commercial ok |
| FluidInference/parakeet-ctc-110m-coreml automatic-speech-recognition | 96,360 | 110M name | — | CC-BY-4.0 | commercial ok |
| Falconsai/text_summarization summarization | 53,360 | 61M | — | Apache-2.0 | commercial ok |
| openbmb/MiniCPM-o-4_5-gguf any-to-any | 44,545 | 9.4B base model | 4–16-bit 11 builds | Apache-2.0 | commercial ok |
| Barrymanalow/nb-whisper-coreml automatic-speech-recognition | 41,974 | 242M base model | — | Apache-2.0 | commercial ok |
| argmaxinc/speakerkit-coreml automatic-speech-recognition | 41,206 | — | — | CC-BY-4.0 | commercial ok |
| distilbert/distilbert-base-uncased-distilled-squad question-answering | 37,853 | 66M | — | Apache-2.0 | commercial ok |
| aufklarer/Parakeet-TDT-v3-CoreML-INT8-iOS-5s | 34,439 | — | 8-bit | CC-BY-4.0 | commercial ok |
| tiiuae/falcon-7b-instruct text-generation | 31,800 | 7.2B | — | Apache-2.0 | commercial ok |
| FluidInference/silero-vad-coreml declared voice-activity-detection | 30,516 | — | — | MIT | commercial ok |
| FluidInference/speaker-diarization-coreml voice-activity-detection | 19,800 | — | — | CC-BY-4.0 | commercial ok |
| aufklarer/Chatterbox-Flash-CoreML text-to-speech | 15,095 | — | — | MIT | commercial ok |
| FluidInference/parakeet-tdt-0.6b-v2-coreml automatic-speech-recognition | 12,320 | 600M name | — | CC-BY-4.0 | commercial ok |
| Gustavosta/MagicPrompt-Stable-Diffusion text-generation | 10,953 | 137M | — | MIT | commercial ok |
| aufklarer/Silero-VAD-v5-CoreML voice-activity-detection | 10,760 | — | — | MIT | commercial ok |
| aufklarer/Parakeet-TDT-v3-CoreML-INT8 | 9,526 | — | 8-bit | CC-BY-4.0 | commercial ok |
| unum-cloud/uform3-image-text-multilingual-base feature-extraction | 6,359 | — | — | Apache-2.0 | commercial ok |
| BarathwajAnandan/cohere-transcribe-03-2026-CoreML-6bit declared automatic-speech-recognition | 5,230 | 2.1B base model | 6-bit | GPL-3.0-only | commercial ok |
| openbmb/MiniCPM-V-4-gguf image-text-to-text | 4,264 | 4.1B base model | 4–16-bit 11 builds | Apache-2.0 | commercial ok |
| aufklarer/DeepFilterNet3-CoreML declared audio-to-audio | 3,737 | — | — | Apache-2.0 | commercial ok |
| aufklarer/Kokoro-82M-CoreML declared text-to-speech | 3,691 | 82M name | — | Apache-2.0 | commercial ok |
| aufklarer/Pyannote-Community-1-CoreML audio-classification | 3,588 | — | — | CC-BY-4.0 | commercial ok |
| mattmireles/kokoro-coreml declared text-to-speech | 2,777 | — | — | Apache-2.0 | commercial ok |
| gety-ai/gety-embed-v0 sentence-similarity | 2,499 | 118M base model | — | MIT | commercial ok |
| FluidInference/diar-streaming-sortformer-coreml declared automatic-speech-recognition | 2,436 | — | — | CC-BY-4.0 | commercial ok |
| FluidInference/kokoro-82m-coreml text-to-speech | 2,344 | 82M name | — | Apache-2.0 | commercial ok |
| aufklarer/WeSpeaker-ResNet34-LM-CoreML audio-classification | 2,217 | — | — | MIT | commercial ok |
| aufklarer/Omnilingual-ASR-CTC-300M-CoreML-INT8-10s declared automatic-speech-recognition | 2,139 | 300M name | 8-bit | Apache-2.0 | commercial ok |
| TheStageAI/thewhisper-large-v3-turbo declared automatic-speech-recognition | 1,769 | 809M | — | CC-BY-4.0 | commercial ok |
| FluidInference/parakeet-unified-en-0.6b-coreml declared automatic-speech-recognition | 1,740 | 600M name | — | CC-BY-4.0 | commercial ok |
| aufklarer/Sidon-CoreML declared audio-to-audio | 1,701 | 580M base model | — | MIT | commercial ok |
| apple/coreml-depth-anything-v2-small declared depth-estimation | 1,614 | — | — | Apache-2.0 | commercial ok |
| FluidInference/pocket-tts-coreml declared text-to-speech | 1,506 | — | — | CC-BY-4.0 | commercial ok |
| kensora/kokoro-coreml declared text-to-speech | 1,482 | — | — | Apache-2.0 | commercial ok |
| Falconsai/medical_summarization summarization | 1,326 | 61M | — | Apache-2.0 | commercial ok |
| DictionLabs/whisperkit-coreml automatic-speech-recognition | 1,283 | 73M base model | — | MIT | commercial ok |
| mickekringai/kb-whisper-coreml automatic-speech-recognition | 1,046 | — | — | Apache-2.0 | commercial ok |
| aufklarer/Silero-VAD-v6.2.1-CoreML declared voice-activity-detection | 1,024 | — | — | MIT | commercial ok |
| yslinear/kotoba-whisper-v2.2-coreml automatic-speech-recognition | 984 | 756M base model | — | Apache-2.0 | commercial ok |
| aufklarer/Sortformer-Diarization-CoreML declared audio-classification | 809 | — | — | CC-BY-4.0 | commercial ok |
| aufklarer/Qwen3-ASR-CoreML automatic-speech-recognition | 752 | 938M base model | — | Apache-2.0 | commercial ok |
| apple/coreml-sam2.1-tiny declared mask-generation | 719 | — | — | Apache-2.0 | commercial ok |
| FluidInference/parakeet-tdt-ctc-110m-coreml automatic-speech-recognition | 674 | 110M name | — | CC-BY-4.0 | commercial ok |
| mweinbach/Kokoro-82M-Swift text-to-speech | 661 | 82M name | — | Apache-2.0 | commercial ok |
| takanori-ishikawa/Qwen3.5-ANE-CoreML text-generation | 649 | 873M base model | — | Apache-2.0 | commercial ok |
| apple/coreml-sam2.1-baseplus declared mask-generation | 514 | — | — | Apache-2.0 | commercial ok |
| Writer/palmyra-small text-generation | 490 | 176M | — | Apache-2.0 | commercial ok |
| aufklarer/Whisper-Large-v3-Turbo-CoreML declared automatic-speech-recognition | 441 | 1.5B base model | — | MIT | commercial ok |
| mlboydaisuke/qwen3.5-0.8B-CoreML declared text-generation | 431 | 873M base model | — | Apache-2.0 | commercial ok |
| aufklarer/Qwen3-ForcedAligner-0.6B-CoreML-INT8 audio-classification | 412 | 918M base model | 8-bit | Apache-2.0 | commercial ok |
| apple/coreml-detr-semantic-segmentation declared image-segmentation | 397 | — | — | Apache-2.0 | commercial ok |
| mlboydaisuke/qwen3.5-2B-CoreML declared text-generation | 394 | 2.3B base model | — | Apache-2.0 | commercial ok |
| niduank/Qwen2.5-0.5B-Instruct-ANE-int8 declared | 392 | 494M base model | 8-bit | Apache-2.0 | commercial ok |
| apple/coreml-depth-anything-small declared depth-estimation | 379 | — | — | Apache-2.0 | commercial ok |
Ordered by downloads, which on these 202,261 models is a real signal — 96% have a non-zero count. Parameter counts are resolved, not just read: the card first, then the base model's card, then the number in the name, then the GGUF file size, which is a per-parameter figure. That sizes 207 of the 355 models coreml can load; the source is printed under every count that did not come from the card itself. Quantisation is read the same way: the card's quant_bits or method where stated, otherwise the GGUF builds the repository actually ships, which is where a range like 2–16-bit and a build count come from.
The licence column resolved in full is on what you're allowed to ship. Everything here is queryable through the API.