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 burn can load
- Either signal
- 113,832
- Declared
- 0 — nobody writes it on a card
- By format
- 113,832
- Commercial use ok
- 65,531
- Quantised build
- 26,706
| Model | Downloads | Params | Quant | Licence | Terms |
|---|---|---|---|---|---|
| sentence-transformers/all-MiniLM-L6-v2 sentence-similarity | 256,481,161 | 23M | — | Apache-2.0 | commercial ok |
| cross-encoder/ms-marco-MiniLM-L6-v2 text-ranking | 88,865,138 | 23M | — | Apache-2.0 | commercial ok |
| BAAI/bge-small-en-v1.5 feature-extraction | 64,638,739 | 33M | — | MIT | commercial ok |
| google-bert/bert-base-uncased fill-mask | 47,693,504 | 110M | — | Apache-2.0 | commercial ok |
| sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 sentence-similarity | 45,865,568 | 118M | — | Apache-2.0 | commercial ok |
| BAAI/bge-m3 sentence-similarity | 38,136,809 | — | — | MIT | commercial ok |
| google-t5/t5-small translation | 25,000,361 | 61M | — | Apache-2.0 | commercial ok |
| sentence-transformers/all-mpnet-base-v2 sentence-similarity | 23,432,230 | 109M | — | Apache-2.0 | commercial ok |
| amazon/chronos-2 time-series-forecasting | 22,779,369 | 119M | — | Apache-2.0 | commercial ok |
| FacebookAI/xlm-roberta-base fill-mask | 22,235,431 | 279M | — | MIT | commercial ok |
| Qwen/Qwen3-0.6B text-generation | 22,157,967 | 752M | — | Apache-2.0 | commercial ok |
| Comfy-Org/MiniMax-H3 | 20,053,814 | 33B base model | — | — | unknown |
| Qwen/Qwen3-VL-8B-Instruct image-text-to-text | 18,432,686 | 8.8B | — | Apache-2.0 | commercial ok |
| BAAI/bge-reranker-v2-m3 text-classification | 18,034,248 | 568M | — | Apache-2.0 | commercial ok |
| timm/mobilenetv3_small_100.lamb_in1k image-classification | 17,261,813 | 3M | — | Apache-2.0 | commercial ok |
| openai-community/gpt2 text-generation | 15,584,259 | 137M | — | MIT | commercial ok |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 text-generation | 15,437,304 | 2M | — | — | unknown |
| Comfy-Org/stable-diffusion-v1-5-archive | 15,242,545 | — | — | — | conditional |
| nomic-ai/nomic-embed-text-v1.5 sentence-similarity | 15,135,151 | 137M | — | Apache-2.0 | commercial ok |
| Qwen/Qwen3-8B text-generation | 13,103,652 | 8.2B | — | Apache-2.0 | commercial ok |
| timm/efficientnet_b3.ra2_in1k image-classification | 12,701,413 | 12M | — | Apache-2.0 | commercial ok |
| intfloat/multilingual-e5-small sentence-similarity | 12,377,202 | 118M | — | MIT | commercial ok |
| BAAI/bge-large-en-v1.5 feature-extraction | 12,050,258 | 335M | — | MIT | commercial ok |
| Qwen/Qwen3.6-35B-A3B-FP8 image-text-to-text | 10,634,071 | 36B | — fp8 | Apache-2.0 | commercial ok |
| BAAI/bge-base-en-v1.5 feature-extraction | 10,534,497 | 109M | — | MIT | commercial ok |
| Qwen/Qwen2.5-7B-Instruct text-generation | 9,880,006 | 7.6B | — | Apache-2.0 | commercial ok |
| google/gemma-4-26B-A4B-it image-text-to-text | 9,463,922 | 26B | — | Apache-2.0 | commercial ok |
| sentence-transformers/paraphrase-multilingual-mpnet-base-v2 sentence-similarity | 9,445,030 | 278M | — | Apache-2.0 | commercial ok |
| Qwen/Qwen3.5-9B image-text-to-text | 9,341,507 | 9.7B | — | Apache-2.0 | commercial ok |
| amazon/chronos-bolt-small time-series-forecasting | 9,145,041 | 48M | — | Apache-2.0 | commercial ok |
| google/gemma-4-31B-it image-text-to-text | 9,138,731 | 31B | — | Apache-2.0 | commercial ok |
| autogluon/chronos-2 time-series-forecasting | 8,970,857 | 119M | — | Apache-2.0 | commercial ok |
| nvidia/Qwen3.6-35B-A3B-NVFP4 text-generation | 8,739,085 | 19B | 8-bit modelopt | Apache-2.0 | commercial ok |
| Qwen/Qwen3-Embedding-0.6B feature-extraction | 8,526,863 | 596M | — | Apache-2.0 | commercial ok |
| Qwen/Qwen2.5-0.5B-Instruct text-generation | 8,518,031 | 494M | — | Apache-2.0 | commercial ok |
| Comfy-Org/Krea-2 | 8,468,421 | 13B base model | — | — | unknown |
| openai/clip-vit-large-patch14 zero-shot-image-classification | 8,362,708 | 428M | — | — | unknown |
| laion/clap-htsat-fused audio-classification | 8,077,041 | 154M | — | Apache-2.0 | commercial ok |
| Qwen/Qwen3.8-27B-FP8 image-text-to-text | 7,767,481 | 28B | — fp8 | Apache-2.0 | commercial ok |
| Comfy-Org/z_image_turbo | 7,757,125 | 6.2B base model | — | Apache-2.0 | commercial ok |
| FacebookAI/roberta-base fill-mask | 7,750,559 | 125M | — | MIT | commercial ok |
| Qwen/Qwen3.8-27B image-text-to-text | 7,667,556 | 28B | — | Apache-2.0 | commercial ok |
| farbodtavakkoli/OTel-2.0-LLM-31B-IT text-generation | 7,588,109 | 31B | — | Apache-2.0 | commercial ok |
| intfloat/multilingual-e5-base sentence-similarity | 7,460,971 | 278M | — | MIT | commercial ok |
| distilbert/distilbert-base-uncased fill-mask | 7,410,664 | 67M | — | Apache-2.0 | commercial ok |
| Qwen/Qwen2.5-1.5B-Instruct text-generation | 7,206,677 | 1.5B | — | Apache-2.0 | commercial ok |
| Qwen/Qwen3.5-4B image-text-to-text | 7,060,659 | 4.7B | — | Apache-2.0 | commercial ok |
| Qwen/Qwen2.5-VL-7B-Instruct image-text-to-text | 7,050,575 | 8.3B | — | Apache-2.0 | commercial ok |
| intfloat/multilingual-e5-large feature-extraction | 6,966,155 | 560M | — | MIT | commercial ok |
| Qwen/Qwen3-4B text-generation | 6,897,206 | 4.0B | — | Apache-2.0 | commercial ok |
| autogluon/chronos-2-small time-series-forecasting | 6,877,236 | 28M | — | Apache-2.0 | commercial ok |
| google/vit-base-patch16-224 image-classification | 6,834,322 | 87M | — | Apache-2.0 | commercial ok |
| openai/whisper-large-v3-turbo automatic-speech-recognition | 6,787,988 | 809M | — | MIT | commercial ok |
| meta-llama/Llama-3.2-1B-Instruct text-generation | 6,731,334 | 1.2B | — | — | conditional |
| openai/gpt-oss-20b text-generation | 6,697,247 | 21B | 8-bit mxfp4 | Apache-2.0 | commercial ok |
| ibm-granite/granite-embedding-small-english-r2 feature-extraction | 6,398,398 | 48M | — | Apache-2.0 | commercial ok |
| Qwen/Qwen3.6-27B-FP8 image-text-to-text | 6,338,910 | 28B | — fp8 | Apache-2.0 | commercial ok |
| FacebookAI/roberta-large fill-mask | 6,335,648 | 355M | — | MIT | commercial ok |
| autogluon/chronos-bolt-small time-series-forecasting | 6,303,567 | 48M | — | Apache-2.0 | commercial ok |
| meta-llama/Llama-3.1-8B-Instruct text-generation | 5,861,705 | 8.0B | — | — | conditional |
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 108,545 of the 113,832 models burn 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.